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Federated Quantum Long Short-term Memory (FedQLSTM) (2312.14309v1)
Published 21 Dec 2023 in cs.LG, cs.NI, and quant-ph
Abstract: Quantum federated learning (QFL) can facilitate collaborative learning across multiple clients using quantum machine learning (QML) models, while preserving data privacy. Although recent advances in QFL span different tasks like classification while leveraging several data types, no prior work has focused on developing a QFL framework that utilizes temporal data to approximate functions useful to analyze the performance of distributed quantum sensing networks. In this paper, a novel QFL framework that is the first to integrate quantum long short-term memory (QLSTM) models with temporal data is proposed. The proposed federated QLSTM (FedQLSTM) framework is exploited for performing the task of function approximation. In this regard, three key use cases are presented: Bessel function approximation, sinusoidal delayed quantum feedback control function approximation, and Struve function approximation. Simulation results confirm that, for all considered use cases, the proposed FedQLSTM framework achieves a faster convergence rate under one local training epoch, minimizing the overall computations, and saving 25-33% of the number of communication rounds needed until convergence compared to an FL framework with classical LSTM models.
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In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8622–8626 (2022). IEEE Bausch [2020] Bausch, J.: Recurrent quantum neural networks. Advances in neural information processing systems 33, 1368–1379 (2020) Stein et al. [2020] Stein, S.A., Baheri, B., Tischio, R.M., Mao, Y., Guan, Q., Li, A., Fang, B., Xu, S.: Qugan: A generative adversarial network through quantum states. arXiv preprint arXiv:2010.09036 (2020) Chen et al. [2022] Chen, S.Y.-C., Huang, C.-M., Hsing, C.-W., Goan, H.-S., Kao, Y.-J.: Variational quantum reinforcement learning via evolutionary optimization. Machine Learning: Science and Technology 3(1), 015025 (2022) Cong et al. [2019] Cong, I., Choi, S., Lukin, M.D.: Quantum convolutional neural networks. Nature Physics 15(12), 1273–1278 (2019) Chen et al. [2022a] Chen, S.Y.-C., Wei, T.-C., Zhang, C., Yu, H., Yoo, S.: Quantum convolutional neural networks for high energy physics data analysis. Physical Review Research 4(1), 013231 (2022) Chen et al. [2022b] Chen, S.Y.-C., Fry, D., Deshmukh, A., Rastunkov, V., Stefanski, C.: Reservoir computing via quantum recurrent neural networks. arXiv preprint arXiv:2211.02612 (2022) Di Sipio et al. [2022] Di Sipio, R., Huang, J.-H., Chen, S.Y.-C., Mangini, S., Worring, M.: The dawn of quantum natural language processing. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8612–8616 (2022). IEEE Giovannetti et al. [2004] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced measurements: beating the standard quantum limit. Science 306(5700), 1330–1336 (2004) Giovannetti et al. [2001] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced positioning and clock synchronization. Nature 412(6845), 417–419 (2001) Chehimi et al. [2023] Chehimi, M., Hashash, O., Saad, W.: The roadmap to a quantum-enabled wireless metaverse: Beyond the classical limits. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7–12 (2023). IEEE Chehimi and Saad [2021] Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. 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[2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Benedetti, M., Lloyd, E., Sack, S., Fiorentini, M.: Parameterized quantum circuits as machine learning models. Quantum Science and Technology 4(4), 043001 (2019) Mitarai et al. [2018] Mitarai, K., Negoro, M., Kitagawa, M., Fujii, K.: Quantum circuit learning. Physical Review A 98(3), 032309 (2018) Chen et al. [2020] Chen, S.Y.-C., Huang, C.-M., Hsing, C.-W., Kao, Y.-J.: Hybrid quantum-classical classifier based on tensor network and variational quantum circuit. arXiv preprint arXiv:2011.14651 (2020) Schuld et al. [2018] Schuld, M., Bocharov, A., Svore, K., Wiebe, N.: Circuit-centric quantum classifiers. arXiv preprint arXiv:1804.00633 (2018) Chen et al. [2022] Chen, S.Y.-C., Yoo, S., Fang, Y.-L.L.: Quantum long short-term memory. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8622–8626 (2022). IEEE Bausch [2020] Bausch, J.: Recurrent quantum neural networks. Advances in neural information processing systems 33, 1368–1379 (2020) Stein et al. [2020] Stein, S.A., Baheri, B., Tischio, R.M., Mao, Y., Guan, Q., Li, A., Fang, B., Xu, S.: Qugan: A generative adversarial network through quantum states. arXiv preprint arXiv:2010.09036 (2020) Chen et al. [2022] Chen, S.Y.-C., Huang, C.-M., Hsing, C.-W., Goan, H.-S., Kao, Y.-J.: Variational quantum reinforcement learning via evolutionary optimization. Machine Learning: Science and Technology 3(1), 015025 (2022) Cong et al. [2019] Cong, I., Choi, S., Lukin, M.D.: Quantum convolutional neural networks. Nature Physics 15(12), 1273–1278 (2019) Chen et al. [2022a] Chen, S.Y.-C., Wei, T.-C., Zhang, C., Yu, H., Yoo, S.: Quantum convolutional neural networks for high energy physics data analysis. Physical Review Research 4(1), 013231 (2022) Chen et al. [2022b] Chen, S.Y.-C., Fry, D., Deshmukh, A., Rastunkov, V., Stefanski, C.: Reservoir computing via quantum recurrent neural networks. arXiv preprint arXiv:2211.02612 (2022) Di Sipio et al. [2022] Di Sipio, R., Huang, J.-H., Chen, S.Y.-C., Mangini, S., Worring, M.: The dawn of quantum natural language processing. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8612–8616 (2022). IEEE Giovannetti et al. [2004] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced measurements: beating the standard quantum limit. Science 306(5700), 1330–1336 (2004) Giovannetti et al. [2001] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced positioning and clock synchronization. Nature 412(6845), 417–419 (2001) Chehimi et al. [2023] Chehimi, M., Hashash, O., Saad, W.: The roadmap to a quantum-enabled wireless metaverse: Beyond the classical limits. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7–12 (2023). IEEE Chehimi and Saad [2021] Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Mitarai, K., Negoro, M., Kitagawa, M., Fujii, K.: Quantum circuit learning. Physical Review A 98(3), 032309 (2018) Chen et al. [2020] Chen, S.Y.-C., Huang, C.-M., Hsing, C.-W., Kao, Y.-J.: Hybrid quantum-classical classifier based on tensor network and variational quantum circuit. arXiv preprint arXiv:2011.14651 (2020) Schuld et al. [2018] Schuld, M., Bocharov, A., Svore, K., Wiebe, N.: Circuit-centric quantum classifiers. arXiv preprint arXiv:1804.00633 (2018) Chen et al. [2022] Chen, S.Y.-C., Yoo, S., Fang, Y.-L.L.: Quantum long short-term memory. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8622–8626 (2022). IEEE Bausch [2020] Bausch, J.: Recurrent quantum neural networks. Advances in neural information processing systems 33, 1368–1379 (2020) Stein et al. [2020] Stein, S.A., Baheri, B., Tischio, R.M., Mao, Y., Guan, Q., Li, A., Fang, B., Xu, S.: Qugan: A generative adversarial network through quantum states. arXiv preprint arXiv:2010.09036 (2020) Chen et al. [2022] Chen, S.Y.-C., Huang, C.-M., Hsing, C.-W., Goan, H.-S., Kao, Y.-J.: Variational quantum reinforcement learning via evolutionary optimization. Machine Learning: Science and Technology 3(1), 015025 (2022) Cong et al. [2019] Cong, I., Choi, S., Lukin, M.D.: Quantum convolutional neural networks. Nature Physics 15(12), 1273–1278 (2019) Chen et al. [2022a] Chen, S.Y.-C., Wei, T.-C., Zhang, C., Yu, H., Yoo, S.: Quantum convolutional neural networks for high energy physics data analysis. Physical Review Research 4(1), 013231 (2022) Chen et al. [2022b] Chen, S.Y.-C., Fry, D., Deshmukh, A., Rastunkov, V., Stefanski, C.: Reservoir computing via quantum recurrent neural networks. arXiv preprint arXiv:2211.02612 (2022) Di Sipio et al. [2022] Di Sipio, R., Huang, J.-H., Chen, S.Y.-C., Mangini, S., Worring, M.: The dawn of quantum natural language processing. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8612–8616 (2022). IEEE Giovannetti et al. [2004] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced measurements: beating the standard quantum limit. Science 306(5700), 1330–1336 (2004) Giovannetti et al. [2001] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced positioning and clock synchronization. Nature 412(6845), 417–419 (2001) Chehimi et al. [2023] Chehimi, M., Hashash, O., Saad, W.: The roadmap to a quantum-enabled wireless metaverse: Beyond the classical limits. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7–12 (2023). IEEE Chehimi and Saad [2021] Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chen, S.Y.-C., Huang, C.-M., Hsing, C.-W., Kao, Y.-J.: Hybrid quantum-classical classifier based on tensor network and variational quantum circuit. arXiv preprint arXiv:2011.14651 (2020) Schuld et al. [2018] Schuld, M., Bocharov, A., Svore, K., Wiebe, N.: Circuit-centric quantum classifiers. arXiv preprint arXiv:1804.00633 (2018) Chen et al. [2022] Chen, S.Y.-C., Yoo, S., Fang, Y.-L.L.: Quantum long short-term memory. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8622–8626 (2022). IEEE Bausch [2020] Bausch, J.: Recurrent quantum neural networks. Advances in neural information processing systems 33, 1368–1379 (2020) Stein et al. [2020] Stein, S.A., Baheri, B., Tischio, R.M., Mao, Y., Guan, Q., Li, A., Fang, B., Xu, S.: Qugan: A generative adversarial network through quantum states. arXiv preprint arXiv:2010.09036 (2020) Chen et al. [2022] Chen, S.Y.-C., Huang, C.-M., Hsing, C.-W., Goan, H.-S., Kao, Y.-J.: Variational quantum reinforcement learning via evolutionary optimization. Machine Learning: Science and Technology 3(1), 015025 (2022) Cong et al. [2019] Cong, I., Choi, S., Lukin, M.D.: Quantum convolutional neural networks. Nature Physics 15(12), 1273–1278 (2019) Chen et al. [2022a] Chen, S.Y.-C., Wei, T.-C., Zhang, C., Yu, H., Yoo, S.: Quantum convolutional neural networks for high energy physics data analysis. Physical Review Research 4(1), 013231 (2022) Chen et al. [2022b] Chen, S.Y.-C., Fry, D., Deshmukh, A., Rastunkov, V., Stefanski, C.: Reservoir computing via quantum recurrent neural networks. arXiv preprint arXiv:2211.02612 (2022) Di Sipio et al. [2022] Di Sipio, R., Huang, J.-H., Chen, S.Y.-C., Mangini, S., Worring, M.: The dawn of quantum natural language processing. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8612–8616 (2022). IEEE Giovannetti et al. [2004] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced measurements: beating the standard quantum limit. Science 306(5700), 1330–1336 (2004) Giovannetti et al. [2001] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced positioning and clock synchronization. Nature 412(6845), 417–419 (2001) Chehimi et al. [2023] Chehimi, M., Hashash, O., Saad, W.: The roadmap to a quantum-enabled wireless metaverse: Beyond the classical limits. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7–12 (2023). IEEE Chehimi and Saad [2021] Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Schuld, M., Bocharov, A., Svore, K., Wiebe, N.: Circuit-centric quantum classifiers. arXiv preprint arXiv:1804.00633 (2018) Chen et al. [2022] Chen, S.Y.-C., Yoo, S., Fang, Y.-L.L.: Quantum long short-term memory. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8622–8626 (2022). IEEE Bausch [2020] Bausch, J.: Recurrent quantum neural networks. Advances in neural information processing systems 33, 1368–1379 (2020) Stein et al. [2020] Stein, S.A., Baheri, B., Tischio, R.M., Mao, Y., Guan, Q., Li, A., Fang, B., Xu, S.: Qugan: A generative adversarial network through quantum states. arXiv preprint arXiv:2010.09036 (2020) Chen et al. [2022] Chen, S.Y.-C., Huang, C.-M., Hsing, C.-W., Goan, H.-S., Kao, Y.-J.: Variational quantum reinforcement learning via evolutionary optimization. Machine Learning: Science and Technology 3(1), 015025 (2022) Cong et al. [2019] Cong, I., Choi, S., Lukin, M.D.: Quantum convolutional neural networks. Nature Physics 15(12), 1273–1278 (2019) Chen et al. [2022a] Chen, S.Y.-C., Wei, T.-C., Zhang, C., Yu, H., Yoo, S.: Quantum convolutional neural networks for high energy physics data analysis. Physical Review Research 4(1), 013231 (2022) Chen et al. [2022b] Chen, S.Y.-C., Fry, D., Deshmukh, A., Rastunkov, V., Stefanski, C.: Reservoir computing via quantum recurrent neural networks. arXiv preprint arXiv:2211.02612 (2022) Di Sipio et al. [2022] Di Sipio, R., Huang, J.-H., Chen, S.Y.-C., Mangini, S., Worring, M.: The dawn of quantum natural language processing. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8612–8616 (2022). IEEE Giovannetti et al. [2004] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced measurements: beating the standard quantum limit. Science 306(5700), 1330–1336 (2004) Giovannetti et al. [2001] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced positioning and clock synchronization. Nature 412(6845), 417–419 (2001) Chehimi et al. [2023] Chehimi, M., Hashash, O., Saad, W.: The roadmap to a quantum-enabled wireless metaverse: Beyond the classical limits. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7–12 (2023). IEEE Chehimi and Saad [2021] Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chen, S.Y.-C., Yoo, S., Fang, Y.-L.L.: Quantum long short-term memory. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8622–8626 (2022). IEEE Bausch [2020] Bausch, J.: Recurrent quantum neural networks. Advances in neural information processing systems 33, 1368–1379 (2020) Stein et al. [2020] Stein, S.A., Baheri, B., Tischio, R.M., Mao, Y., Guan, Q., Li, A., Fang, B., Xu, S.: Qugan: A generative adversarial network through quantum states. arXiv preprint arXiv:2010.09036 (2020) Chen et al. [2022] Chen, S.Y.-C., Huang, C.-M., Hsing, C.-W., Goan, H.-S., Kao, Y.-J.: Variational quantum reinforcement learning via evolutionary optimization. Machine Learning: Science and Technology 3(1), 015025 (2022) Cong et al. [2019] Cong, I., Choi, S., Lukin, M.D.: Quantum convolutional neural networks. Nature Physics 15(12), 1273–1278 (2019) Chen et al. [2022a] Chen, S.Y.-C., Wei, T.-C., Zhang, C., Yu, H., Yoo, S.: Quantum convolutional neural networks for high energy physics data analysis. Physical Review Research 4(1), 013231 (2022) Chen et al. [2022b] Chen, S.Y.-C., Fry, D., Deshmukh, A., Rastunkov, V., Stefanski, C.: Reservoir computing via quantum recurrent neural networks. arXiv preprint arXiv:2211.02612 (2022) Di Sipio et al. [2022] Di Sipio, R., Huang, J.-H., Chen, S.Y.-C., Mangini, S., Worring, M.: The dawn of quantum natural language processing. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8612–8616 (2022). IEEE Giovannetti et al. [2004] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced measurements: beating the standard quantum limit. Science 306(5700), 1330–1336 (2004) Giovannetti et al. [2001] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced positioning and clock synchronization. Nature 412(6845), 417–419 (2001) Chehimi et al. [2023] Chehimi, M., Hashash, O., Saad, W.: The roadmap to a quantum-enabled wireless metaverse: Beyond the classical limits. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7–12 (2023). IEEE Chehimi and Saad [2021] Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Bausch, J.: Recurrent quantum neural networks. Advances in neural information processing systems 33, 1368–1379 (2020) Stein et al. [2020] Stein, S.A., Baheri, B., Tischio, R.M., Mao, Y., Guan, Q., Li, A., Fang, B., Xu, S.: Qugan: A generative adversarial network through quantum states. arXiv preprint arXiv:2010.09036 (2020) Chen et al. [2022] Chen, S.Y.-C., Huang, C.-M., Hsing, C.-W., Goan, H.-S., Kao, Y.-J.: Variational quantum reinforcement learning via evolutionary optimization. Machine Learning: Science and Technology 3(1), 015025 (2022) Cong et al. [2019] Cong, I., Choi, S., Lukin, M.D.: Quantum convolutional neural networks. Nature Physics 15(12), 1273–1278 (2019) Chen et al. [2022a] Chen, S.Y.-C., Wei, T.-C., Zhang, C., Yu, H., Yoo, S.: Quantum convolutional neural networks for high energy physics data analysis. Physical Review Research 4(1), 013231 (2022) Chen et al. [2022b] Chen, S.Y.-C., Fry, D., Deshmukh, A., Rastunkov, V., Stefanski, C.: Reservoir computing via quantum recurrent neural networks. arXiv preprint arXiv:2211.02612 (2022) Di Sipio et al. [2022] Di Sipio, R., Huang, J.-H., Chen, S.Y.-C., Mangini, S., Worring, M.: The dawn of quantum natural language processing. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8612–8616 (2022). IEEE Giovannetti et al. [2004] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced measurements: beating the standard quantum limit. Science 306(5700), 1330–1336 (2004) Giovannetti et al. [2001] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced positioning and clock synchronization. Nature 412(6845), 417–419 (2001) Chehimi et al. [2023] Chehimi, M., Hashash, O., Saad, W.: The roadmap to a quantum-enabled wireless metaverse: Beyond the classical limits. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7–12 (2023). IEEE Chehimi and Saad [2021] Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Stein, S.A., Baheri, B., Tischio, R.M., Mao, Y., Guan, Q., Li, A., Fang, B., Xu, S.: Qugan: A generative adversarial network through quantum states. arXiv preprint arXiv:2010.09036 (2020) Chen et al. [2022] Chen, S.Y.-C., Huang, C.-M., Hsing, C.-W., Goan, H.-S., Kao, Y.-J.: Variational quantum reinforcement learning via evolutionary optimization. Machine Learning: Science and Technology 3(1), 015025 (2022) Cong et al. [2019] Cong, I., Choi, S., Lukin, M.D.: Quantum convolutional neural networks. Nature Physics 15(12), 1273–1278 (2019) Chen et al. [2022a] Chen, S.Y.-C., Wei, T.-C., Zhang, C., Yu, H., Yoo, S.: Quantum convolutional neural networks for high energy physics data analysis. Physical Review Research 4(1), 013231 (2022) Chen et al. [2022b] Chen, S.Y.-C., Fry, D., Deshmukh, A., Rastunkov, V., Stefanski, C.: Reservoir computing via quantum recurrent neural networks. arXiv preprint arXiv:2211.02612 (2022) Di Sipio et al. [2022] Di Sipio, R., Huang, J.-H., Chen, S.Y.-C., Mangini, S., Worring, M.: The dawn of quantum natural language processing. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8612–8616 (2022). IEEE Giovannetti et al. [2004] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced measurements: beating the standard quantum limit. Science 306(5700), 1330–1336 (2004) Giovannetti et al. [2001] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced positioning and clock synchronization. Nature 412(6845), 417–419 (2001) Chehimi et al. [2023] Chehimi, M., Hashash, O., Saad, W.: The roadmap to a quantum-enabled wireless metaverse: Beyond the classical limits. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7–12 (2023). IEEE Chehimi and Saad [2021] Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chen, S.Y.-C., Huang, C.-M., Hsing, C.-W., Goan, H.-S., Kao, Y.-J.: Variational quantum reinforcement learning via evolutionary optimization. Machine Learning: Science and Technology 3(1), 015025 (2022) Cong et al. [2019] Cong, I., Choi, S., Lukin, M.D.: Quantum convolutional neural networks. Nature Physics 15(12), 1273–1278 (2019) Chen et al. [2022a] Chen, S.Y.-C., Wei, T.-C., Zhang, C., Yu, H., Yoo, S.: Quantum convolutional neural networks for high energy physics data analysis. Physical Review Research 4(1), 013231 (2022) Chen et al. [2022b] Chen, S.Y.-C., Fry, D., Deshmukh, A., Rastunkov, V., Stefanski, C.: Reservoir computing via quantum recurrent neural networks. arXiv preprint arXiv:2211.02612 (2022) Di Sipio et al. [2022] Di Sipio, R., Huang, J.-H., Chen, S.Y.-C., Mangini, S., Worring, M.: The dawn of quantum natural language processing. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8612–8616 (2022). IEEE Giovannetti et al. [2004] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced measurements: beating the standard quantum limit. Science 306(5700), 1330–1336 (2004) Giovannetti et al. [2001] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced positioning and clock synchronization. Nature 412(6845), 417–419 (2001) Chehimi et al. [2023] Chehimi, M., Hashash, O., Saad, W.: The roadmap to a quantum-enabled wireless metaverse: Beyond the classical limits. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7–12 (2023). IEEE Chehimi and Saad [2021] Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Cong, I., Choi, S., Lukin, M.D.: Quantum convolutional neural networks. Nature Physics 15(12), 1273–1278 (2019) Chen et al. [2022a] Chen, S.Y.-C., Wei, T.-C., Zhang, C., Yu, H., Yoo, S.: Quantum convolutional neural networks for high energy physics data analysis. Physical Review Research 4(1), 013231 (2022) Chen et al. [2022b] Chen, S.Y.-C., Fry, D., Deshmukh, A., Rastunkov, V., Stefanski, C.: Reservoir computing via quantum recurrent neural networks. arXiv preprint arXiv:2211.02612 (2022) Di Sipio et al. [2022] Di Sipio, R., Huang, J.-H., Chen, S.Y.-C., Mangini, S., Worring, M.: The dawn of quantum natural language processing. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8612–8616 (2022). IEEE Giovannetti et al. [2004] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced measurements: beating the standard quantum limit. Science 306(5700), 1330–1336 (2004) Giovannetti et al. [2001] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced positioning and clock synchronization. Nature 412(6845), 417–419 (2001) Chehimi et al. [2023] Chehimi, M., Hashash, O., Saad, W.: The roadmap to a quantum-enabled wireless metaverse: Beyond the classical limits. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7–12 (2023). IEEE Chehimi and Saad [2021] Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chen, S.Y.-C., Wei, T.-C., Zhang, C., Yu, H., Yoo, S.: Quantum convolutional neural networks for high energy physics data analysis. Physical Review Research 4(1), 013231 (2022) Chen et al. [2022b] Chen, S.Y.-C., Fry, D., Deshmukh, A., Rastunkov, V., Stefanski, C.: Reservoir computing via quantum recurrent neural networks. arXiv preprint arXiv:2211.02612 (2022) Di Sipio et al. [2022] Di Sipio, R., Huang, J.-H., Chen, S.Y.-C., Mangini, S., Worring, M.: The dawn of quantum natural language processing. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8612–8616 (2022). IEEE Giovannetti et al. [2004] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced measurements: beating the standard quantum limit. Science 306(5700), 1330–1336 (2004) Giovannetti et al. [2001] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced positioning and clock synchronization. Nature 412(6845), 417–419 (2001) Chehimi et al. [2023] Chehimi, M., Hashash, O., Saad, W.: The roadmap to a quantum-enabled wireless metaverse: Beyond the classical limits. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7–12 (2023). IEEE Chehimi and Saad [2021] Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chen, S.Y.-C., Fry, D., Deshmukh, A., Rastunkov, V., Stefanski, C.: Reservoir computing via quantum recurrent neural networks. arXiv preprint arXiv:2211.02612 (2022) Di Sipio et al. [2022] Di Sipio, R., Huang, J.-H., Chen, S.Y.-C., Mangini, S., Worring, M.: The dawn of quantum natural language processing. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8612–8616 (2022). IEEE Giovannetti et al. [2004] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced measurements: beating the standard quantum limit. Science 306(5700), 1330–1336 (2004) Giovannetti et al. [2001] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced positioning and clock synchronization. Nature 412(6845), 417–419 (2001) Chehimi et al. [2023] Chehimi, M., Hashash, O., Saad, W.: The roadmap to a quantum-enabled wireless metaverse: Beyond the classical limits. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7–12 (2023). IEEE Chehimi and Saad [2021] Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Di Sipio, R., Huang, J.-H., Chen, S.Y.-C., Mangini, S., Worring, M.: The dawn of quantum natural language processing. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8612–8616 (2022). IEEE Giovannetti et al. [2004] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced measurements: beating the standard quantum limit. Science 306(5700), 1330–1336 (2004) Giovannetti et al. [2001] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced positioning and clock synchronization. Nature 412(6845), 417–419 (2001) Chehimi et al. [2023] Chehimi, M., Hashash, O., Saad, W.: The roadmap to a quantum-enabled wireless metaverse: Beyond the classical limits. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7–12 (2023). IEEE Chehimi and Saad [2021] Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced measurements: beating the standard quantum limit. Science 306(5700), 1330–1336 (2004) Giovannetti et al. [2001] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced positioning and clock synchronization. Nature 412(6845), 417–419 (2001) Chehimi et al. [2023] Chehimi, M., Hashash, O., Saad, W.: The roadmap to a quantum-enabled wireless metaverse: Beyond the classical limits. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7–12 (2023). IEEE Chehimi and Saad [2021] Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. 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[2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. 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In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chehimi, M., Hashash, O., Saad, W.: The roadmap to a quantum-enabled wireless metaverse: Beyond the classical limits. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7–12 (2023). IEEE Chehimi and Saad [2021] Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? 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[2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. 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[2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. 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[2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. 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[2022a] Chen, S.Y.-C., Wei, T.-C., Zhang, C., Yu, H., Yoo, S.: Quantum convolutional neural networks for high energy physics data analysis. Physical Review Research 4(1), 013231 (2022) Chen et al. [2022b] Chen, S.Y.-C., Fry, D., Deshmukh, A., Rastunkov, V., Stefanski, C.: Reservoir computing via quantum recurrent neural networks. arXiv preprint arXiv:2211.02612 (2022) Di Sipio et al. [2022] Di Sipio, R., Huang, J.-H., Chen, S.Y.-C., Mangini, S., Worring, M.: The dawn of quantum natural language processing. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8612–8616 (2022). IEEE Giovannetti et al. [2004] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced measurements: beating the standard quantum limit. Science 306(5700), 1330–1336 (2004) Giovannetti et al. [2001] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced positioning and clock synchronization. Nature 412(6845), 417–419 (2001) Chehimi et al. [2023] Chehimi, M., Hashash, O., Saad, W.: The roadmap to a quantum-enabled wireless metaverse: Beyond the classical limits. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7–12 (2023). IEEE Chehimi and Saad [2021] Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chen, S.Y.-C., Huang, C.-M., Hsing, C.-W., Kao, Y.-J.: Hybrid quantum-classical classifier based on tensor network and variational quantum circuit. arXiv preprint arXiv:2011.14651 (2020) Schuld et al. [2018] Schuld, M., Bocharov, A., Svore, K., Wiebe, N.: Circuit-centric quantum classifiers. arXiv preprint arXiv:1804.00633 (2018) Chen et al. [2022] Chen, S.Y.-C., Yoo, S., Fang, Y.-L.L.: Quantum long short-term memory. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8622–8626 (2022). IEEE Bausch [2020] Bausch, J.: Recurrent quantum neural networks. Advances in neural information processing systems 33, 1368–1379 (2020) Stein et al. [2020] Stein, S.A., Baheri, B., Tischio, R.M., Mao, Y., Guan, Q., Li, A., Fang, B., Xu, S.: Qugan: A generative adversarial network through quantum states. arXiv preprint arXiv:2010.09036 (2020) Chen et al. [2022] Chen, S.Y.-C., Huang, C.-M., Hsing, C.-W., Goan, H.-S., Kao, Y.-J.: Variational quantum reinforcement learning via evolutionary optimization. Machine Learning: Science and Technology 3(1), 015025 (2022) Cong et al. [2019] Cong, I., Choi, S., Lukin, M.D.: Quantum convolutional neural networks. Nature Physics 15(12), 1273–1278 (2019) Chen et al. [2022a] Chen, S.Y.-C., Wei, T.-C., Zhang, C., Yu, H., Yoo, S.: Quantum convolutional neural networks for high energy physics data analysis. Physical Review Research 4(1), 013231 (2022) Chen et al. [2022b] Chen, S.Y.-C., Fry, D., Deshmukh, A., Rastunkov, V., Stefanski, C.: Reservoir computing via quantum recurrent neural networks. arXiv preprint arXiv:2211.02612 (2022) Di Sipio et al. [2022] Di Sipio, R., Huang, J.-H., Chen, S.Y.-C., Mangini, S., Worring, M.: The dawn of quantum natural language processing. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8612–8616 (2022). IEEE Giovannetti et al. [2004] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced measurements: beating the standard quantum limit. Science 306(5700), 1330–1336 (2004) Giovannetti et al. [2001] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced positioning and clock synchronization. Nature 412(6845), 417–419 (2001) Chehimi et al. [2023] Chehimi, M., Hashash, O., Saad, W.: The roadmap to a quantum-enabled wireless metaverse: Beyond the classical limits. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7–12 (2023). IEEE Chehimi and Saad [2021] Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Schuld, M., Bocharov, A., Svore, K., Wiebe, N.: Circuit-centric quantum classifiers. arXiv preprint arXiv:1804.00633 (2018) Chen et al. [2022] Chen, S.Y.-C., Yoo, S., Fang, Y.-L.L.: Quantum long short-term memory. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8622–8626 (2022). IEEE Bausch [2020] Bausch, J.: Recurrent quantum neural networks. Advances in neural information processing systems 33, 1368–1379 (2020) Stein et al. [2020] Stein, S.A., Baheri, B., Tischio, R.M., Mao, Y., Guan, Q., Li, A., Fang, B., Xu, S.: Qugan: A generative adversarial network through quantum states. arXiv preprint arXiv:2010.09036 (2020) Chen et al. [2022] Chen, S.Y.-C., Huang, C.-M., Hsing, C.-W., Goan, H.-S., Kao, Y.-J.: Variational quantum reinforcement learning via evolutionary optimization. Machine Learning: Science and Technology 3(1), 015025 (2022) Cong et al. [2019] Cong, I., Choi, S., Lukin, M.D.: Quantum convolutional neural networks. Nature Physics 15(12), 1273–1278 (2019) Chen et al. [2022a] Chen, S.Y.-C., Wei, T.-C., Zhang, C., Yu, H., Yoo, S.: Quantum convolutional neural networks for high energy physics data analysis. Physical Review Research 4(1), 013231 (2022) Chen et al. [2022b] Chen, S.Y.-C., Fry, D., Deshmukh, A., Rastunkov, V., Stefanski, C.: Reservoir computing via quantum recurrent neural networks. arXiv preprint arXiv:2211.02612 (2022) Di Sipio et al. [2022] Di Sipio, R., Huang, J.-H., Chen, S.Y.-C., Mangini, S., Worring, M.: The dawn of quantum natural language processing. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8612–8616 (2022). IEEE Giovannetti et al. [2004] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced measurements: beating the standard quantum limit. Science 306(5700), 1330–1336 (2004) Giovannetti et al. [2001] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced positioning and clock synchronization. Nature 412(6845), 417–419 (2001) Chehimi et al. [2023] Chehimi, M., Hashash, O., Saad, W.: The roadmap to a quantum-enabled wireless metaverse: Beyond the classical limits. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7–12 (2023). IEEE Chehimi and Saad [2021] Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chen, S.Y.-C., Yoo, S., Fang, Y.-L.L.: Quantum long short-term memory. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8622–8626 (2022). IEEE Bausch [2020] Bausch, J.: Recurrent quantum neural networks. Advances in neural information processing systems 33, 1368–1379 (2020) Stein et al. [2020] Stein, S.A., Baheri, B., Tischio, R.M., Mao, Y., Guan, Q., Li, A., Fang, B., Xu, S.: Qugan: A generative adversarial network through quantum states. arXiv preprint arXiv:2010.09036 (2020) Chen et al. [2022] Chen, S.Y.-C., Huang, C.-M., Hsing, C.-W., Goan, H.-S., Kao, Y.-J.: Variational quantum reinforcement learning via evolutionary optimization. Machine Learning: Science and Technology 3(1), 015025 (2022) Cong et al. [2019] Cong, I., Choi, S., Lukin, M.D.: Quantum convolutional neural networks. Nature Physics 15(12), 1273–1278 (2019) Chen et al. [2022a] Chen, S.Y.-C., Wei, T.-C., Zhang, C., Yu, H., Yoo, S.: Quantum convolutional neural networks for high energy physics data analysis. Physical Review Research 4(1), 013231 (2022) Chen et al. [2022b] Chen, S.Y.-C., Fry, D., Deshmukh, A., Rastunkov, V., Stefanski, C.: Reservoir computing via quantum recurrent neural networks. arXiv preprint arXiv:2211.02612 (2022) Di Sipio et al. [2022] Di Sipio, R., Huang, J.-H., Chen, S.Y.-C., Mangini, S., Worring, M.: The dawn of quantum natural language processing. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8612–8616 (2022). IEEE Giovannetti et al. [2004] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced measurements: beating the standard quantum limit. Science 306(5700), 1330–1336 (2004) Giovannetti et al. [2001] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced positioning and clock synchronization. Nature 412(6845), 417–419 (2001) Chehimi et al. [2023] Chehimi, M., Hashash, O., Saad, W.: The roadmap to a quantum-enabled wireless metaverse: Beyond the classical limits. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7–12 (2023). IEEE Chehimi and Saad [2021] Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Bausch, J.: Recurrent quantum neural networks. Advances in neural information processing systems 33, 1368–1379 (2020) Stein et al. [2020] Stein, S.A., Baheri, B., Tischio, R.M., Mao, Y., Guan, Q., Li, A., Fang, B., Xu, S.: Qugan: A generative adversarial network through quantum states. arXiv preprint arXiv:2010.09036 (2020) Chen et al. [2022] Chen, S.Y.-C., Huang, C.-M., Hsing, C.-W., Goan, H.-S., Kao, Y.-J.: Variational quantum reinforcement learning via evolutionary optimization. Machine Learning: Science and Technology 3(1), 015025 (2022) Cong et al. [2019] Cong, I., Choi, S., Lukin, M.D.: Quantum convolutional neural networks. Nature Physics 15(12), 1273–1278 (2019) Chen et al. [2022a] Chen, S.Y.-C., Wei, T.-C., Zhang, C., Yu, H., Yoo, S.: Quantum convolutional neural networks for high energy physics data analysis. Physical Review Research 4(1), 013231 (2022) Chen et al. [2022b] Chen, S.Y.-C., Fry, D., Deshmukh, A., Rastunkov, V., Stefanski, C.: Reservoir computing via quantum recurrent neural networks. arXiv preprint arXiv:2211.02612 (2022) Di Sipio et al. [2022] Di Sipio, R., Huang, J.-H., Chen, S.Y.-C., Mangini, S., Worring, M.: The dawn of quantum natural language processing. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8612–8616 (2022). IEEE Giovannetti et al. [2004] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced measurements: beating the standard quantum limit. Science 306(5700), 1330–1336 (2004) Giovannetti et al. [2001] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced positioning and clock synchronization. Nature 412(6845), 417–419 (2001) Chehimi et al. [2023] Chehimi, M., Hashash, O., Saad, W.: The roadmap to a quantum-enabled wireless metaverse: Beyond the classical limits. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7–12 (2023). IEEE Chehimi and Saad [2021] Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Stein, S.A., Baheri, B., Tischio, R.M., Mao, Y., Guan, Q., Li, A., Fang, B., Xu, S.: Qugan: A generative adversarial network through quantum states. arXiv preprint arXiv:2010.09036 (2020) Chen et al. [2022] Chen, S.Y.-C., Huang, C.-M., Hsing, C.-W., Goan, H.-S., Kao, Y.-J.: Variational quantum reinforcement learning via evolutionary optimization. Machine Learning: Science and Technology 3(1), 015025 (2022) Cong et al. [2019] Cong, I., Choi, S., Lukin, M.D.: Quantum convolutional neural networks. Nature Physics 15(12), 1273–1278 (2019) Chen et al. [2022a] Chen, S.Y.-C., Wei, T.-C., Zhang, C., Yu, H., Yoo, S.: Quantum convolutional neural networks for high energy physics data analysis. Physical Review Research 4(1), 013231 (2022) Chen et al. [2022b] Chen, S.Y.-C., Fry, D., Deshmukh, A., Rastunkov, V., Stefanski, C.: Reservoir computing via quantum recurrent neural networks. arXiv preprint arXiv:2211.02612 (2022) Di Sipio et al. [2022] Di Sipio, R., Huang, J.-H., Chen, S.Y.-C., Mangini, S., Worring, M.: The dawn of quantum natural language processing. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8612–8616 (2022). IEEE Giovannetti et al. [2004] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced measurements: beating the standard quantum limit. Science 306(5700), 1330–1336 (2004) Giovannetti et al. [2001] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced positioning and clock synchronization. Nature 412(6845), 417–419 (2001) Chehimi et al. [2023] Chehimi, M., Hashash, O., Saad, W.: The roadmap to a quantum-enabled wireless metaverse: Beyond the classical limits. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7–12 (2023). IEEE Chehimi and Saad [2021] Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chen, S.Y.-C., Huang, C.-M., Hsing, C.-W., Goan, H.-S., Kao, Y.-J.: Variational quantum reinforcement learning via evolutionary optimization. Machine Learning: Science and Technology 3(1), 015025 (2022) Cong et al. [2019] Cong, I., Choi, S., Lukin, M.D.: Quantum convolutional neural networks. Nature Physics 15(12), 1273–1278 (2019) Chen et al. [2022a] Chen, S.Y.-C., Wei, T.-C., Zhang, C., Yu, H., Yoo, S.: Quantum convolutional neural networks for high energy physics data analysis. Physical Review Research 4(1), 013231 (2022) Chen et al. [2022b] Chen, S.Y.-C., Fry, D., Deshmukh, A., Rastunkov, V., Stefanski, C.: Reservoir computing via quantum recurrent neural networks. arXiv preprint arXiv:2211.02612 (2022) Di Sipio et al. [2022] Di Sipio, R., Huang, J.-H., Chen, S.Y.-C., Mangini, S., Worring, M.: The dawn of quantum natural language processing. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8612–8616 (2022). IEEE Giovannetti et al. [2004] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced measurements: beating the standard quantum limit. Science 306(5700), 1330–1336 (2004) Giovannetti et al. [2001] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced positioning and clock synchronization. Nature 412(6845), 417–419 (2001) Chehimi et al. [2023] Chehimi, M., Hashash, O., Saad, W.: The roadmap to a quantum-enabled wireless metaverse: Beyond the classical limits. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7–12 (2023). IEEE Chehimi and Saad [2021] Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Cong, I., Choi, S., Lukin, M.D.: Quantum convolutional neural networks. Nature Physics 15(12), 1273–1278 (2019) Chen et al. [2022a] Chen, S.Y.-C., Wei, T.-C., Zhang, C., Yu, H., Yoo, S.: Quantum convolutional neural networks for high energy physics data analysis. Physical Review Research 4(1), 013231 (2022) Chen et al. [2022b] Chen, S.Y.-C., Fry, D., Deshmukh, A., Rastunkov, V., Stefanski, C.: Reservoir computing via quantum recurrent neural networks. arXiv preprint arXiv:2211.02612 (2022) Di Sipio et al. [2022] Di Sipio, R., Huang, J.-H., Chen, S.Y.-C., Mangini, S., Worring, M.: The dawn of quantum natural language processing. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8612–8616 (2022). IEEE Giovannetti et al. [2004] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced measurements: beating the standard quantum limit. Science 306(5700), 1330–1336 (2004) Giovannetti et al. [2001] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced positioning and clock synchronization. Nature 412(6845), 417–419 (2001) Chehimi et al. [2023] Chehimi, M., Hashash, O., Saad, W.: The roadmap to a quantum-enabled wireless metaverse: Beyond the classical limits. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7–12 (2023). IEEE Chehimi and Saad [2021] Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chen, S.Y.-C., Wei, T.-C., Zhang, C., Yu, H., Yoo, S.: Quantum convolutional neural networks for high energy physics data analysis. Physical Review Research 4(1), 013231 (2022) Chen et al. [2022b] Chen, S.Y.-C., Fry, D., Deshmukh, A., Rastunkov, V., Stefanski, C.: Reservoir computing via quantum recurrent neural networks. arXiv preprint arXiv:2211.02612 (2022) Di Sipio et al. [2022] Di Sipio, R., Huang, J.-H., Chen, S.Y.-C., Mangini, S., Worring, M.: The dawn of quantum natural language processing. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8612–8616 (2022). IEEE Giovannetti et al. [2004] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced measurements: beating the standard quantum limit. Science 306(5700), 1330–1336 (2004) Giovannetti et al. [2001] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced positioning and clock synchronization. Nature 412(6845), 417–419 (2001) Chehimi et al. [2023] Chehimi, M., Hashash, O., Saad, W.: The roadmap to a quantum-enabled wireless metaverse: Beyond the classical limits. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7–12 (2023). IEEE Chehimi and Saad [2021] Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chen, S.Y.-C., Fry, D., Deshmukh, A., Rastunkov, V., Stefanski, C.: Reservoir computing via quantum recurrent neural networks. arXiv preprint arXiv:2211.02612 (2022) Di Sipio et al. [2022] Di Sipio, R., Huang, J.-H., Chen, S.Y.-C., Mangini, S., Worring, M.: The dawn of quantum natural language processing. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8612–8616 (2022). IEEE Giovannetti et al. [2004] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced measurements: beating the standard quantum limit. Science 306(5700), 1330–1336 (2004) Giovannetti et al. [2001] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced positioning and clock synchronization. Nature 412(6845), 417–419 (2001) Chehimi et al. [2023] Chehimi, M., Hashash, O., Saad, W.: The roadmap to a quantum-enabled wireless metaverse: Beyond the classical limits. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7–12 (2023). IEEE Chehimi and Saad [2021] Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Di Sipio, R., Huang, J.-H., Chen, S.Y.-C., Mangini, S., Worring, M.: The dawn of quantum natural language processing. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8612–8616 (2022). IEEE Giovannetti et al. [2004] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced measurements: beating the standard quantum limit. Science 306(5700), 1330–1336 (2004) Giovannetti et al. [2001] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced positioning and clock synchronization. Nature 412(6845), 417–419 (2001) Chehimi et al. [2023] Chehimi, M., Hashash, O., Saad, W.: The roadmap to a quantum-enabled wireless metaverse: Beyond the classical limits. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7–12 (2023). IEEE Chehimi and Saad [2021] Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced measurements: beating the standard quantum limit. Science 306(5700), 1330–1336 (2004) Giovannetti et al. [2001] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced positioning and clock synchronization. Nature 412(6845), 417–419 (2001) Chehimi et al. [2023] Chehimi, M., Hashash, O., Saad, W.: The roadmap to a quantum-enabled wireless metaverse: Beyond the classical limits. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7–12 (2023). IEEE Chehimi and Saad [2021] Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced positioning and clock synchronization. Nature 412(6845), 417–419 (2001) Chehimi et al. [2023] Chehimi, M., Hashash, O., Saad, W.: The roadmap to a quantum-enabled wireless metaverse: Beyond the classical limits. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7–12 (2023). IEEE Chehimi and Saad [2021] Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chehimi, M., Hashash, O., Saad, W.: The roadmap to a quantum-enabled wireless metaverse: Beyond the classical limits. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7–12 (2023). IEEE Chehimi and Saad [2021] Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. 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New Journal of Physics 15(1), 013041 (2013) Chen, S.Y.-C., Huang, C.-M., Hsing, C.-W., Kao, Y.-J.: Hybrid quantum-classical classifier based on tensor network and variational quantum circuit. arXiv preprint arXiv:2011.14651 (2020) Schuld et al. [2018] Schuld, M., Bocharov, A., Svore, K., Wiebe, N.: Circuit-centric quantum classifiers. arXiv preprint arXiv:1804.00633 (2018) Chen et al. [2022] Chen, S.Y.-C., Yoo, S., Fang, Y.-L.L.: Quantum long short-term memory. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8622–8626 (2022). IEEE Bausch [2020] Bausch, J.: Recurrent quantum neural networks. Advances in neural information processing systems 33, 1368–1379 (2020) Stein et al. [2020] Stein, S.A., Baheri, B., Tischio, R.M., Mao, Y., Guan, Q., Li, A., Fang, B., Xu, S.: Qugan: A generative adversarial network through quantum states. arXiv preprint arXiv:2010.09036 (2020) Chen et al. [2022] Chen, S.Y.-C., Huang, C.-M., Hsing, C.-W., Goan, H.-S., Kao, Y.-J.: Variational quantum reinforcement learning via evolutionary optimization. Machine Learning: Science and Technology 3(1), 015025 (2022) Cong et al. [2019] Cong, I., Choi, S., Lukin, M.D.: Quantum convolutional neural networks. Nature Physics 15(12), 1273–1278 (2019) Chen et al. [2022a] Chen, S.Y.-C., Wei, T.-C., Zhang, C., Yu, H., Yoo, S.: Quantum convolutional neural networks for high energy physics data analysis. Physical Review Research 4(1), 013231 (2022) Chen et al. [2022b] Chen, S.Y.-C., Fry, D., Deshmukh, A., Rastunkov, V., Stefanski, C.: Reservoir computing via quantum recurrent neural networks. arXiv preprint arXiv:2211.02612 (2022) Di Sipio et al. [2022] Di Sipio, R., Huang, J.-H., Chen, S.Y.-C., Mangini, S., Worring, M.: The dawn of quantum natural language processing. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8612–8616 (2022). IEEE Giovannetti et al. [2004] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced measurements: beating the standard quantum limit. Science 306(5700), 1330–1336 (2004) Giovannetti et al. [2001] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced positioning and clock synchronization. Nature 412(6845), 417–419 (2001) Chehimi et al. [2023] Chehimi, M., Hashash, O., Saad, W.: The roadmap to a quantum-enabled wireless metaverse: Beyond the classical limits. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7–12 (2023). IEEE Chehimi and Saad [2021] Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Schuld, M., Bocharov, A., Svore, K., Wiebe, N.: Circuit-centric quantum classifiers. arXiv preprint arXiv:1804.00633 (2018) Chen et al. [2022] Chen, S.Y.-C., Yoo, S., Fang, Y.-L.L.: Quantum long short-term memory. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8622–8626 (2022). IEEE Bausch [2020] Bausch, J.: Recurrent quantum neural networks. Advances in neural information processing systems 33, 1368–1379 (2020) Stein et al. [2020] Stein, S.A., Baheri, B., Tischio, R.M., Mao, Y., Guan, Q., Li, A., Fang, B., Xu, S.: Qugan: A generative adversarial network through quantum states. arXiv preprint arXiv:2010.09036 (2020) Chen et al. [2022] Chen, S.Y.-C., Huang, C.-M., Hsing, C.-W., Goan, H.-S., Kao, Y.-J.: Variational quantum reinforcement learning via evolutionary optimization. Machine Learning: Science and Technology 3(1), 015025 (2022) Cong et al. [2019] Cong, I., Choi, S., Lukin, M.D.: Quantum convolutional neural networks. Nature Physics 15(12), 1273–1278 (2019) Chen et al. [2022a] Chen, S.Y.-C., Wei, T.-C., Zhang, C., Yu, H., Yoo, S.: Quantum convolutional neural networks for high energy physics data analysis. Physical Review Research 4(1), 013231 (2022) Chen et al. [2022b] Chen, S.Y.-C., Fry, D., Deshmukh, A., Rastunkov, V., Stefanski, C.: Reservoir computing via quantum recurrent neural networks. arXiv preprint arXiv:2211.02612 (2022) Di Sipio et al. [2022] Di Sipio, R., Huang, J.-H., Chen, S.Y.-C., Mangini, S., Worring, M.: The dawn of quantum natural language processing. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8612–8616 (2022). IEEE Giovannetti et al. [2004] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced measurements: beating the standard quantum limit. Science 306(5700), 1330–1336 (2004) Giovannetti et al. [2001] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced positioning and clock synchronization. Nature 412(6845), 417–419 (2001) Chehimi et al. [2023] Chehimi, M., Hashash, O., Saad, W.: The roadmap to a quantum-enabled wireless metaverse: Beyond the classical limits. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7–12 (2023). IEEE Chehimi and Saad [2021] Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chen, S.Y.-C., Yoo, S., Fang, Y.-L.L.: Quantum long short-term memory. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8622–8626 (2022). IEEE Bausch [2020] Bausch, J.: Recurrent quantum neural networks. Advances in neural information processing systems 33, 1368–1379 (2020) Stein et al. [2020] Stein, S.A., Baheri, B., Tischio, R.M., Mao, Y., Guan, Q., Li, A., Fang, B., Xu, S.: Qugan: A generative adversarial network through quantum states. arXiv preprint arXiv:2010.09036 (2020) Chen et al. [2022] Chen, S.Y.-C., Huang, C.-M., Hsing, C.-W., Goan, H.-S., Kao, Y.-J.: Variational quantum reinforcement learning via evolutionary optimization. Machine Learning: Science and Technology 3(1), 015025 (2022) Cong et al. [2019] Cong, I., Choi, S., Lukin, M.D.: Quantum convolutional neural networks. Nature Physics 15(12), 1273–1278 (2019) Chen et al. [2022a] Chen, S.Y.-C., Wei, T.-C., Zhang, C., Yu, H., Yoo, S.: Quantum convolutional neural networks for high energy physics data analysis. Physical Review Research 4(1), 013231 (2022) Chen et al. [2022b] Chen, S.Y.-C., Fry, D., Deshmukh, A., Rastunkov, V., Stefanski, C.: Reservoir computing via quantum recurrent neural networks. arXiv preprint arXiv:2211.02612 (2022) Di Sipio et al. [2022] Di Sipio, R., Huang, J.-H., Chen, S.Y.-C., Mangini, S., Worring, M.: The dawn of quantum natural language processing. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8612–8616 (2022). IEEE Giovannetti et al. [2004] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced measurements: beating the standard quantum limit. Science 306(5700), 1330–1336 (2004) Giovannetti et al. [2001] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced positioning and clock synchronization. Nature 412(6845), 417–419 (2001) Chehimi et al. [2023] Chehimi, M., Hashash, O., Saad, W.: The roadmap to a quantum-enabled wireless metaverse: Beyond the classical limits. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7–12 (2023). IEEE Chehimi and Saad [2021] Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Bausch, J.: Recurrent quantum neural networks. Advances in neural information processing systems 33, 1368–1379 (2020) Stein et al. [2020] Stein, S.A., Baheri, B., Tischio, R.M., Mao, Y., Guan, Q., Li, A., Fang, B., Xu, S.: Qugan: A generative adversarial network through quantum states. arXiv preprint arXiv:2010.09036 (2020) Chen et al. [2022] Chen, S.Y.-C., Huang, C.-M., Hsing, C.-W., Goan, H.-S., Kao, Y.-J.: Variational quantum reinforcement learning via evolutionary optimization. Machine Learning: Science and Technology 3(1), 015025 (2022) Cong et al. [2019] Cong, I., Choi, S., Lukin, M.D.: Quantum convolutional neural networks. Nature Physics 15(12), 1273–1278 (2019) Chen et al. [2022a] Chen, S.Y.-C., Wei, T.-C., Zhang, C., Yu, H., Yoo, S.: Quantum convolutional neural networks for high energy physics data analysis. Physical Review Research 4(1), 013231 (2022) Chen et al. [2022b] Chen, S.Y.-C., Fry, D., Deshmukh, A., Rastunkov, V., Stefanski, C.: Reservoir computing via quantum recurrent neural networks. arXiv preprint arXiv:2211.02612 (2022) Di Sipio et al. [2022] Di Sipio, R., Huang, J.-H., Chen, S.Y.-C., Mangini, S., Worring, M.: The dawn of quantum natural language processing. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8612–8616 (2022). IEEE Giovannetti et al. [2004] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced measurements: beating the standard quantum limit. Science 306(5700), 1330–1336 (2004) Giovannetti et al. [2001] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced positioning and clock synchronization. Nature 412(6845), 417–419 (2001) Chehimi et al. [2023] Chehimi, M., Hashash, O., Saad, W.: The roadmap to a quantum-enabled wireless metaverse: Beyond the classical limits. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7–12 (2023). IEEE Chehimi and Saad [2021] Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Stein, S.A., Baheri, B., Tischio, R.M., Mao, Y., Guan, Q., Li, A., Fang, B., Xu, S.: Qugan: A generative adversarial network through quantum states. arXiv preprint arXiv:2010.09036 (2020) Chen et al. [2022] Chen, S.Y.-C., Huang, C.-M., Hsing, C.-W., Goan, H.-S., Kao, Y.-J.: Variational quantum reinforcement learning via evolutionary optimization. Machine Learning: Science and Technology 3(1), 015025 (2022) Cong et al. [2019] Cong, I., Choi, S., Lukin, M.D.: Quantum convolutional neural networks. Nature Physics 15(12), 1273–1278 (2019) Chen et al. [2022a] Chen, S.Y.-C., Wei, T.-C., Zhang, C., Yu, H., Yoo, S.: Quantum convolutional neural networks for high energy physics data analysis. Physical Review Research 4(1), 013231 (2022) Chen et al. [2022b] Chen, S.Y.-C., Fry, D., Deshmukh, A., Rastunkov, V., Stefanski, C.: Reservoir computing via quantum recurrent neural networks. arXiv preprint arXiv:2211.02612 (2022) Di Sipio et al. [2022] Di Sipio, R., Huang, J.-H., Chen, S.Y.-C., Mangini, S., Worring, M.: The dawn of quantum natural language processing. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8612–8616 (2022). IEEE Giovannetti et al. [2004] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced measurements: beating the standard quantum limit. Science 306(5700), 1330–1336 (2004) Giovannetti et al. [2001] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced positioning and clock synchronization. Nature 412(6845), 417–419 (2001) Chehimi et al. [2023] Chehimi, M., Hashash, O., Saad, W.: The roadmap to a quantum-enabled wireless metaverse: Beyond the classical limits. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7–12 (2023). IEEE Chehimi and Saad [2021] Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chen, S.Y.-C., Huang, C.-M., Hsing, C.-W., Goan, H.-S., Kao, Y.-J.: Variational quantum reinforcement learning via evolutionary optimization. Machine Learning: Science and Technology 3(1), 015025 (2022) Cong et al. [2019] Cong, I., Choi, S., Lukin, M.D.: Quantum convolutional neural networks. Nature Physics 15(12), 1273–1278 (2019) Chen et al. [2022a] Chen, S.Y.-C., Wei, T.-C., Zhang, C., Yu, H., Yoo, S.: Quantum convolutional neural networks for high energy physics data analysis. Physical Review Research 4(1), 013231 (2022) Chen et al. [2022b] Chen, S.Y.-C., Fry, D., Deshmukh, A., Rastunkov, V., Stefanski, C.: Reservoir computing via quantum recurrent neural networks. arXiv preprint arXiv:2211.02612 (2022) Di Sipio et al. [2022] Di Sipio, R., Huang, J.-H., Chen, S.Y.-C., Mangini, S., Worring, M.: The dawn of quantum natural language processing. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8612–8616 (2022). IEEE Giovannetti et al. [2004] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced measurements: beating the standard quantum limit. Science 306(5700), 1330–1336 (2004) Giovannetti et al. [2001] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced positioning and clock synchronization. Nature 412(6845), 417–419 (2001) Chehimi et al. [2023] Chehimi, M., Hashash, O., Saad, W.: The roadmap to a quantum-enabled wireless metaverse: Beyond the classical limits. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7–12 (2023). IEEE Chehimi and Saad [2021] Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Cong, I., Choi, S., Lukin, M.D.: Quantum convolutional neural networks. Nature Physics 15(12), 1273–1278 (2019) Chen et al. [2022a] Chen, S.Y.-C., Wei, T.-C., Zhang, C., Yu, H., Yoo, S.: Quantum convolutional neural networks for high energy physics data analysis. Physical Review Research 4(1), 013231 (2022) Chen et al. [2022b] Chen, S.Y.-C., Fry, D., Deshmukh, A., Rastunkov, V., Stefanski, C.: Reservoir computing via quantum recurrent neural networks. arXiv preprint arXiv:2211.02612 (2022) Di Sipio et al. [2022] Di Sipio, R., Huang, J.-H., Chen, S.Y.-C., Mangini, S., Worring, M.: The dawn of quantum natural language processing. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8612–8616 (2022). IEEE Giovannetti et al. [2004] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced measurements: beating the standard quantum limit. Science 306(5700), 1330–1336 (2004) Giovannetti et al. [2001] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced positioning and clock synchronization. Nature 412(6845), 417–419 (2001) Chehimi et al. [2023] Chehimi, M., Hashash, O., Saad, W.: The roadmap to a quantum-enabled wireless metaverse: Beyond the classical limits. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7–12 (2023). IEEE Chehimi and Saad [2021] Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chen, S.Y.-C., Wei, T.-C., Zhang, C., Yu, H., Yoo, S.: Quantum convolutional neural networks for high energy physics data analysis. Physical Review Research 4(1), 013231 (2022) Chen et al. [2022b] Chen, S.Y.-C., Fry, D., Deshmukh, A., Rastunkov, V., Stefanski, C.: Reservoir computing via quantum recurrent neural networks. arXiv preprint arXiv:2211.02612 (2022) Di Sipio et al. [2022] Di Sipio, R., Huang, J.-H., Chen, S.Y.-C., Mangini, S., Worring, M.: The dawn of quantum natural language processing. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8612–8616 (2022). IEEE Giovannetti et al. [2004] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced measurements: beating the standard quantum limit. Science 306(5700), 1330–1336 (2004) Giovannetti et al. [2001] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced positioning and clock synchronization. Nature 412(6845), 417–419 (2001) Chehimi et al. [2023] Chehimi, M., Hashash, O., Saad, W.: The roadmap to a quantum-enabled wireless metaverse: Beyond the classical limits. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7–12 (2023). IEEE Chehimi and Saad [2021] Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chen, S.Y.-C., Fry, D., Deshmukh, A., Rastunkov, V., Stefanski, C.: Reservoir computing via quantum recurrent neural networks. arXiv preprint arXiv:2211.02612 (2022) Di Sipio et al. [2022] Di Sipio, R., Huang, J.-H., Chen, S.Y.-C., Mangini, S., Worring, M.: The dawn of quantum natural language processing. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8612–8616 (2022). IEEE Giovannetti et al. [2004] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced measurements: beating the standard quantum limit. Science 306(5700), 1330–1336 (2004) Giovannetti et al. [2001] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced positioning and clock synchronization. Nature 412(6845), 417–419 (2001) Chehimi et al. [2023] Chehimi, M., Hashash, O., Saad, W.: The roadmap to a quantum-enabled wireless metaverse: Beyond the classical limits. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7–12 (2023). IEEE Chehimi and Saad [2021] Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Di Sipio, R., Huang, J.-H., Chen, S.Y.-C., Mangini, S., Worring, M.: The dawn of quantum natural language processing. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8612–8616 (2022). IEEE Giovannetti et al. [2004] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced measurements: beating the standard quantum limit. Science 306(5700), 1330–1336 (2004) Giovannetti et al. [2001] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced positioning and clock synchronization. Nature 412(6845), 417–419 (2001) Chehimi et al. [2023] Chehimi, M., Hashash, O., Saad, W.: The roadmap to a quantum-enabled wireless metaverse: Beyond the classical limits. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7–12 (2023). IEEE Chehimi and Saad [2021] Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced measurements: beating the standard quantum limit. Science 306(5700), 1330–1336 (2004) Giovannetti et al. [2001] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced positioning and clock synchronization. Nature 412(6845), 417–419 (2001) Chehimi et al. [2023] Chehimi, M., Hashash, O., Saad, W.: The roadmap to a quantum-enabled wireless metaverse: Beyond the classical limits. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7–12 (2023). IEEE Chehimi and Saad [2021] Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced positioning and clock synchronization. Nature 412(6845), 417–419 (2001) Chehimi et al. [2023] Chehimi, M., Hashash, O., Saad, W.: The roadmap to a quantum-enabled wireless metaverse: Beyond the classical limits. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7–12 (2023). IEEE Chehimi and Saad [2021] Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chehimi, M., Hashash, O., Saad, W.: The roadmap to a quantum-enabled wireless metaverse: Beyond the classical limits. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7–12 (2023). IEEE Chehimi and Saad [2021] Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. 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[2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. 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Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? 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[2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. 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[2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. 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[2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. 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Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chen, S.Y.-C., Yoo, S., Fang, Y.-L.L.: Quantum long short-term memory. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8622–8626 (2022). IEEE Bausch [2020] Bausch, J.: Recurrent quantum neural networks. Advances in neural information processing systems 33, 1368–1379 (2020) Stein et al. [2020] Stein, S.A., Baheri, B., Tischio, R.M., Mao, Y., Guan, Q., Li, A., Fang, B., Xu, S.: Qugan: A generative adversarial network through quantum states. arXiv preprint arXiv:2010.09036 (2020) Chen et al. [2022] Chen, S.Y.-C., Huang, C.-M., Hsing, C.-W., Goan, H.-S., Kao, Y.-J.: Variational quantum reinforcement learning via evolutionary optimization. Machine Learning: Science and Technology 3(1), 015025 (2022) Cong et al. [2019] Cong, I., Choi, S., Lukin, M.D.: Quantum convolutional neural networks. Nature Physics 15(12), 1273–1278 (2019) Chen et al. [2022a] Chen, S.Y.-C., Wei, T.-C., Zhang, C., Yu, H., Yoo, S.: Quantum convolutional neural networks for high energy physics data analysis. Physical Review Research 4(1), 013231 (2022) Chen et al. [2022b] Chen, S.Y.-C., Fry, D., Deshmukh, A., Rastunkov, V., Stefanski, C.: Reservoir computing via quantum recurrent neural networks. arXiv preprint arXiv:2211.02612 (2022) Di Sipio et al. [2022] Di Sipio, R., Huang, J.-H., Chen, S.Y.-C., Mangini, S., Worring, M.: The dawn of quantum natural language processing. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8612–8616 (2022). IEEE Giovannetti et al. [2004] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced measurements: beating the standard quantum limit. Science 306(5700), 1330–1336 (2004) Giovannetti et al. [2001] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced positioning and clock synchronization. Nature 412(6845), 417–419 (2001) Chehimi et al. [2023] Chehimi, M., Hashash, O., Saad, W.: The roadmap to a quantum-enabled wireless metaverse: Beyond the classical limits. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7–12 (2023). IEEE Chehimi and Saad [2021] Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Bausch, J.: Recurrent quantum neural networks. Advances in neural information processing systems 33, 1368–1379 (2020) Stein et al. [2020] Stein, S.A., Baheri, B., Tischio, R.M., Mao, Y., Guan, Q., Li, A., Fang, B., Xu, S.: Qugan: A generative adversarial network through quantum states. arXiv preprint arXiv:2010.09036 (2020) Chen et al. [2022] Chen, S.Y.-C., Huang, C.-M., Hsing, C.-W., Goan, H.-S., Kao, Y.-J.: Variational quantum reinforcement learning via evolutionary optimization. Machine Learning: Science and Technology 3(1), 015025 (2022) Cong et al. [2019] Cong, I., Choi, S., Lukin, M.D.: Quantum convolutional neural networks. Nature Physics 15(12), 1273–1278 (2019) Chen et al. [2022a] Chen, S.Y.-C., Wei, T.-C., Zhang, C., Yu, H., Yoo, S.: Quantum convolutional neural networks for high energy physics data analysis. Physical Review Research 4(1), 013231 (2022) Chen et al. [2022b] Chen, S.Y.-C., Fry, D., Deshmukh, A., Rastunkov, V., Stefanski, C.: Reservoir computing via quantum recurrent neural networks. arXiv preprint arXiv:2211.02612 (2022) Di Sipio et al. [2022] Di Sipio, R., Huang, J.-H., Chen, S.Y.-C., Mangini, S., Worring, M.: The dawn of quantum natural language processing. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8612–8616 (2022). IEEE Giovannetti et al. [2004] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced measurements: beating the standard quantum limit. Science 306(5700), 1330–1336 (2004) Giovannetti et al. [2001] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced positioning and clock synchronization. Nature 412(6845), 417–419 (2001) Chehimi et al. [2023] Chehimi, M., Hashash, O., Saad, W.: The roadmap to a quantum-enabled wireless metaverse: Beyond the classical limits. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7–12 (2023). IEEE Chehimi and Saad [2021] Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Stein, S.A., Baheri, B., Tischio, R.M., Mao, Y., Guan, Q., Li, A., Fang, B., Xu, S.: Qugan: A generative adversarial network through quantum states. arXiv preprint arXiv:2010.09036 (2020) Chen et al. [2022] Chen, S.Y.-C., Huang, C.-M., Hsing, C.-W., Goan, H.-S., Kao, Y.-J.: Variational quantum reinforcement learning via evolutionary optimization. Machine Learning: Science and Technology 3(1), 015025 (2022) Cong et al. [2019] Cong, I., Choi, S., Lukin, M.D.: Quantum convolutional neural networks. Nature Physics 15(12), 1273–1278 (2019) Chen et al. [2022a] Chen, S.Y.-C., Wei, T.-C., Zhang, C., Yu, H., Yoo, S.: Quantum convolutional neural networks for high energy physics data analysis. Physical Review Research 4(1), 013231 (2022) Chen et al. [2022b] Chen, S.Y.-C., Fry, D., Deshmukh, A., Rastunkov, V., Stefanski, C.: Reservoir computing via quantum recurrent neural networks. arXiv preprint arXiv:2211.02612 (2022) Di Sipio et al. [2022] Di Sipio, R., Huang, J.-H., Chen, S.Y.-C., Mangini, S., Worring, M.: The dawn of quantum natural language processing. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8612–8616 (2022). IEEE Giovannetti et al. [2004] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced measurements: beating the standard quantum limit. Science 306(5700), 1330–1336 (2004) Giovannetti et al. [2001] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced positioning and clock synchronization. Nature 412(6845), 417–419 (2001) Chehimi et al. [2023] Chehimi, M., Hashash, O., Saad, W.: The roadmap to a quantum-enabled wireless metaverse: Beyond the classical limits. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7–12 (2023). IEEE Chehimi and Saad [2021] Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chen, S.Y.-C., Huang, C.-M., Hsing, C.-W., Goan, H.-S., Kao, Y.-J.: Variational quantum reinforcement learning via evolutionary optimization. Machine Learning: Science and Technology 3(1), 015025 (2022) Cong et al. [2019] Cong, I., Choi, S., Lukin, M.D.: Quantum convolutional neural networks. Nature Physics 15(12), 1273–1278 (2019) Chen et al. [2022a] Chen, S.Y.-C., Wei, T.-C., Zhang, C., Yu, H., Yoo, S.: Quantum convolutional neural networks for high energy physics data analysis. Physical Review Research 4(1), 013231 (2022) Chen et al. [2022b] Chen, S.Y.-C., Fry, D., Deshmukh, A., Rastunkov, V., Stefanski, C.: Reservoir computing via quantum recurrent neural networks. arXiv preprint arXiv:2211.02612 (2022) Di Sipio et al. [2022] Di Sipio, R., Huang, J.-H., Chen, S.Y.-C., Mangini, S., Worring, M.: The dawn of quantum natural language processing. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8612–8616 (2022). IEEE Giovannetti et al. [2004] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced measurements: beating the standard quantum limit. Science 306(5700), 1330–1336 (2004) Giovannetti et al. [2001] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced positioning and clock synchronization. Nature 412(6845), 417–419 (2001) Chehimi et al. [2023] Chehimi, M., Hashash, O., Saad, W.: The roadmap to a quantum-enabled wireless metaverse: Beyond the classical limits. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7–12 (2023). IEEE Chehimi and Saad [2021] Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Cong, I., Choi, S., Lukin, M.D.: Quantum convolutional neural networks. Nature Physics 15(12), 1273–1278 (2019) Chen et al. [2022a] Chen, S.Y.-C., Wei, T.-C., Zhang, C., Yu, H., Yoo, S.: Quantum convolutional neural networks for high energy physics data analysis. Physical Review Research 4(1), 013231 (2022) Chen et al. [2022b] Chen, S.Y.-C., Fry, D., Deshmukh, A., Rastunkov, V., Stefanski, C.: Reservoir computing via quantum recurrent neural networks. arXiv preprint arXiv:2211.02612 (2022) Di Sipio et al. [2022] Di Sipio, R., Huang, J.-H., Chen, S.Y.-C., Mangini, S., Worring, M.: The dawn of quantum natural language processing. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8612–8616 (2022). IEEE Giovannetti et al. [2004] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced measurements: beating the standard quantum limit. Science 306(5700), 1330–1336 (2004) Giovannetti et al. [2001] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced positioning and clock synchronization. Nature 412(6845), 417–419 (2001) Chehimi et al. [2023] Chehimi, M., Hashash, O., Saad, W.: The roadmap to a quantum-enabled wireless metaverse: Beyond the classical limits. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7–12 (2023). IEEE Chehimi and Saad [2021] Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chen, S.Y.-C., Wei, T.-C., Zhang, C., Yu, H., Yoo, S.: Quantum convolutional neural networks for high energy physics data analysis. Physical Review Research 4(1), 013231 (2022) Chen et al. [2022b] Chen, S.Y.-C., Fry, D., Deshmukh, A., Rastunkov, V., Stefanski, C.: Reservoir computing via quantum recurrent neural networks. arXiv preprint arXiv:2211.02612 (2022) Di Sipio et al. [2022] Di Sipio, R., Huang, J.-H., Chen, S.Y.-C., Mangini, S., Worring, M.: The dawn of quantum natural language processing. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8612–8616 (2022). IEEE Giovannetti et al. [2004] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced measurements: beating the standard quantum limit. Science 306(5700), 1330–1336 (2004) Giovannetti et al. [2001] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced positioning and clock synchronization. Nature 412(6845), 417–419 (2001) Chehimi et al. [2023] Chehimi, M., Hashash, O., Saad, W.: The roadmap to a quantum-enabled wireless metaverse: Beyond the classical limits. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7–12 (2023). IEEE Chehimi and Saad [2021] Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chen, S.Y.-C., Fry, D., Deshmukh, A., Rastunkov, V., Stefanski, C.: Reservoir computing via quantum recurrent neural networks. arXiv preprint arXiv:2211.02612 (2022) Di Sipio et al. [2022] Di Sipio, R., Huang, J.-H., Chen, S.Y.-C., Mangini, S., Worring, M.: The dawn of quantum natural language processing. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8612–8616 (2022). IEEE Giovannetti et al. [2004] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced measurements: beating the standard quantum limit. Science 306(5700), 1330–1336 (2004) Giovannetti et al. [2001] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced positioning and clock synchronization. Nature 412(6845), 417–419 (2001) Chehimi et al. [2023] Chehimi, M., Hashash, O., Saad, W.: The roadmap to a quantum-enabled wireless metaverse: Beyond the classical limits. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7–12 (2023). IEEE Chehimi and Saad [2021] Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Di Sipio, R., Huang, J.-H., Chen, S.Y.-C., Mangini, S., Worring, M.: The dawn of quantum natural language processing. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8612–8616 (2022). IEEE Giovannetti et al. [2004] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced measurements: beating the standard quantum limit. Science 306(5700), 1330–1336 (2004) Giovannetti et al. [2001] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced positioning and clock synchronization. Nature 412(6845), 417–419 (2001) Chehimi et al. [2023] Chehimi, M., Hashash, O., Saad, W.: The roadmap to a quantum-enabled wireless metaverse: Beyond the classical limits. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7–12 (2023). IEEE Chehimi and Saad [2021] Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced measurements: beating the standard quantum limit. Science 306(5700), 1330–1336 (2004) Giovannetti et al. [2001] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced positioning and clock synchronization. Nature 412(6845), 417–419 (2001) Chehimi et al. [2023] Chehimi, M., Hashash, O., Saad, W.: The roadmap to a quantum-enabled wireless metaverse: Beyond the classical limits. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7–12 (2023). IEEE Chehimi and Saad [2021] Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. 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Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced positioning and clock synchronization. Nature 412(6845), 417–419 (2001) Chehimi et al. [2023] Chehimi, M., Hashash, O., Saad, W.: The roadmap to a quantum-enabled wireless metaverse: Beyond the classical limits. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7–12 (2023). IEEE Chehimi and Saad [2021] Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chehimi, M., Hashash, O., Saad, W.: The roadmap to a quantum-enabled wireless metaverse: Beyond the classical limits. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7–12 (2023). IEEE Chehimi and Saad [2021] Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. 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[2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. 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[2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. 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[2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. 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[2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. 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IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. 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[2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. 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[2022] Di Sipio, R., Huang, J.-H., Chen, S.Y.-C., Mangini, S., Worring, M.: The dawn of quantum natural language processing. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8612–8616 (2022). IEEE Giovannetti et al. [2004] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced measurements: beating the standard quantum limit. Science 306(5700), 1330–1336 (2004) Giovannetti et al. [2001] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced positioning and clock synchronization. Nature 412(6845), 417–419 (2001) Chehimi et al. [2023] Chehimi, M., Hashash, O., Saad, W.: The roadmap to a quantum-enabled wireless metaverse: Beyond the classical limits. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7–12 (2023). IEEE Chehimi and Saad [2021] Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Bausch, J.: Recurrent quantum neural networks. Advances in neural information processing systems 33, 1368–1379 (2020) Stein et al. [2020] Stein, S.A., Baheri, B., Tischio, R.M., Mao, Y., Guan, Q., Li, A., Fang, B., Xu, S.: Qugan: A generative adversarial network through quantum states. arXiv preprint arXiv:2010.09036 (2020) Chen et al. [2022] Chen, S.Y.-C., Huang, C.-M., Hsing, C.-W., Goan, H.-S., Kao, Y.-J.: Variational quantum reinforcement learning via evolutionary optimization. Machine Learning: Science and Technology 3(1), 015025 (2022) Cong et al. [2019] Cong, I., Choi, S., Lukin, M.D.: Quantum convolutional neural networks. Nature Physics 15(12), 1273–1278 (2019) Chen et al. [2022a] Chen, S.Y.-C., Wei, T.-C., Zhang, C., Yu, H., Yoo, S.: Quantum convolutional neural networks for high energy physics data analysis. Physical Review Research 4(1), 013231 (2022) Chen et al. [2022b] Chen, S.Y.-C., Fry, D., Deshmukh, A., Rastunkov, V., Stefanski, C.: Reservoir computing via quantum recurrent neural networks. arXiv preprint arXiv:2211.02612 (2022) Di Sipio et al. [2022] Di Sipio, R., Huang, J.-H., Chen, S.Y.-C., Mangini, S., Worring, M.: The dawn of quantum natural language processing. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8612–8616 (2022). IEEE Giovannetti et al. [2004] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced measurements: beating the standard quantum limit. Science 306(5700), 1330–1336 (2004) Giovannetti et al. [2001] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced positioning and clock synchronization. Nature 412(6845), 417–419 (2001) Chehimi et al. [2023] Chehimi, M., Hashash, O., Saad, W.: The roadmap to a quantum-enabled wireless metaverse: Beyond the classical limits. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7–12 (2023). IEEE Chehimi and Saad [2021] Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Stein, S.A., Baheri, B., Tischio, R.M., Mao, Y., Guan, Q., Li, A., Fang, B., Xu, S.: Qugan: A generative adversarial network through quantum states. arXiv preprint arXiv:2010.09036 (2020) Chen et al. [2022] Chen, S.Y.-C., Huang, C.-M., Hsing, C.-W., Goan, H.-S., Kao, Y.-J.: Variational quantum reinforcement learning via evolutionary optimization. Machine Learning: Science and Technology 3(1), 015025 (2022) Cong et al. [2019] Cong, I., Choi, S., Lukin, M.D.: Quantum convolutional neural networks. Nature Physics 15(12), 1273–1278 (2019) Chen et al. [2022a] Chen, S.Y.-C., Wei, T.-C., Zhang, C., Yu, H., Yoo, S.: Quantum convolutional neural networks for high energy physics data analysis. Physical Review Research 4(1), 013231 (2022) Chen et al. [2022b] Chen, S.Y.-C., Fry, D., Deshmukh, A., Rastunkov, V., Stefanski, C.: Reservoir computing via quantum recurrent neural networks. arXiv preprint arXiv:2211.02612 (2022) Di Sipio et al. [2022] Di Sipio, R., Huang, J.-H., Chen, S.Y.-C., Mangini, S., Worring, M.: The dawn of quantum natural language processing. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8612–8616 (2022). IEEE Giovannetti et al. [2004] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced measurements: beating the standard quantum limit. Science 306(5700), 1330–1336 (2004) Giovannetti et al. [2001] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced positioning and clock synchronization. Nature 412(6845), 417–419 (2001) Chehimi et al. [2023] Chehimi, M., Hashash, O., Saad, W.: The roadmap to a quantum-enabled wireless metaverse: Beyond the classical limits. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7–12 (2023). IEEE Chehimi and Saad [2021] Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chen, S.Y.-C., Huang, C.-M., Hsing, C.-W., Goan, H.-S., Kao, Y.-J.: Variational quantum reinforcement learning via evolutionary optimization. Machine Learning: Science and Technology 3(1), 015025 (2022) Cong et al. [2019] Cong, I., Choi, S., Lukin, M.D.: Quantum convolutional neural networks. Nature Physics 15(12), 1273–1278 (2019) Chen et al. [2022a] Chen, S.Y.-C., Wei, T.-C., Zhang, C., Yu, H., Yoo, S.: Quantum convolutional neural networks for high energy physics data analysis. Physical Review Research 4(1), 013231 (2022) Chen et al. [2022b] Chen, S.Y.-C., Fry, D., Deshmukh, A., Rastunkov, V., Stefanski, C.: Reservoir computing via quantum recurrent neural networks. arXiv preprint arXiv:2211.02612 (2022) Di Sipio et al. [2022] Di Sipio, R., Huang, J.-H., Chen, S.Y.-C., Mangini, S., Worring, M.: The dawn of quantum natural language processing. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8612–8616 (2022). IEEE Giovannetti et al. [2004] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced measurements: beating the standard quantum limit. Science 306(5700), 1330–1336 (2004) Giovannetti et al. [2001] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced positioning and clock synchronization. Nature 412(6845), 417–419 (2001) Chehimi et al. [2023] Chehimi, M., Hashash, O., Saad, W.: The roadmap to a quantum-enabled wireless metaverse: Beyond the classical limits. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7–12 (2023). IEEE Chehimi and Saad [2021] Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Cong, I., Choi, S., Lukin, M.D.: Quantum convolutional neural networks. Nature Physics 15(12), 1273–1278 (2019) Chen et al. [2022a] Chen, S.Y.-C., Wei, T.-C., Zhang, C., Yu, H., Yoo, S.: Quantum convolutional neural networks for high energy physics data analysis. Physical Review Research 4(1), 013231 (2022) Chen et al. [2022b] Chen, S.Y.-C., Fry, D., Deshmukh, A., Rastunkov, V., Stefanski, C.: Reservoir computing via quantum recurrent neural networks. arXiv preprint arXiv:2211.02612 (2022) Di Sipio et al. [2022] Di Sipio, R., Huang, J.-H., Chen, S.Y.-C., Mangini, S., Worring, M.: The dawn of quantum natural language processing. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8612–8616 (2022). IEEE Giovannetti et al. [2004] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced measurements: beating the standard quantum limit. Science 306(5700), 1330–1336 (2004) Giovannetti et al. [2001] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced positioning and clock synchronization. Nature 412(6845), 417–419 (2001) Chehimi et al. [2023] Chehimi, M., Hashash, O., Saad, W.: The roadmap to a quantum-enabled wireless metaverse: Beyond the classical limits. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7–12 (2023). IEEE Chehimi and Saad [2021] Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chen, S.Y.-C., Wei, T.-C., Zhang, C., Yu, H., Yoo, S.: Quantum convolutional neural networks for high energy physics data analysis. Physical Review Research 4(1), 013231 (2022) Chen et al. [2022b] Chen, S.Y.-C., Fry, D., Deshmukh, A., Rastunkov, V., Stefanski, C.: Reservoir computing via quantum recurrent neural networks. arXiv preprint arXiv:2211.02612 (2022) Di Sipio et al. [2022] Di Sipio, R., Huang, J.-H., Chen, S.Y.-C., Mangini, S., Worring, M.: The dawn of quantum natural language processing. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8612–8616 (2022). IEEE Giovannetti et al. [2004] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced measurements: beating the standard quantum limit. Science 306(5700), 1330–1336 (2004) Giovannetti et al. [2001] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced positioning and clock synchronization. Nature 412(6845), 417–419 (2001) Chehimi et al. [2023] Chehimi, M., Hashash, O., Saad, W.: The roadmap to a quantum-enabled wireless metaverse: Beyond the classical limits. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7–12 (2023). IEEE Chehimi and Saad [2021] Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chen, S.Y.-C., Fry, D., Deshmukh, A., Rastunkov, V., Stefanski, C.: Reservoir computing via quantum recurrent neural networks. arXiv preprint arXiv:2211.02612 (2022) Di Sipio et al. [2022] Di Sipio, R., Huang, J.-H., Chen, S.Y.-C., Mangini, S., Worring, M.: The dawn of quantum natural language processing. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8612–8616 (2022). IEEE Giovannetti et al. [2004] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced measurements: beating the standard quantum limit. Science 306(5700), 1330–1336 (2004) Giovannetti et al. [2001] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced positioning and clock synchronization. Nature 412(6845), 417–419 (2001) Chehimi et al. [2023] Chehimi, M., Hashash, O., Saad, W.: The roadmap to a quantum-enabled wireless metaverse: Beyond the classical limits. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7–12 (2023). IEEE Chehimi and Saad [2021] Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Di Sipio, R., Huang, J.-H., Chen, S.Y.-C., Mangini, S., Worring, M.: The dawn of quantum natural language processing. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8612–8616 (2022). IEEE Giovannetti et al. [2004] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced measurements: beating the standard quantum limit. Science 306(5700), 1330–1336 (2004) Giovannetti et al. [2001] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced positioning and clock synchronization. Nature 412(6845), 417–419 (2001) Chehimi et al. [2023] Chehimi, M., Hashash, O., Saad, W.: The roadmap to a quantum-enabled wireless metaverse: Beyond the classical limits. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7–12 (2023). IEEE Chehimi and Saad [2021] Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced measurements: beating the standard quantum limit. Science 306(5700), 1330–1336 (2004) Giovannetti et al. [2001] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced positioning and clock synchronization. Nature 412(6845), 417–419 (2001) Chehimi et al. [2023] Chehimi, M., Hashash, O., Saad, W.: The roadmap to a quantum-enabled wireless metaverse: Beyond the classical limits. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7–12 (2023). IEEE Chehimi and Saad [2021] Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. 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In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chehimi, M., Hashash, O., Saad, W.: The roadmap to a quantum-enabled wireless metaverse: Beyond the classical limits. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7–12 (2023). IEEE Chehimi and Saad [2021] Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. 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[2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. 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[2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. 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IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. 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[2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Stein, S.A., Baheri, B., Tischio, R.M., Mao, Y., Guan, Q., Li, A., Fang, B., Xu, S.: Qugan: A generative adversarial network through quantum states. arXiv preprint arXiv:2010.09036 (2020) Chen et al. [2022] Chen, S.Y.-C., Huang, C.-M., Hsing, C.-W., Goan, H.-S., Kao, Y.-J.: Variational quantum reinforcement learning via evolutionary optimization. Machine Learning: Science and Technology 3(1), 015025 (2022) Cong et al. [2019] Cong, I., Choi, S., Lukin, M.D.: Quantum convolutional neural networks. Nature Physics 15(12), 1273–1278 (2019) Chen et al. [2022a] Chen, S.Y.-C., Wei, T.-C., Zhang, C., Yu, H., Yoo, S.: Quantum convolutional neural networks for high energy physics data analysis. Physical Review Research 4(1), 013231 (2022) Chen et al. [2022b] Chen, S.Y.-C., Fry, D., Deshmukh, A., Rastunkov, V., Stefanski, C.: Reservoir computing via quantum recurrent neural networks. arXiv preprint arXiv:2211.02612 (2022) Di Sipio et al. [2022] Di Sipio, R., Huang, J.-H., Chen, S.Y.-C., Mangini, S., Worring, M.: The dawn of quantum natural language processing. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8612–8616 (2022). IEEE Giovannetti et al. [2004] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced measurements: beating the standard quantum limit. Science 306(5700), 1330–1336 (2004) Giovannetti et al. [2001] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced positioning and clock synchronization. Nature 412(6845), 417–419 (2001) Chehimi et al. [2023] Chehimi, M., Hashash, O., Saad, W.: The roadmap to a quantum-enabled wireless metaverse: Beyond the classical limits. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7–12 (2023). IEEE Chehimi and Saad [2021] Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chen, S.Y.-C., Huang, C.-M., Hsing, C.-W., Goan, H.-S., Kao, Y.-J.: Variational quantum reinforcement learning via evolutionary optimization. Machine Learning: Science and Technology 3(1), 015025 (2022) Cong et al. [2019] Cong, I., Choi, S., Lukin, M.D.: Quantum convolutional neural networks. Nature Physics 15(12), 1273–1278 (2019) Chen et al. [2022a] Chen, S.Y.-C., Wei, T.-C., Zhang, C., Yu, H., Yoo, S.: Quantum convolutional neural networks for high energy physics data analysis. Physical Review Research 4(1), 013231 (2022) Chen et al. [2022b] Chen, S.Y.-C., Fry, D., Deshmukh, A., Rastunkov, V., Stefanski, C.: Reservoir computing via quantum recurrent neural networks. arXiv preprint arXiv:2211.02612 (2022) Di Sipio et al. [2022] Di Sipio, R., Huang, J.-H., Chen, S.Y.-C., Mangini, S., Worring, M.: The dawn of quantum natural language processing. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8612–8616 (2022). IEEE Giovannetti et al. [2004] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced measurements: beating the standard quantum limit. Science 306(5700), 1330–1336 (2004) Giovannetti et al. [2001] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced positioning and clock synchronization. Nature 412(6845), 417–419 (2001) Chehimi et al. [2023] Chehimi, M., Hashash, O., Saad, W.: The roadmap to a quantum-enabled wireless metaverse: Beyond the classical limits. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7–12 (2023). IEEE Chehimi and Saad [2021] Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Cong, I., Choi, S., Lukin, M.D.: Quantum convolutional neural networks. Nature Physics 15(12), 1273–1278 (2019) Chen et al. [2022a] Chen, S.Y.-C., Wei, T.-C., Zhang, C., Yu, H., Yoo, S.: Quantum convolutional neural networks for high energy physics data analysis. Physical Review Research 4(1), 013231 (2022) Chen et al. [2022b] Chen, S.Y.-C., Fry, D., Deshmukh, A., Rastunkov, V., Stefanski, C.: Reservoir computing via quantum recurrent neural networks. arXiv preprint arXiv:2211.02612 (2022) Di Sipio et al. [2022] Di Sipio, R., Huang, J.-H., Chen, S.Y.-C., Mangini, S., Worring, M.: The dawn of quantum natural language processing. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8612–8616 (2022). IEEE Giovannetti et al. [2004] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced measurements: beating the standard quantum limit. Science 306(5700), 1330–1336 (2004) Giovannetti et al. [2001] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced positioning and clock synchronization. Nature 412(6845), 417–419 (2001) Chehimi et al. [2023] Chehimi, M., Hashash, O., Saad, W.: The roadmap to a quantum-enabled wireless metaverse: Beyond the classical limits. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7–12 (2023). IEEE Chehimi and Saad [2021] Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chen, S.Y.-C., Wei, T.-C., Zhang, C., Yu, H., Yoo, S.: Quantum convolutional neural networks for high energy physics data analysis. Physical Review Research 4(1), 013231 (2022) Chen et al. [2022b] Chen, S.Y.-C., Fry, D., Deshmukh, A., Rastunkov, V., Stefanski, C.: Reservoir computing via quantum recurrent neural networks. arXiv preprint arXiv:2211.02612 (2022) Di Sipio et al. [2022] Di Sipio, R., Huang, J.-H., Chen, S.Y.-C., Mangini, S., Worring, M.: The dawn of quantum natural language processing. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8612–8616 (2022). IEEE Giovannetti et al. [2004] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced measurements: beating the standard quantum limit. Science 306(5700), 1330–1336 (2004) Giovannetti et al. [2001] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced positioning and clock synchronization. Nature 412(6845), 417–419 (2001) Chehimi et al. [2023] Chehimi, M., Hashash, O., Saad, W.: The roadmap to a quantum-enabled wireless metaverse: Beyond the classical limits. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7–12 (2023). IEEE Chehimi and Saad [2021] Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chen, S.Y.-C., Fry, D., Deshmukh, A., Rastunkov, V., Stefanski, C.: Reservoir computing via quantum recurrent neural networks. arXiv preprint arXiv:2211.02612 (2022) Di Sipio et al. [2022] Di Sipio, R., Huang, J.-H., Chen, S.Y.-C., Mangini, S., Worring, M.: The dawn of quantum natural language processing. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8612–8616 (2022). IEEE Giovannetti et al. [2004] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced measurements: beating the standard quantum limit. Science 306(5700), 1330–1336 (2004) Giovannetti et al. [2001] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced positioning and clock synchronization. Nature 412(6845), 417–419 (2001) Chehimi et al. [2023] Chehimi, M., Hashash, O., Saad, W.: The roadmap to a quantum-enabled wireless metaverse: Beyond the classical limits. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7–12 (2023). IEEE Chehimi and Saad [2021] Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Di Sipio, R., Huang, J.-H., Chen, S.Y.-C., Mangini, S., Worring, M.: The dawn of quantum natural language processing. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8612–8616 (2022). IEEE Giovannetti et al. [2004] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced measurements: beating the standard quantum limit. Science 306(5700), 1330–1336 (2004) Giovannetti et al. [2001] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced positioning and clock synchronization. Nature 412(6845), 417–419 (2001) Chehimi et al. [2023] Chehimi, M., Hashash, O., Saad, W.: The roadmap to a quantum-enabled wireless metaverse: Beyond the classical limits. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7–12 (2023). IEEE Chehimi and Saad [2021] Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced measurements: beating the standard quantum limit. Science 306(5700), 1330–1336 (2004) Giovannetti et al. [2001] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced positioning and clock synchronization. Nature 412(6845), 417–419 (2001) Chehimi et al. [2023] Chehimi, M., Hashash, O., Saad, W.: The roadmap to a quantum-enabled wireless metaverse: Beyond the classical limits. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7–12 (2023). IEEE Chehimi and Saad [2021] Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced positioning and clock synchronization. Nature 412(6845), 417–419 (2001) Chehimi et al. [2023] Chehimi, M., Hashash, O., Saad, W.: The roadmap to a quantum-enabled wireless metaverse: Beyond the classical limits. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7–12 (2023). IEEE Chehimi and Saad [2021] Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chehimi, M., Hashash, O., Saad, W.: The roadmap to a quantum-enabled wireless metaverse: Beyond the classical limits. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7–12 (2023). IEEE Chehimi and Saad [2021] Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. 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New Journal of Physics 15(1), 013041 (2013) Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013)
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[2022b] Chen, S.Y.-C., Fry, D., Deshmukh, A., Rastunkov, V., Stefanski, C.: Reservoir computing via quantum recurrent neural networks. arXiv preprint arXiv:2211.02612 (2022) Di Sipio et al. [2022] Di Sipio, R., Huang, J.-H., Chen, S.Y.-C., Mangini, S., Worring, M.: The dawn of quantum natural language processing. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8612–8616 (2022). IEEE Giovannetti et al. [2004] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced measurements: beating the standard quantum limit. Science 306(5700), 1330–1336 (2004) Giovannetti et al. [2001] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced positioning and clock synchronization. Nature 412(6845), 417–419 (2001) Chehimi et al. [2023] Chehimi, M., Hashash, O., Saad, W.: The roadmap to a quantum-enabled wireless metaverse: Beyond the classical limits. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7–12 (2023). IEEE Chehimi and Saad [2021] Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Stein, S.A., Baheri, B., Tischio, R.M., Mao, Y., Guan, Q., Li, A., Fang, B., Xu, S.: Qugan: A generative adversarial network through quantum states. arXiv preprint arXiv:2010.09036 (2020) Chen et al. [2022] Chen, S.Y.-C., Huang, C.-M., Hsing, C.-W., Goan, H.-S., Kao, Y.-J.: Variational quantum reinforcement learning via evolutionary optimization. Machine Learning: Science and Technology 3(1), 015025 (2022) Cong et al. [2019] Cong, I., Choi, S., Lukin, M.D.: Quantum convolutional neural networks. Nature Physics 15(12), 1273–1278 (2019) Chen et al. [2022a] Chen, S.Y.-C., Wei, T.-C., Zhang, C., Yu, H., Yoo, S.: Quantum convolutional neural networks for high energy physics data analysis. Physical Review Research 4(1), 013231 (2022) Chen et al. [2022b] Chen, S.Y.-C., Fry, D., Deshmukh, A., Rastunkov, V., Stefanski, C.: Reservoir computing via quantum recurrent neural networks. arXiv preprint arXiv:2211.02612 (2022) Di Sipio et al. [2022] Di Sipio, R., Huang, J.-H., Chen, S.Y.-C., Mangini, S., Worring, M.: The dawn of quantum natural language processing. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8612–8616 (2022). IEEE Giovannetti et al. [2004] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced measurements: beating the standard quantum limit. Science 306(5700), 1330–1336 (2004) Giovannetti et al. [2001] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced positioning and clock synchronization. Nature 412(6845), 417–419 (2001) Chehimi et al. [2023] Chehimi, M., Hashash, O., Saad, W.: The roadmap to a quantum-enabled wireless metaverse: Beyond the classical limits. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7–12 (2023). IEEE Chehimi and Saad [2021] Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chen, S.Y.-C., Huang, C.-M., Hsing, C.-W., Goan, H.-S., Kao, Y.-J.: Variational quantum reinforcement learning via evolutionary optimization. Machine Learning: Science and Technology 3(1), 015025 (2022) Cong et al. [2019] Cong, I., Choi, S., Lukin, M.D.: Quantum convolutional neural networks. Nature Physics 15(12), 1273–1278 (2019) Chen et al. [2022a] Chen, S.Y.-C., Wei, T.-C., Zhang, C., Yu, H., Yoo, S.: Quantum convolutional neural networks for high energy physics data analysis. Physical Review Research 4(1), 013231 (2022) Chen et al. [2022b] Chen, S.Y.-C., Fry, D., Deshmukh, A., Rastunkov, V., Stefanski, C.: Reservoir computing via quantum recurrent neural networks. arXiv preprint arXiv:2211.02612 (2022) Di Sipio et al. [2022] Di Sipio, R., Huang, J.-H., Chen, S.Y.-C., Mangini, S., Worring, M.: The dawn of quantum natural language processing. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8612–8616 (2022). IEEE Giovannetti et al. [2004] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced measurements: beating the standard quantum limit. Science 306(5700), 1330–1336 (2004) Giovannetti et al. [2001] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced positioning and clock synchronization. Nature 412(6845), 417–419 (2001) Chehimi et al. [2023] Chehimi, M., Hashash, O., Saad, W.: The roadmap to a quantum-enabled wireless metaverse: Beyond the classical limits. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7–12 (2023). IEEE Chehimi and Saad [2021] Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Cong, I., Choi, S., Lukin, M.D.: Quantum convolutional neural networks. Nature Physics 15(12), 1273–1278 (2019) Chen et al. [2022a] Chen, S.Y.-C., Wei, T.-C., Zhang, C., Yu, H., Yoo, S.: Quantum convolutional neural networks for high energy physics data analysis. Physical Review Research 4(1), 013231 (2022) Chen et al. [2022b] Chen, S.Y.-C., Fry, D., Deshmukh, A., Rastunkov, V., Stefanski, C.: Reservoir computing via quantum recurrent neural networks. arXiv preprint arXiv:2211.02612 (2022) Di Sipio et al. [2022] Di Sipio, R., Huang, J.-H., Chen, S.Y.-C., Mangini, S., Worring, M.: The dawn of quantum natural language processing. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8612–8616 (2022). IEEE Giovannetti et al. [2004] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced measurements: beating the standard quantum limit. Science 306(5700), 1330–1336 (2004) Giovannetti et al. [2001] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced positioning and clock synchronization. Nature 412(6845), 417–419 (2001) Chehimi et al. [2023] Chehimi, M., Hashash, O., Saad, W.: The roadmap to a quantum-enabled wireless metaverse: Beyond the classical limits. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7–12 (2023). IEEE Chehimi and Saad [2021] Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chen, S.Y.-C., Wei, T.-C., Zhang, C., Yu, H., Yoo, S.: Quantum convolutional neural networks for high energy physics data analysis. Physical Review Research 4(1), 013231 (2022) Chen et al. [2022b] Chen, S.Y.-C., Fry, D., Deshmukh, A., Rastunkov, V., Stefanski, C.: Reservoir computing via quantum recurrent neural networks. arXiv preprint arXiv:2211.02612 (2022) Di Sipio et al. [2022] Di Sipio, R., Huang, J.-H., Chen, S.Y.-C., Mangini, S., Worring, M.: The dawn of quantum natural language processing. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8612–8616 (2022). IEEE Giovannetti et al. [2004] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced measurements: beating the standard quantum limit. Science 306(5700), 1330–1336 (2004) Giovannetti et al. [2001] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced positioning and clock synchronization. Nature 412(6845), 417–419 (2001) Chehimi et al. [2023] Chehimi, M., Hashash, O., Saad, W.: The roadmap to a quantum-enabled wireless metaverse: Beyond the classical limits. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7–12 (2023). IEEE Chehimi and Saad [2021] Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chen, S.Y.-C., Fry, D., Deshmukh, A., Rastunkov, V., Stefanski, C.: Reservoir computing via quantum recurrent neural networks. arXiv preprint arXiv:2211.02612 (2022) Di Sipio et al. [2022] Di Sipio, R., Huang, J.-H., Chen, S.Y.-C., Mangini, S., Worring, M.: The dawn of quantum natural language processing. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8612–8616 (2022). IEEE Giovannetti et al. [2004] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced measurements: beating the standard quantum limit. Science 306(5700), 1330–1336 (2004) Giovannetti et al. [2001] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced positioning and clock synchronization. Nature 412(6845), 417–419 (2001) Chehimi et al. [2023] Chehimi, M., Hashash, O., Saad, W.: The roadmap to a quantum-enabled wireless metaverse: Beyond the classical limits. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7–12 (2023). IEEE Chehimi and Saad [2021] Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Di Sipio, R., Huang, J.-H., Chen, S.Y.-C., Mangini, S., Worring, M.: The dawn of quantum natural language processing. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8612–8616 (2022). IEEE Giovannetti et al. [2004] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced measurements: beating the standard quantum limit. Science 306(5700), 1330–1336 (2004) Giovannetti et al. [2001] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced positioning and clock synchronization. Nature 412(6845), 417–419 (2001) Chehimi et al. [2023] Chehimi, M., Hashash, O., Saad, W.: The roadmap to a quantum-enabled wireless metaverse: Beyond the classical limits. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7–12 (2023). IEEE Chehimi and Saad [2021] Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced measurements: beating the standard quantum limit. Science 306(5700), 1330–1336 (2004) Giovannetti et al. [2001] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced positioning and clock synchronization. Nature 412(6845), 417–419 (2001) Chehimi et al. [2023] Chehimi, M., Hashash, O., Saad, W.: The roadmap to a quantum-enabled wireless metaverse: Beyond the classical limits. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7–12 (2023). IEEE Chehimi and Saad [2021] Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced positioning and clock synchronization. Nature 412(6845), 417–419 (2001) Chehimi et al. [2023] Chehimi, M., Hashash, O., Saad, W.: The roadmap to a quantum-enabled wireless metaverse: Beyond the classical limits. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7–12 (2023). IEEE Chehimi and Saad [2021] Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chehimi, M., Hashash, O., Saad, W.: The roadmap to a quantum-enabled wireless metaverse: Beyond the classical limits. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7–12 (2023). IEEE Chehimi and Saad [2021] Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. 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[2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. 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Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. 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[2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. 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[2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Cong, I., Choi, S., Lukin, M.D.: Quantum convolutional neural networks. Nature Physics 15(12), 1273–1278 (2019) Chen et al. [2022a] Chen, S.Y.-C., Wei, T.-C., Zhang, C., Yu, H., Yoo, S.: Quantum convolutional neural networks for high energy physics data analysis. Physical Review Research 4(1), 013231 (2022) Chen et al. [2022b] Chen, S.Y.-C., Fry, D., Deshmukh, A., Rastunkov, V., Stefanski, C.: Reservoir computing via quantum recurrent neural networks. arXiv preprint arXiv:2211.02612 (2022) Di Sipio et al. [2022] Di Sipio, R., Huang, J.-H., Chen, S.Y.-C., Mangini, S., Worring, M.: The dawn of quantum natural language processing. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8612–8616 (2022). IEEE Giovannetti et al. [2004] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced measurements: beating the standard quantum limit. Science 306(5700), 1330–1336 (2004) Giovannetti et al. [2001] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced positioning and clock synchronization. Nature 412(6845), 417–419 (2001) Chehimi et al. [2023] Chehimi, M., Hashash, O., Saad, W.: The roadmap to a quantum-enabled wireless metaverse: Beyond the classical limits. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7–12 (2023). IEEE Chehimi and Saad [2021] Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. 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[2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chen, S.Y.-C., Wei, T.-C., Zhang, C., Yu, H., Yoo, S.: Quantum convolutional neural networks for high energy physics data analysis. Physical Review Research 4(1), 013231 (2022) Chen et al. [2022b] Chen, S.Y.-C., Fry, D., Deshmukh, A., Rastunkov, V., Stefanski, C.: Reservoir computing via quantum recurrent neural networks. arXiv preprint arXiv:2211.02612 (2022) Di Sipio et al. [2022] Di Sipio, R., Huang, J.-H., Chen, S.Y.-C., Mangini, S., Worring, M.: The dawn of quantum natural language processing. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8612–8616 (2022). IEEE Giovannetti et al. [2004] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced measurements: beating the standard quantum limit. Science 306(5700), 1330–1336 (2004) Giovannetti et al. [2001] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced positioning and clock synchronization. Nature 412(6845), 417–419 (2001) Chehimi et al. [2023] Chehimi, M., Hashash, O., Saad, W.: The roadmap to a quantum-enabled wireless metaverse: Beyond the classical limits. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7–12 (2023). IEEE Chehimi and Saad [2021] Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chen, S.Y.-C., Fry, D., Deshmukh, A., Rastunkov, V., Stefanski, C.: Reservoir computing via quantum recurrent neural networks. arXiv preprint arXiv:2211.02612 (2022) Di Sipio et al. [2022] Di Sipio, R., Huang, J.-H., Chen, S.Y.-C., Mangini, S., Worring, M.: The dawn of quantum natural language processing. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8612–8616 (2022). IEEE Giovannetti et al. [2004] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced measurements: beating the standard quantum limit. Science 306(5700), 1330–1336 (2004) Giovannetti et al. [2001] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced positioning and clock synchronization. Nature 412(6845), 417–419 (2001) Chehimi et al. [2023] Chehimi, M., Hashash, O., Saad, W.: The roadmap to a quantum-enabled wireless metaverse: Beyond the classical limits. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7–12 (2023). IEEE Chehimi and Saad [2021] Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Di Sipio, R., Huang, J.-H., Chen, S.Y.-C., Mangini, S., Worring, M.: The dawn of quantum natural language processing. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8612–8616 (2022). IEEE Giovannetti et al. [2004] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced measurements: beating the standard quantum limit. Science 306(5700), 1330–1336 (2004) Giovannetti et al. [2001] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced positioning and clock synchronization. Nature 412(6845), 417–419 (2001) Chehimi et al. [2023] Chehimi, M., Hashash, O., Saad, W.: The roadmap to a quantum-enabled wireless metaverse: Beyond the classical limits. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7–12 (2023). IEEE Chehimi and Saad [2021] Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced measurements: beating the standard quantum limit. Science 306(5700), 1330–1336 (2004) Giovannetti et al. [2001] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced positioning and clock synchronization. Nature 412(6845), 417–419 (2001) Chehimi et al. [2023] Chehimi, M., Hashash, O., Saad, W.: The roadmap to a quantum-enabled wireless metaverse: Beyond the classical limits. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7–12 (2023). IEEE Chehimi and Saad [2021] Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced positioning and clock synchronization. Nature 412(6845), 417–419 (2001) Chehimi et al. [2023] Chehimi, M., Hashash, O., Saad, W.: The roadmap to a quantum-enabled wireless metaverse: Beyond the classical limits. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7–12 (2023). IEEE Chehimi and Saad [2021] Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chehimi, M., Hashash, O., Saad, W.: The roadmap to a quantum-enabled wireless metaverse: Beyond the classical limits. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7–12 (2023). IEEE Chehimi and Saad [2021] Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. 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[2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. 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[2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. 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[2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Cong, I., Choi, S., Lukin, M.D.: Quantum convolutional neural networks. Nature Physics 15(12), 1273–1278 (2019) Chen et al. [2022a] Chen, S.Y.-C., Wei, T.-C., Zhang, C., Yu, H., Yoo, S.: Quantum convolutional neural networks for high energy physics data analysis. Physical Review Research 4(1), 013231 (2022) Chen et al. [2022b] Chen, S.Y.-C., Fry, D., Deshmukh, A., Rastunkov, V., Stefanski, C.: Reservoir computing via quantum recurrent neural networks. arXiv preprint arXiv:2211.02612 (2022) Di Sipio et al. [2022] Di Sipio, R., Huang, J.-H., Chen, S.Y.-C., Mangini, S., Worring, M.: The dawn of quantum natural language processing. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8612–8616 (2022). IEEE Giovannetti et al. [2004] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced measurements: beating the standard quantum limit. Science 306(5700), 1330–1336 (2004) Giovannetti et al. [2001] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced positioning and clock synchronization. Nature 412(6845), 417–419 (2001) Chehimi et al. [2023] Chehimi, M., Hashash, O., Saad, W.: The roadmap to a quantum-enabled wireless metaverse: Beyond the classical limits. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7–12 (2023). IEEE Chehimi and Saad [2021] Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chen, S.Y.-C., Wei, T.-C., Zhang, C., Yu, H., Yoo, S.: Quantum convolutional neural networks for high energy physics data analysis. Physical Review Research 4(1), 013231 (2022) Chen et al. [2022b] Chen, S.Y.-C., Fry, D., Deshmukh, A., Rastunkov, V., Stefanski, C.: Reservoir computing via quantum recurrent neural networks. arXiv preprint arXiv:2211.02612 (2022) Di Sipio et al. [2022] Di Sipio, R., Huang, J.-H., Chen, S.Y.-C., Mangini, S., Worring, M.: The dawn of quantum natural language processing. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8612–8616 (2022). IEEE Giovannetti et al. [2004] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced measurements: beating the standard quantum limit. Science 306(5700), 1330–1336 (2004) Giovannetti et al. [2001] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced positioning and clock synchronization. Nature 412(6845), 417–419 (2001) Chehimi et al. [2023] Chehimi, M., Hashash, O., Saad, W.: The roadmap to a quantum-enabled wireless metaverse: Beyond the classical limits. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7–12 (2023). IEEE Chehimi and Saad [2021] Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chen, S.Y.-C., Fry, D., Deshmukh, A., Rastunkov, V., Stefanski, C.: Reservoir computing via quantum recurrent neural networks. arXiv preprint arXiv:2211.02612 (2022) Di Sipio et al. [2022] Di Sipio, R., Huang, J.-H., Chen, S.Y.-C., Mangini, S., Worring, M.: The dawn of quantum natural language processing. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8612–8616 (2022). IEEE Giovannetti et al. [2004] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced measurements: beating the standard quantum limit. Science 306(5700), 1330–1336 (2004) Giovannetti et al. [2001] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced positioning and clock synchronization. Nature 412(6845), 417–419 (2001) Chehimi et al. [2023] Chehimi, M., Hashash, O., Saad, W.: The roadmap to a quantum-enabled wireless metaverse: Beyond the classical limits. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7–12 (2023). IEEE Chehimi and Saad [2021] Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Di Sipio, R., Huang, J.-H., Chen, S.Y.-C., Mangini, S., Worring, M.: The dawn of quantum natural language processing. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8612–8616 (2022). IEEE Giovannetti et al. [2004] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced measurements: beating the standard quantum limit. Science 306(5700), 1330–1336 (2004) Giovannetti et al. [2001] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced positioning and clock synchronization. Nature 412(6845), 417–419 (2001) Chehimi et al. [2023] Chehimi, M., Hashash, O., Saad, W.: The roadmap to a quantum-enabled wireless metaverse: Beyond the classical limits. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7–12 (2023). IEEE Chehimi and Saad [2021] Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced measurements: beating the standard quantum limit. Science 306(5700), 1330–1336 (2004) Giovannetti et al. [2001] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced positioning and clock synchronization. Nature 412(6845), 417–419 (2001) Chehimi et al. [2023] Chehimi, M., Hashash, O., Saad, W.: The roadmap to a quantum-enabled wireless metaverse: Beyond the classical limits. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7–12 (2023). IEEE Chehimi and Saad [2021] Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced positioning and clock synchronization. Nature 412(6845), 417–419 (2001) Chehimi et al. [2023] Chehimi, M., Hashash, O., Saad, W.: The roadmap to a quantum-enabled wireless metaverse: Beyond the classical limits. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7–12 (2023). IEEE Chehimi and Saad [2021] Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chehimi, M., Hashash, O., Saad, W.: The roadmap to a quantum-enabled wireless metaverse: Beyond the classical limits. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7–12 (2023). IEEE Chehimi and Saad [2021] Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. 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[2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. 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[2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. 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[2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. 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[2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. 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[2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. 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[2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013)
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[2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chen, S.Y.-C., Wei, T.-C., Zhang, C., Yu, H., Yoo, S.: Quantum convolutional neural networks for high energy physics data analysis. Physical Review Research 4(1), 013231 (2022) Chen et al. [2022b] Chen, S.Y.-C., Fry, D., Deshmukh, A., Rastunkov, V., Stefanski, C.: Reservoir computing via quantum recurrent neural networks. arXiv preprint arXiv:2211.02612 (2022) Di Sipio et al. [2022] Di Sipio, R., Huang, J.-H., Chen, S.Y.-C., Mangini, S., Worring, M.: The dawn of quantum natural language processing. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8612–8616 (2022). IEEE Giovannetti et al. [2004] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced measurements: beating the standard quantum limit. Science 306(5700), 1330–1336 (2004) Giovannetti et al. [2001] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced positioning and clock synchronization. Nature 412(6845), 417–419 (2001) Chehimi et al. [2023] Chehimi, M., Hashash, O., Saad, W.: The roadmap to a quantum-enabled wireless metaverse: Beyond the classical limits. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7–12 (2023). IEEE Chehimi and Saad [2021] Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chen, S.Y.-C., Fry, D., Deshmukh, A., Rastunkov, V., Stefanski, C.: Reservoir computing via quantum recurrent neural networks. arXiv preprint arXiv:2211.02612 (2022) Di Sipio et al. [2022] Di Sipio, R., Huang, J.-H., Chen, S.Y.-C., Mangini, S., Worring, M.: The dawn of quantum natural language processing. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8612–8616 (2022). IEEE Giovannetti et al. [2004] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced measurements: beating the standard quantum limit. Science 306(5700), 1330–1336 (2004) Giovannetti et al. [2001] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced positioning and clock synchronization. Nature 412(6845), 417–419 (2001) Chehimi et al. [2023] Chehimi, M., Hashash, O., Saad, W.: The roadmap to a quantum-enabled wireless metaverse: Beyond the classical limits. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7–12 (2023). IEEE Chehimi and Saad [2021] Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Di Sipio, R., Huang, J.-H., Chen, S.Y.-C., Mangini, S., Worring, M.: The dawn of quantum natural language processing. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8612–8616 (2022). IEEE Giovannetti et al. [2004] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced measurements: beating the standard quantum limit. Science 306(5700), 1330–1336 (2004) Giovannetti et al. [2001] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced positioning and clock synchronization. Nature 412(6845), 417–419 (2001) Chehimi et al. [2023] Chehimi, M., Hashash, O., Saad, W.: The roadmap to a quantum-enabled wireless metaverse: Beyond the classical limits. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7–12 (2023). IEEE Chehimi and Saad [2021] Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced measurements: beating the standard quantum limit. Science 306(5700), 1330–1336 (2004) Giovannetti et al. [2001] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced positioning and clock synchronization. Nature 412(6845), 417–419 (2001) Chehimi et al. [2023] Chehimi, M., Hashash, O., Saad, W.: The roadmap to a quantum-enabled wireless metaverse: Beyond the classical limits. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7–12 (2023). IEEE Chehimi and Saad [2021] Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced positioning and clock synchronization. Nature 412(6845), 417–419 (2001) Chehimi et al. [2023] Chehimi, M., Hashash, O., Saad, W.: The roadmap to a quantum-enabled wireless metaverse: Beyond the classical limits. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7–12 (2023). IEEE Chehimi and Saad [2021] Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chehimi, M., Hashash, O., Saad, W.: The roadmap to a quantum-enabled wireless metaverse: Beyond the classical limits. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7–12 (2023). IEEE Chehimi and Saad [2021] Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. 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[2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? 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[2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. 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[2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. 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[2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. 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[2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. 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New Journal of Physics 15(1), 013041 (2013) McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. 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New Journal of Physics 15(1), 013041 (2013) Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013)
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[2023] Chehimi, M., Hashash, O., Saad, W.: The roadmap to a quantum-enabled wireless metaverse: Beyond the classical limits. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7–12 (2023). IEEE Chehimi and Saad [2021] Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chen, S.Y.-C., Fry, D., Deshmukh, A., Rastunkov, V., Stefanski, C.: Reservoir computing via quantum recurrent neural networks. arXiv preprint arXiv:2211.02612 (2022) Di Sipio et al. [2022] Di Sipio, R., Huang, J.-H., Chen, S.Y.-C., Mangini, S., Worring, M.: The dawn of quantum natural language processing. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8612–8616 (2022). IEEE Giovannetti et al. [2004] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced measurements: beating the standard quantum limit. Science 306(5700), 1330–1336 (2004) Giovannetti et al. [2001] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced positioning and clock synchronization. Nature 412(6845), 417–419 (2001) Chehimi et al. [2023] Chehimi, M., Hashash, O., Saad, W.: The roadmap to a quantum-enabled wireless metaverse: Beyond the classical limits. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7–12 (2023). IEEE Chehimi and Saad [2021] Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Di Sipio, R., Huang, J.-H., Chen, S.Y.-C., Mangini, S., Worring, M.: The dawn of quantum natural language processing. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8612–8616 (2022). IEEE Giovannetti et al. [2004] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced measurements: beating the standard quantum limit. Science 306(5700), 1330–1336 (2004) Giovannetti et al. [2001] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced positioning and clock synchronization. Nature 412(6845), 417–419 (2001) Chehimi et al. [2023] Chehimi, M., Hashash, O., Saad, W.: The roadmap to a quantum-enabled wireless metaverse: Beyond the classical limits. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7–12 (2023). IEEE Chehimi and Saad [2021] Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced measurements: beating the standard quantum limit. Science 306(5700), 1330–1336 (2004) Giovannetti et al. [2001] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced positioning and clock synchronization. Nature 412(6845), 417–419 (2001) Chehimi et al. [2023] Chehimi, M., Hashash, O., Saad, W.: The roadmap to a quantum-enabled wireless metaverse: Beyond the classical limits. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7–12 (2023). IEEE Chehimi and Saad [2021] Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced positioning and clock synchronization. Nature 412(6845), 417–419 (2001) Chehimi et al. [2023] Chehimi, M., Hashash, O., Saad, W.: The roadmap to a quantum-enabled wireless metaverse: Beyond the classical limits. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7–12 (2023). IEEE Chehimi and Saad [2021] Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chehimi, M., Hashash, O., Saad, W.: The roadmap to a quantum-enabled wireless metaverse: Beyond the classical limits. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7–12 (2023). IEEE Chehimi and Saad [2021] Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. 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[2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Di Sipio, R., Huang, J.-H., Chen, S.Y.-C., Mangini, S., Worring, M.: The dawn of quantum natural language processing. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8612–8616 (2022). IEEE Giovannetti et al. [2004] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced measurements: beating the standard quantum limit. Science 306(5700), 1330–1336 (2004) Giovannetti et al. [2001] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced positioning and clock synchronization. Nature 412(6845), 417–419 (2001) Chehimi et al. [2023] Chehimi, M., Hashash, O., Saad, W.: The roadmap to a quantum-enabled wireless metaverse: Beyond the classical limits. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7–12 (2023). IEEE Chehimi and Saad [2021] Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced measurements: beating the standard quantum limit. Science 306(5700), 1330–1336 (2004) Giovannetti et al. [2001] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced positioning and clock synchronization. Nature 412(6845), 417–419 (2001) Chehimi et al. [2023] Chehimi, M., Hashash, O., Saad, W.: The roadmap to a quantum-enabled wireless metaverse: Beyond the classical limits. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7–12 (2023). IEEE Chehimi and Saad [2021] Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced positioning and clock synchronization. Nature 412(6845), 417–419 (2001) Chehimi et al. [2023] Chehimi, M., Hashash, O., Saad, W.: The roadmap to a quantum-enabled wireless metaverse: Beyond the classical limits. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7–12 (2023). IEEE Chehimi and Saad [2021] Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chehimi, M., Hashash, O., Saad, W.: The roadmap to a quantum-enabled wireless metaverse: Beyond the classical limits. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7–12 (2023). IEEE Chehimi and Saad [2021] Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. 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[2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? 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[2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. 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[2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. 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[2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. 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[2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. 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New Journal of Physics 15(1), 013041 (2013) McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. 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[2001] Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced positioning and clock synchronization. Nature 412(6845), 417–419 (2001) Chehimi et al. [2023] Chehimi, M., Hashash, O., Saad, W.: The roadmap to a quantum-enabled wireless metaverse: Beyond the classical limits. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7–12 (2023). IEEE Chehimi and Saad [2021] Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. 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[2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced positioning and clock synchronization. Nature 412(6845), 417–419 (2001) Chehimi et al. [2023] Chehimi, M., Hashash, O., Saad, W.: The roadmap to a quantum-enabled wireless metaverse: Beyond the classical limits. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7–12 (2023). IEEE Chehimi and Saad [2021] Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chehimi, M., Hashash, O., Saad, W.: The roadmap to a quantum-enabled wireless metaverse: Beyond the classical limits. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7–12 (2023). IEEE Chehimi and Saad [2021] Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. 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[2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? 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[2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. 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[2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. 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[2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. 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[2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. 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New Journal of Physics 15(1), 013041 (2013) McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. 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New Journal of Physics 15(1), 013041 (2013) Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013)
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[2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. 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[2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Giovannetti, V., Lloyd, S., Maccone, L.: Quantum-enhanced positioning and clock synchronization. Nature 412(6845), 417–419 (2001) Chehimi et al. [2023] Chehimi, M., Hashash, O., Saad, W.: The roadmap to a quantum-enabled wireless metaverse: Beyond the classical limits. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7–12 (2023). IEEE Chehimi and Saad [2021] Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. 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[2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chehimi, M., Hashash, O., Saad, W.: The roadmap to a quantum-enabled wireless metaverse: Beyond the classical limits. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7–12 (2023). IEEE Chehimi and Saad [2021] Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. 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[2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chehimi, M., Hashash, O., Saad, W.: The roadmap to a quantum-enabled wireless metaverse: Beyond the classical limits. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7–12 (2023). IEEE Chehimi and Saad [2021] Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. 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[2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. 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[2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. 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New Journal of Physics 15(1), 013041 (2013) Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. 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[2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. 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New Journal of Physics 15(1), 013041 (2013) McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. 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[2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. 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New Journal of Physics 15(1), 013041 (2013) Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013)
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[2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. 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[2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. 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Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chehimi, M., Saad, W.: Entanglement rate optimization in heterogeneous quantum communication networks. In: 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6 (2021). IEEE Kairouz et al. [2021] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. 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[2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. 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IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. 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[2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. 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[2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. 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New Journal of Physics 15(1), 013041 (2013) McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. 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New Journal of Physics 15(1), 013041 (2013) Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013)
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IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. 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[2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1–2), 1–210 (2021) Chehimi et al. [2023] Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. 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[2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? 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New Journal of Physics 15(1), 013041 (2013) Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013)
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In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. 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[2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? 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[2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. 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[2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. 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[2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. 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Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013)
- Chehimi, M., Chen, S.Y.-C., Saad, W., Towsley, D., Debbah, M.: Foundations of quantum federated learning over classical and quantum networks. IEEE Network (2023) Chen and Yoo [2021] Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. 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[2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chen, S.Y.-C., Yoo, S.: Federated quantum machine learning. Entropy 23(4), 460 (2021) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chehimi, M., Saad, W.: Quantum federated learning with quantum data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621 (2022). IEEE Li et al. [2023] Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. 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[2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. 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[2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. 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[2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. 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[2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. 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[2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. 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[2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. 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Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013)
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Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. 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[2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. 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(2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. 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[2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. 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Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. 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[2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013)
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[2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Li, S.S., Zhang, X., Zhou, S., Shu, H., Liang, R., Liu, H., Garcia, L.P.: Pqlm-multilingual decentralized portable quantum language model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE Huang et al. [2022] Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. 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[2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? 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[2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. 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Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. 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[2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. 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[2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Huang, R., Tan, X., Xu, Q.: Quantum federated learning with decentralized data. IEEE Journal of Selected Topics in Quantum Electronics 28(4), 1–10 (2022) Yun et al. [2022] Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. 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[2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? 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[2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. 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[2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? 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[2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. 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[2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. 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Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. 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[2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. 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New Journal of Physics 15(1), 013041 (2013) Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013)
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[2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Yun, W.J., Kim, J.P., Jung, S., Park, J., Bennis, M., Kim, J.: Slimmable quantum federated learning. arXiv preprint arXiv:2207.10221 (2022) Rofougaran et al. [2023] Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Rofougaran, R., Yoo, S., Tseng, H.-H., Chen, S.Y.-C.: Federated quantum machine learning with differential privacy. arXiv preprint arXiv:2310.06973 (2023) Cao et al. [2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. 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[2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. 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[2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. 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Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. 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[2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. 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Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. 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New Journal of Physics 15(1), 013041 (2013) Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013)
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[2023] Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. 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[2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. 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[2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. 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[2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. 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[2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. 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IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. 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[2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013)
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[2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. 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Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. 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[2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. 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[2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013)
- Cao, Y., Zhou, X., Fei, X., Zhao, H., Liu, W., Zhao, J.: Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Machine Intelligence 5(2), 1–12 (2023) Garg et al. [2019] Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Garg, D., Ikbal, S., Srivastava, S.K., Vishwakarma, H., Karanam, H., Subramaniam, L.V.: Quantum embedding of knowledge for reasoning. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 5594–5604 (2019) Mottonen et al. [2004] Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph/0407010 (2004) Chen et al. [2020] Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chen, S.Y.-C., Yang, C.-H.H., Qi, J., Chen, P.-Y., Ma, X., Goan, H.-S.: Variational quantum circuits for deep reinforcement learning. IEEE Access 8, 141007–141024 (2020) Sim et al. [2019] Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. 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[2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. 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[2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. 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Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? 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In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. 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[2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. 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In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. 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[2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies 2(12), 1900070 (2019) Lanting et al. [2014] Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Lanting, T., Przybysz, A.J., Smirnov, A.Y., Spedalieri, F.M., Amin, M.H., Berkley, A.J., Harris, R., Altomare, F., Boixo, S., Bunyk, P., et al.: Entanglement in a quantum annealing processor. Physical Review X 4(2), 021041 (2014) Du et al. [2018] Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: The expressive power of parameterized quantum circuits. arXiv preprint arXiv:1810.11922 (2018) Abbas et al. [2021] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. 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[2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? 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New Journal of Physics 15(1), 013041 (2013) Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. 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Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. 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[2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. 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[2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. 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IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. 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New Journal of Physics 15(1), 013041 (2013) McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. 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Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. 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[2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S.: The power of quantum neural networks. Nature Computational Science 1(6), 403–409 (2021) Caro et al. [2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. 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[2022] Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. 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(2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Caro, M.C., Huang, H.-Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., Coles, P.J.: Generalization in quantum machine learning from few training data. Nature communications 13(1), 1–11 (2022) Chen et al. [2021] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A.V., Poor, H.V.: Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. 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IEEE Journal on Selected Areas in Communications 39(12), 3579–3605 (2021) Ren et al. [2023] Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. 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[2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Ren, C., Yu, H., Yan, R., Xu, M., Shen, Y., Zhu, H., Niyato, D., Dong, Z.Y., Kwek, L.C.: Towards quantum federated learning. arXiv preprint arXiv:2306.09912 (2023) Chehimi and Saad [2022] Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. 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[2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. [2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. 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[2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? 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[2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. 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[2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chehimi, M., Saad, W.: Physics-informed quantum communication networks: A vision towards the quantum internet. IEEE network, 134–142 (2022) Chehimi et al. 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Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. 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[2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. 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New Journal of Physics 15(1), 013041 (2013) Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013)
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[2023a] Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chehimi, M., Pouryousef, S., Panigrahy, N., Towsley, D., Saad, W.: Scaling limits of quantum repeater networks. In: Proc. of IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA USA (2023) Chehimi et al. [2023b] Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. 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[2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. 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New Journal of Physics 15(1), 013041 (2013) Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013)
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New Journal of Physics 15(1), 013041 (2013) Chehimi, M., Simon, B., Saad, W., Klein, A., Towsley, D., Debbah, M.: Matching game for optimized association in quantum communication networks. In: Proc. of IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia (2023) McMahan et al. [2017] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017). PMLR Pelayo et al. [2023] Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013)
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New Journal of Physics 15(1), 013041 (2013) Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013)
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Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013)
- Pelayo, J.C., Gietka, K., Busch, T.: Distributed quantum sensing with optical lattices. Physical Review A 107(3), 033318 (2023) Fang et al. [2018] Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013)
- Fang, Y.-L.L., Ciccarello, F., Baranger, H.U.: Non-markovian dynamics of a qubit due to single-photon scattering in a waveguide. New Journal of Physics 20(4), 043035 (2018) Calajó et al. [2019] Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Calajó, G., Fang, Y.-L.L., Baranger, H.U., Ciccarello, F., et al.: Exciting a bound state in the continuum through multiphoton scattering plus delayed quantum feedback. Physical review letters 122(7), 073601 (2019) Tufarelli et al. [2013] Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013)
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- Tufarelli, T., Ciccarello, F., Kim, M.: Dynamics of spontaneous emission in a single-end photonic waveguide. Physical Review A 87(1), 013820 (2013) Pistolesi et al. [2021] Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013)
- Pistolesi, F., Cleland, A., Bachtold, A.: Proposal for a nanomechanical qubit. Physical Review X 11(3), 031027 (2021) Pedersen [2003] Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013)
- Pedersen, T.G.: Variational approach to excitons in carbon nanotubes. Physical Review B 67(7), 073401 (2003) Shao and Hänggi [1998] Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Shao, J., Hänggi, P.: Decoherent dynamics of a two-level system coupled to a sea of spins. Physical review letters 81(26), 5710 (1998) Olver et al. [2010] Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Olver, F.W., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions Hardback and CD-ROM. Cambridge university press, ??? (2010) Maclaurin et al. [2013] Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013) Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013)
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- Maclaurin, D., Hall, L., Martin, A., Hollenberg, L.: Nanoscale magnetometry through quantum control of nitrogen–vacancy centres in rotationally diffusing nanodiamonds. New Journal of Physics 15(1), 013041 (2013)