The cross-sectional stock return predictions via quantum neural network and tensor network (2304.12501v2)
Abstract: In this paper, we investigate the application of quantum and quantum-inspired machine learning algorithms to stock return predictions. Specifically, we evaluate the performance of quantum neural network, an algorithm suited for noisy intermediate-scale quantum computers, and tensor network, a quantum-inspired machine learning algorithm, against classical models such as linear regression and neural networks. To evaluate their abilities, we construct portfolios based on their predictions and measure investment performances. The empirical study on the Japanese stock market shows the tensor network model achieves superior performance compared to classical benchmark models, including linear and neural network models. Though the quantum neural network model attains a lowered risk-adjusted excess return than the classical neural network models over the whole period, both the quantum neural network and tensor network models have superior performances in the latest market environment, which suggests the capability of the model's capturing non-linearity between input features.
- Abe M, Nakayama H (2018) Deep learning for forecasting stock returns in the cross-section. In: Pacific-Asia conference on knowledge discovery and data mining, Springer, pp 273–284 Arute et al [2019] Arute F, Arya K, Babbush R, et al (2019) Quantum supremacy using a programmable superconducting processor. Nature 574(7779):505–510 Bao et al [2017] Bao W, Yue J, Rao Y (2017) A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7):e0180944 Bausch [2020] Bausch J (2020) Recurrent quantum neural networks. Advances in neural information processing systems 33:1368–1379 Cerezo et al [2021] Cerezo M, Arrasmith A, Babbush R, et al (2021) Variational quantum algorithms. Nature Reviews Physics 3(9):625–644 Chinco et al [2019] Chinco A, Clark-Joseph AD, Ye M (2019) Sparse signals in the cross-section of returns. The Journal of Finance 74(1):449–492 Dixon and Polson [2020] Dixon M, Polson N (2020) Deep fundamental factor models. SIAM Journal on Financial Mathematics 11(3):SC26–SC37 Duan and Kashima [2021] Duan J, Kashima H (2021) Learning to rank for multi-step ahead time-series forecasting. IEEE Access 9:49372–49386 Efthymiou et al [2019] Efthymiou S, Hidary J, Leichenauer S (2019) Tensornetwork for machine learning. arXiv preprint arXiv:190606329 Eugene and French [1992] Eugene F, French K (1992) The cross-section of expected stock returns. Journal of Finance 47(2):427–465 Fannes et al [1992] Fannes M, Nachtergaele B, Werner RF (1992) Finitely correlated states on quantum spin chains. Communications in mathematical physics 144(3):443–490 Gu et al [2020] Gu S, Kelly B, Xiu D (2020) Empirical Asset Pricing via Machine Learning. The Review of Financial Studies 33(5):2223–2273. 10.1093/rfs/hhaa009, URL https://doi.org/10.1093/rfs/hhaa009, https://academic.oup.com/rfs/article-pdf/33/5/2223/33209812/hhaa009.pdf Gu et al [2021] Gu S, Kelly B, Xiu D (2021) Autoencoder asset pricing models. Journal of Econometrics 222(1):429–450 Huggins et al [2019] Huggins W, Patil P, Mitchell B, et al (2019) Towards quantum machine learning with tensor networks. Quantum Science and technology 4(2):024001 Kim [2019] Kim S (2019) Enhancing the momentum strategy through deep regression. Quantitative Finance 19(7):1121–1133 Lim et al [2019] Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Arute F, Arya K, Babbush R, et al (2019) Quantum supremacy using a programmable superconducting processor. Nature 574(7779):505–510 Bao et al [2017] Bao W, Yue J, Rao Y (2017) A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7):e0180944 Bausch [2020] Bausch J (2020) Recurrent quantum neural networks. Advances in neural information processing systems 33:1368–1379 Cerezo et al [2021] Cerezo M, Arrasmith A, Babbush R, et al (2021) Variational quantum algorithms. Nature Reviews Physics 3(9):625–644 Chinco et al [2019] Chinco A, Clark-Joseph AD, Ye M (2019) Sparse signals in the cross-section of returns. The Journal of Finance 74(1):449–492 Dixon and Polson [2020] Dixon M, Polson N (2020) Deep fundamental factor models. SIAM Journal on Financial Mathematics 11(3):SC26–SC37 Duan and Kashima [2021] Duan J, Kashima H (2021) Learning to rank for multi-step ahead time-series forecasting. IEEE Access 9:49372–49386 Efthymiou et al [2019] Efthymiou S, Hidary J, Leichenauer S (2019) Tensornetwork for machine learning. arXiv preprint arXiv:190606329 Eugene and French [1992] Eugene F, French K (1992) The cross-section of expected stock returns. Journal of Finance 47(2):427–465 Fannes et al [1992] Fannes M, Nachtergaele B, Werner RF (1992) Finitely correlated states on quantum spin chains. Communications in mathematical physics 144(3):443–490 Gu et al [2020] Gu S, Kelly B, Xiu D (2020) Empirical Asset Pricing via Machine Learning. The Review of Financial Studies 33(5):2223–2273. 10.1093/rfs/hhaa009, URL https://doi.org/10.1093/rfs/hhaa009, https://academic.oup.com/rfs/article-pdf/33/5/2223/33209812/hhaa009.pdf Gu et al [2021] Gu S, Kelly B, Xiu D (2021) Autoencoder asset pricing models. Journal of Econometrics 222(1):429–450 Huggins et al [2019] Huggins W, Patil P, Mitchell B, et al (2019) Towards quantum machine learning with tensor networks. Quantum Science and technology 4(2):024001 Kim [2019] Kim S (2019) Enhancing the momentum strategy through deep regression. Quantitative Finance 19(7):1121–1133 Lim et al [2019] Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Bao W, Yue J, Rao Y (2017) A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7):e0180944 Bausch [2020] Bausch J (2020) Recurrent quantum neural networks. Advances in neural information processing systems 33:1368–1379 Cerezo et al [2021] Cerezo M, Arrasmith A, Babbush R, et al (2021) Variational quantum algorithms. Nature Reviews Physics 3(9):625–644 Chinco et al [2019] Chinco A, Clark-Joseph AD, Ye M (2019) Sparse signals in the cross-section of returns. The Journal of Finance 74(1):449–492 Dixon and Polson [2020] Dixon M, Polson N (2020) Deep fundamental factor models. SIAM Journal on Financial Mathematics 11(3):SC26–SC37 Duan and Kashima [2021] Duan J, Kashima H (2021) Learning to rank for multi-step ahead time-series forecasting. IEEE Access 9:49372–49386 Efthymiou et al [2019] Efthymiou S, Hidary J, Leichenauer S (2019) Tensornetwork for machine learning. arXiv preprint arXiv:190606329 Eugene and French [1992] Eugene F, French K (1992) The cross-section of expected stock returns. Journal of Finance 47(2):427–465 Fannes et al [1992] Fannes M, Nachtergaele B, Werner RF (1992) Finitely correlated states on quantum spin chains. Communications in mathematical physics 144(3):443–490 Gu et al [2020] Gu S, Kelly B, Xiu D (2020) Empirical Asset Pricing via Machine Learning. The Review of Financial Studies 33(5):2223–2273. 10.1093/rfs/hhaa009, URL https://doi.org/10.1093/rfs/hhaa009, https://academic.oup.com/rfs/article-pdf/33/5/2223/33209812/hhaa009.pdf Gu et al [2021] Gu S, Kelly B, Xiu D (2021) Autoencoder asset pricing models. Journal of Econometrics 222(1):429–450 Huggins et al [2019] Huggins W, Patil P, Mitchell B, et al (2019) Towards quantum machine learning with tensor networks. Quantum Science and technology 4(2):024001 Kim [2019] Kim S (2019) Enhancing the momentum strategy through deep regression. Quantitative Finance 19(7):1121–1133 Lim et al [2019] Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Bausch J (2020) Recurrent quantum neural networks. Advances in neural information processing systems 33:1368–1379 Cerezo et al [2021] Cerezo M, Arrasmith A, Babbush R, et al (2021) Variational quantum algorithms. Nature Reviews Physics 3(9):625–644 Chinco et al [2019] Chinco A, Clark-Joseph AD, Ye M (2019) Sparse signals in the cross-section of returns. The Journal of Finance 74(1):449–492 Dixon and Polson [2020] Dixon M, Polson N (2020) Deep fundamental factor models. SIAM Journal on Financial Mathematics 11(3):SC26–SC37 Duan and Kashima [2021] Duan J, Kashima H (2021) Learning to rank for multi-step ahead time-series forecasting. IEEE Access 9:49372–49386 Efthymiou et al [2019] Efthymiou S, Hidary J, Leichenauer S (2019) Tensornetwork for machine learning. arXiv preprint arXiv:190606329 Eugene and French [1992] Eugene F, French K (1992) The cross-section of expected stock returns. Journal of Finance 47(2):427–465 Fannes et al [1992] Fannes M, Nachtergaele B, Werner RF (1992) Finitely correlated states on quantum spin chains. Communications in mathematical physics 144(3):443–490 Gu et al [2020] Gu S, Kelly B, Xiu D (2020) Empirical Asset Pricing via Machine Learning. The Review of Financial Studies 33(5):2223–2273. 10.1093/rfs/hhaa009, URL https://doi.org/10.1093/rfs/hhaa009, https://academic.oup.com/rfs/article-pdf/33/5/2223/33209812/hhaa009.pdf Gu et al [2021] Gu S, Kelly B, Xiu D (2021) Autoencoder asset pricing models. Journal of Econometrics 222(1):429–450 Huggins et al [2019] Huggins W, Patil P, Mitchell B, et al (2019) Towards quantum machine learning with tensor networks. Quantum Science and technology 4(2):024001 Kim [2019] Kim S (2019) Enhancing the momentum strategy through deep regression. Quantitative Finance 19(7):1121–1133 Lim et al [2019] Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Cerezo M, Arrasmith A, Babbush R, et al (2021) Variational quantum algorithms. Nature Reviews Physics 3(9):625–644 Chinco et al [2019] Chinco A, Clark-Joseph AD, Ye M (2019) Sparse signals in the cross-section of returns. The Journal of Finance 74(1):449–492 Dixon and Polson [2020] Dixon M, Polson N (2020) Deep fundamental factor models. SIAM Journal on Financial Mathematics 11(3):SC26–SC37 Duan and Kashima [2021] Duan J, Kashima H (2021) Learning to rank for multi-step ahead time-series forecasting. IEEE Access 9:49372–49386 Efthymiou et al [2019] Efthymiou S, Hidary J, Leichenauer S (2019) Tensornetwork for machine learning. arXiv preprint arXiv:190606329 Eugene and French [1992] Eugene F, French K (1992) The cross-section of expected stock returns. Journal of Finance 47(2):427–465 Fannes et al [1992] Fannes M, Nachtergaele B, Werner RF (1992) Finitely correlated states on quantum spin chains. Communications in mathematical physics 144(3):443–490 Gu et al [2020] Gu S, Kelly B, Xiu D (2020) Empirical Asset Pricing via Machine Learning. The Review of Financial Studies 33(5):2223–2273. 10.1093/rfs/hhaa009, URL https://doi.org/10.1093/rfs/hhaa009, https://academic.oup.com/rfs/article-pdf/33/5/2223/33209812/hhaa009.pdf Gu et al [2021] Gu S, Kelly B, Xiu D (2021) Autoencoder asset pricing models. Journal of Econometrics 222(1):429–450 Huggins et al [2019] Huggins W, Patil P, Mitchell B, et al (2019) Towards quantum machine learning with tensor networks. Quantum Science and technology 4(2):024001 Kim [2019] Kim S (2019) Enhancing the momentum strategy through deep regression. Quantitative Finance 19(7):1121–1133 Lim et al [2019] Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Chinco A, Clark-Joseph AD, Ye M (2019) Sparse signals in the cross-section of returns. The Journal of Finance 74(1):449–492 Dixon and Polson [2020] Dixon M, Polson N (2020) Deep fundamental factor models. SIAM Journal on Financial Mathematics 11(3):SC26–SC37 Duan and Kashima [2021] Duan J, Kashima H (2021) Learning to rank for multi-step ahead time-series forecasting. IEEE Access 9:49372–49386 Efthymiou et al [2019] Efthymiou S, Hidary J, Leichenauer S (2019) Tensornetwork for machine learning. arXiv preprint arXiv:190606329 Eugene and French [1992] Eugene F, French K (1992) The cross-section of expected stock returns. Journal of Finance 47(2):427–465 Fannes et al [1992] Fannes M, Nachtergaele B, Werner RF (1992) Finitely correlated states on quantum spin chains. Communications in mathematical physics 144(3):443–490 Gu et al [2020] Gu S, Kelly B, Xiu D (2020) Empirical Asset Pricing via Machine Learning. The Review of Financial Studies 33(5):2223–2273. 10.1093/rfs/hhaa009, URL https://doi.org/10.1093/rfs/hhaa009, https://academic.oup.com/rfs/article-pdf/33/5/2223/33209812/hhaa009.pdf Gu et al [2021] Gu S, Kelly B, Xiu D (2021) Autoencoder asset pricing models. Journal of Econometrics 222(1):429–450 Huggins et al [2019] Huggins W, Patil P, Mitchell B, et al (2019) Towards quantum machine learning with tensor networks. Quantum Science and technology 4(2):024001 Kim [2019] Kim S (2019) Enhancing the momentum strategy through deep regression. Quantitative Finance 19(7):1121–1133 Lim et al [2019] Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Dixon M, Polson N (2020) Deep fundamental factor models. SIAM Journal on Financial Mathematics 11(3):SC26–SC37 Duan and Kashima [2021] Duan J, Kashima H (2021) Learning to rank for multi-step ahead time-series forecasting. IEEE Access 9:49372–49386 Efthymiou et al [2019] Efthymiou S, Hidary J, Leichenauer S (2019) Tensornetwork for machine learning. arXiv preprint arXiv:190606329 Eugene and French [1992] Eugene F, French K (1992) The cross-section of expected stock returns. Journal of Finance 47(2):427–465 Fannes et al [1992] Fannes M, Nachtergaele B, Werner RF (1992) Finitely correlated states on quantum spin chains. Communications in mathematical physics 144(3):443–490 Gu et al [2020] Gu S, Kelly B, Xiu D (2020) Empirical Asset Pricing via Machine Learning. The Review of Financial Studies 33(5):2223–2273. 10.1093/rfs/hhaa009, URL https://doi.org/10.1093/rfs/hhaa009, https://academic.oup.com/rfs/article-pdf/33/5/2223/33209812/hhaa009.pdf Gu et al [2021] Gu S, Kelly B, Xiu D (2021) Autoencoder asset pricing models. Journal of Econometrics 222(1):429–450 Huggins et al [2019] Huggins W, Patil P, Mitchell B, et al (2019) Towards quantum machine learning with tensor networks. Quantum Science and technology 4(2):024001 Kim [2019] Kim S (2019) Enhancing the momentum strategy through deep regression. Quantitative Finance 19(7):1121–1133 Lim et al [2019] Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Duan J, Kashima H (2021) Learning to rank for multi-step ahead time-series forecasting. IEEE Access 9:49372–49386 Efthymiou et al [2019] Efthymiou S, Hidary J, Leichenauer S (2019) Tensornetwork for machine learning. arXiv preprint arXiv:190606329 Eugene and French [1992] Eugene F, French K (1992) The cross-section of expected stock returns. Journal of Finance 47(2):427–465 Fannes et al [1992] Fannes M, Nachtergaele B, Werner RF (1992) Finitely correlated states on quantum spin chains. Communications in mathematical physics 144(3):443–490 Gu et al [2020] Gu S, Kelly B, Xiu D (2020) Empirical Asset Pricing via Machine Learning. The Review of Financial Studies 33(5):2223–2273. 10.1093/rfs/hhaa009, URL https://doi.org/10.1093/rfs/hhaa009, https://academic.oup.com/rfs/article-pdf/33/5/2223/33209812/hhaa009.pdf Gu et al [2021] Gu S, Kelly B, Xiu D (2021) Autoencoder asset pricing models. Journal of Econometrics 222(1):429–450 Huggins et al [2019] Huggins W, Patil P, Mitchell B, et al (2019) Towards quantum machine learning with tensor networks. Quantum Science and technology 4(2):024001 Kim [2019] Kim S (2019) Enhancing the momentum strategy through deep regression. Quantitative Finance 19(7):1121–1133 Lim et al [2019] Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Efthymiou S, Hidary J, Leichenauer S (2019) Tensornetwork for machine learning. arXiv preprint arXiv:190606329 Eugene and French [1992] Eugene F, French K (1992) The cross-section of expected stock returns. Journal of Finance 47(2):427–465 Fannes et al [1992] Fannes M, Nachtergaele B, Werner RF (1992) Finitely correlated states on quantum spin chains. Communications in mathematical physics 144(3):443–490 Gu et al [2020] Gu S, Kelly B, Xiu D (2020) Empirical Asset Pricing via Machine Learning. The Review of Financial Studies 33(5):2223–2273. 10.1093/rfs/hhaa009, URL https://doi.org/10.1093/rfs/hhaa009, https://academic.oup.com/rfs/article-pdf/33/5/2223/33209812/hhaa009.pdf Gu et al [2021] Gu S, Kelly B, Xiu D (2021) Autoencoder asset pricing models. Journal of Econometrics 222(1):429–450 Huggins et al [2019] Huggins W, Patil P, Mitchell B, et al (2019) Towards quantum machine learning with tensor networks. Quantum Science and technology 4(2):024001 Kim [2019] Kim S (2019) Enhancing the momentum strategy through deep regression. Quantitative Finance 19(7):1121–1133 Lim et al [2019] Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Eugene F, French K (1992) The cross-section of expected stock returns. Journal of Finance 47(2):427–465 Fannes et al [1992] Fannes M, Nachtergaele B, Werner RF (1992) Finitely correlated states on quantum spin chains. Communications in mathematical physics 144(3):443–490 Gu et al [2020] Gu S, Kelly B, Xiu D (2020) Empirical Asset Pricing via Machine Learning. The Review of Financial Studies 33(5):2223–2273. 10.1093/rfs/hhaa009, URL https://doi.org/10.1093/rfs/hhaa009, https://academic.oup.com/rfs/article-pdf/33/5/2223/33209812/hhaa009.pdf Gu et al [2021] Gu S, Kelly B, Xiu D (2021) Autoencoder asset pricing models. Journal of Econometrics 222(1):429–450 Huggins et al [2019] Huggins W, Patil P, Mitchell B, et al (2019) Towards quantum machine learning with tensor networks. Quantum Science and technology 4(2):024001 Kim [2019] Kim S (2019) Enhancing the momentum strategy through deep regression. Quantitative Finance 19(7):1121–1133 Lim et al [2019] Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Fannes M, Nachtergaele B, Werner RF (1992) Finitely correlated states on quantum spin chains. Communications in mathematical physics 144(3):443–490 Gu et al [2020] Gu S, Kelly B, Xiu D (2020) Empirical Asset Pricing via Machine Learning. The Review of Financial Studies 33(5):2223–2273. 10.1093/rfs/hhaa009, URL https://doi.org/10.1093/rfs/hhaa009, https://academic.oup.com/rfs/article-pdf/33/5/2223/33209812/hhaa009.pdf Gu et al [2021] Gu S, Kelly B, Xiu D (2021) Autoencoder asset pricing models. Journal of Econometrics 222(1):429–450 Huggins et al [2019] Huggins W, Patil P, Mitchell B, et al (2019) Towards quantum machine learning with tensor networks. Quantum Science and technology 4(2):024001 Kim [2019] Kim S (2019) Enhancing the momentum strategy through deep regression. Quantitative Finance 19(7):1121–1133 Lim et al [2019] Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Gu S, Kelly B, Xiu D (2020) Empirical Asset Pricing via Machine Learning. The Review of Financial Studies 33(5):2223–2273. 10.1093/rfs/hhaa009, URL https://doi.org/10.1093/rfs/hhaa009, https://academic.oup.com/rfs/article-pdf/33/5/2223/33209812/hhaa009.pdf Gu et al [2021] Gu S, Kelly B, Xiu D (2021) Autoencoder asset pricing models. Journal of Econometrics 222(1):429–450 Huggins et al [2019] Huggins W, Patil P, Mitchell B, et al (2019) Towards quantum machine learning with tensor networks. Quantum Science and technology 4(2):024001 Kim [2019] Kim S (2019) Enhancing the momentum strategy through deep regression. Quantitative Finance 19(7):1121–1133 Lim et al [2019] Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Gu S, Kelly B, Xiu D (2021) Autoencoder asset pricing models. Journal of Econometrics 222(1):429–450 Huggins et al [2019] Huggins W, Patil P, Mitchell B, et al (2019) Towards quantum machine learning with tensor networks. Quantum Science and technology 4(2):024001 Kim [2019] Kim S (2019) Enhancing the momentum strategy through deep regression. Quantitative Finance 19(7):1121–1133 Lim et al [2019] Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Huggins W, Patil P, Mitchell B, et al (2019) Towards quantum machine learning with tensor networks. Quantum Science and technology 4(2):024001 Kim [2019] Kim S (2019) Enhancing the momentum strategy through deep regression. Quantitative Finance 19(7):1121–1133 Lim et al [2019] Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Kim S (2019) Enhancing the momentum strategy through deep regression. Quantitative Finance 19(7):1121–1133 Lim et al [2019] Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863
- Arute F, Arya K, Babbush R, et al (2019) Quantum supremacy using a programmable superconducting processor. Nature 574(7779):505–510 Bao et al [2017] Bao W, Yue J, Rao Y (2017) A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7):e0180944 Bausch [2020] Bausch J (2020) Recurrent quantum neural networks. Advances in neural information processing systems 33:1368–1379 Cerezo et al [2021] Cerezo M, Arrasmith A, Babbush R, et al (2021) Variational quantum algorithms. Nature Reviews Physics 3(9):625–644 Chinco et al [2019] Chinco A, Clark-Joseph AD, Ye M (2019) Sparse signals in the cross-section of returns. The Journal of Finance 74(1):449–492 Dixon and Polson [2020] Dixon M, Polson N (2020) Deep fundamental factor models. SIAM Journal on Financial Mathematics 11(3):SC26–SC37 Duan and Kashima [2021] Duan J, Kashima H (2021) Learning to rank for multi-step ahead time-series forecasting. IEEE Access 9:49372–49386 Efthymiou et al [2019] Efthymiou S, Hidary J, Leichenauer S (2019) Tensornetwork for machine learning. arXiv preprint arXiv:190606329 Eugene and French [1992] Eugene F, French K (1992) The cross-section of expected stock returns. Journal of Finance 47(2):427–465 Fannes et al [1992] Fannes M, Nachtergaele B, Werner RF (1992) Finitely correlated states on quantum spin chains. Communications in mathematical physics 144(3):443–490 Gu et al [2020] Gu S, Kelly B, Xiu D (2020) Empirical Asset Pricing via Machine Learning. The Review of Financial Studies 33(5):2223–2273. 10.1093/rfs/hhaa009, URL https://doi.org/10.1093/rfs/hhaa009, https://academic.oup.com/rfs/article-pdf/33/5/2223/33209812/hhaa009.pdf Gu et al [2021] Gu S, Kelly B, Xiu D (2021) Autoencoder asset pricing models. Journal of Econometrics 222(1):429–450 Huggins et al [2019] Huggins W, Patil P, Mitchell B, et al (2019) Towards quantum machine learning with tensor networks. Quantum Science and technology 4(2):024001 Kim [2019] Kim S (2019) Enhancing the momentum strategy through deep regression. Quantitative Finance 19(7):1121–1133 Lim et al [2019] Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Bao W, Yue J, Rao Y (2017) A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7):e0180944 Bausch [2020] Bausch J (2020) Recurrent quantum neural networks. Advances in neural information processing systems 33:1368–1379 Cerezo et al [2021] Cerezo M, Arrasmith A, Babbush R, et al (2021) Variational quantum algorithms. Nature Reviews Physics 3(9):625–644 Chinco et al [2019] Chinco A, Clark-Joseph AD, Ye M (2019) Sparse signals in the cross-section of returns. The Journal of Finance 74(1):449–492 Dixon and Polson [2020] Dixon M, Polson N (2020) Deep fundamental factor models. SIAM Journal on Financial Mathematics 11(3):SC26–SC37 Duan and Kashima [2021] Duan J, Kashima H (2021) Learning to rank for multi-step ahead time-series forecasting. IEEE Access 9:49372–49386 Efthymiou et al [2019] Efthymiou S, Hidary J, Leichenauer S (2019) Tensornetwork for machine learning. arXiv preprint arXiv:190606329 Eugene and French [1992] Eugene F, French K (1992) The cross-section of expected stock returns. Journal of Finance 47(2):427–465 Fannes et al [1992] Fannes M, Nachtergaele B, Werner RF (1992) Finitely correlated states on quantum spin chains. Communications in mathematical physics 144(3):443–490 Gu et al [2020] Gu S, Kelly B, Xiu D (2020) Empirical Asset Pricing via Machine Learning. The Review of Financial Studies 33(5):2223–2273. 10.1093/rfs/hhaa009, URL https://doi.org/10.1093/rfs/hhaa009, https://academic.oup.com/rfs/article-pdf/33/5/2223/33209812/hhaa009.pdf Gu et al [2021] Gu S, Kelly B, Xiu D (2021) Autoencoder asset pricing models. Journal of Econometrics 222(1):429–450 Huggins et al [2019] Huggins W, Patil P, Mitchell B, et al (2019) Towards quantum machine learning with tensor networks. Quantum Science and technology 4(2):024001 Kim [2019] Kim S (2019) Enhancing the momentum strategy through deep regression. Quantitative Finance 19(7):1121–1133 Lim et al [2019] Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Bausch J (2020) Recurrent quantum neural networks. Advances in neural information processing systems 33:1368–1379 Cerezo et al [2021] Cerezo M, Arrasmith A, Babbush R, et al (2021) Variational quantum algorithms. Nature Reviews Physics 3(9):625–644 Chinco et al [2019] Chinco A, Clark-Joseph AD, Ye M (2019) Sparse signals in the cross-section of returns. The Journal of Finance 74(1):449–492 Dixon and Polson [2020] Dixon M, Polson N (2020) Deep fundamental factor models. SIAM Journal on Financial Mathematics 11(3):SC26–SC37 Duan and Kashima [2021] Duan J, Kashima H (2021) Learning to rank for multi-step ahead time-series forecasting. IEEE Access 9:49372–49386 Efthymiou et al [2019] Efthymiou S, Hidary J, Leichenauer S (2019) Tensornetwork for machine learning. arXiv preprint arXiv:190606329 Eugene and French [1992] Eugene F, French K (1992) The cross-section of expected stock returns. Journal of Finance 47(2):427–465 Fannes et al [1992] Fannes M, Nachtergaele B, Werner RF (1992) Finitely correlated states on quantum spin chains. Communications in mathematical physics 144(3):443–490 Gu et al [2020] Gu S, Kelly B, Xiu D (2020) Empirical Asset Pricing via Machine Learning. The Review of Financial Studies 33(5):2223–2273. 10.1093/rfs/hhaa009, URL https://doi.org/10.1093/rfs/hhaa009, https://academic.oup.com/rfs/article-pdf/33/5/2223/33209812/hhaa009.pdf Gu et al [2021] Gu S, Kelly B, Xiu D (2021) Autoencoder asset pricing models. Journal of Econometrics 222(1):429–450 Huggins et al [2019] Huggins W, Patil P, Mitchell B, et al (2019) Towards quantum machine learning with tensor networks. Quantum Science and technology 4(2):024001 Kim [2019] Kim S (2019) Enhancing the momentum strategy through deep regression. Quantitative Finance 19(7):1121–1133 Lim et al [2019] Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Cerezo M, Arrasmith A, Babbush R, et al (2021) Variational quantum algorithms. Nature Reviews Physics 3(9):625–644 Chinco et al [2019] Chinco A, Clark-Joseph AD, Ye M (2019) Sparse signals in the cross-section of returns. The Journal of Finance 74(1):449–492 Dixon and Polson [2020] Dixon M, Polson N (2020) Deep fundamental factor models. SIAM Journal on Financial Mathematics 11(3):SC26–SC37 Duan and Kashima [2021] Duan J, Kashima H (2021) Learning to rank for multi-step ahead time-series forecasting. IEEE Access 9:49372–49386 Efthymiou et al [2019] Efthymiou S, Hidary J, Leichenauer S (2019) Tensornetwork for machine learning. arXiv preprint arXiv:190606329 Eugene and French [1992] Eugene F, French K (1992) The cross-section of expected stock returns. Journal of Finance 47(2):427–465 Fannes et al [1992] Fannes M, Nachtergaele B, Werner RF (1992) Finitely correlated states on quantum spin chains. Communications in mathematical physics 144(3):443–490 Gu et al [2020] Gu S, Kelly B, Xiu D (2020) Empirical Asset Pricing via Machine Learning. The Review of Financial Studies 33(5):2223–2273. 10.1093/rfs/hhaa009, URL https://doi.org/10.1093/rfs/hhaa009, https://academic.oup.com/rfs/article-pdf/33/5/2223/33209812/hhaa009.pdf Gu et al [2021] Gu S, Kelly B, Xiu D (2021) Autoencoder asset pricing models. Journal of Econometrics 222(1):429–450 Huggins et al [2019] Huggins W, Patil P, Mitchell B, et al (2019) Towards quantum machine learning with tensor networks. Quantum Science and technology 4(2):024001 Kim [2019] Kim S (2019) Enhancing the momentum strategy through deep regression. Quantitative Finance 19(7):1121–1133 Lim et al [2019] Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Chinco A, Clark-Joseph AD, Ye M (2019) Sparse signals in the cross-section of returns. The Journal of Finance 74(1):449–492 Dixon and Polson [2020] Dixon M, Polson N (2020) Deep fundamental factor models. SIAM Journal on Financial Mathematics 11(3):SC26–SC37 Duan and Kashima [2021] Duan J, Kashima H (2021) Learning to rank for multi-step ahead time-series forecasting. IEEE Access 9:49372–49386 Efthymiou et al [2019] Efthymiou S, Hidary J, Leichenauer S (2019) Tensornetwork for machine learning. arXiv preprint arXiv:190606329 Eugene and French [1992] Eugene F, French K (1992) The cross-section of expected stock returns. Journal of Finance 47(2):427–465 Fannes et al [1992] Fannes M, Nachtergaele B, Werner RF (1992) Finitely correlated states on quantum spin chains. Communications in mathematical physics 144(3):443–490 Gu et al [2020] Gu S, Kelly B, Xiu D (2020) Empirical Asset Pricing via Machine Learning. The Review of Financial Studies 33(5):2223–2273. 10.1093/rfs/hhaa009, URL https://doi.org/10.1093/rfs/hhaa009, https://academic.oup.com/rfs/article-pdf/33/5/2223/33209812/hhaa009.pdf Gu et al [2021] Gu S, Kelly B, Xiu D (2021) Autoencoder asset pricing models. Journal of Econometrics 222(1):429–450 Huggins et al [2019] Huggins W, Patil P, Mitchell B, et al (2019) Towards quantum machine learning with tensor networks. Quantum Science and technology 4(2):024001 Kim [2019] Kim S (2019) Enhancing the momentum strategy through deep regression. Quantitative Finance 19(7):1121–1133 Lim et al [2019] Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Dixon M, Polson N (2020) Deep fundamental factor models. SIAM Journal on Financial Mathematics 11(3):SC26–SC37 Duan and Kashima [2021] Duan J, Kashima H (2021) Learning to rank for multi-step ahead time-series forecasting. IEEE Access 9:49372–49386 Efthymiou et al [2019] Efthymiou S, Hidary J, Leichenauer S (2019) Tensornetwork for machine learning. arXiv preprint arXiv:190606329 Eugene and French [1992] Eugene F, French K (1992) The cross-section of expected stock returns. Journal of Finance 47(2):427–465 Fannes et al [1992] Fannes M, Nachtergaele B, Werner RF (1992) Finitely correlated states on quantum spin chains. Communications in mathematical physics 144(3):443–490 Gu et al [2020] Gu S, Kelly B, Xiu D (2020) Empirical Asset Pricing via Machine Learning. The Review of Financial Studies 33(5):2223–2273. 10.1093/rfs/hhaa009, URL https://doi.org/10.1093/rfs/hhaa009, https://academic.oup.com/rfs/article-pdf/33/5/2223/33209812/hhaa009.pdf Gu et al [2021] Gu S, Kelly B, Xiu D (2021) Autoencoder asset pricing models. Journal of Econometrics 222(1):429–450 Huggins et al [2019] Huggins W, Patil P, Mitchell B, et al (2019) Towards quantum machine learning with tensor networks. Quantum Science and technology 4(2):024001 Kim [2019] Kim S (2019) Enhancing the momentum strategy through deep regression. Quantitative Finance 19(7):1121–1133 Lim et al [2019] Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Duan J, Kashima H (2021) Learning to rank for multi-step ahead time-series forecasting. IEEE Access 9:49372–49386 Efthymiou et al [2019] Efthymiou S, Hidary J, Leichenauer S (2019) Tensornetwork for machine learning. arXiv preprint arXiv:190606329 Eugene and French [1992] Eugene F, French K (1992) The cross-section of expected stock returns. Journal of Finance 47(2):427–465 Fannes et al [1992] Fannes M, Nachtergaele B, Werner RF (1992) Finitely correlated states on quantum spin chains. Communications in mathematical physics 144(3):443–490 Gu et al [2020] Gu S, Kelly B, Xiu D (2020) Empirical Asset Pricing via Machine Learning. The Review of Financial Studies 33(5):2223–2273. 10.1093/rfs/hhaa009, URL https://doi.org/10.1093/rfs/hhaa009, https://academic.oup.com/rfs/article-pdf/33/5/2223/33209812/hhaa009.pdf Gu et al [2021] Gu S, Kelly B, Xiu D (2021) Autoencoder asset pricing models. Journal of Econometrics 222(1):429–450 Huggins et al [2019] Huggins W, Patil P, Mitchell B, et al (2019) Towards quantum machine learning with tensor networks. Quantum Science and technology 4(2):024001 Kim [2019] Kim S (2019) Enhancing the momentum strategy through deep regression. Quantitative Finance 19(7):1121–1133 Lim et al [2019] Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Efthymiou S, Hidary J, Leichenauer S (2019) Tensornetwork for machine learning. arXiv preprint arXiv:190606329 Eugene and French [1992] Eugene F, French K (1992) The cross-section of expected stock returns. Journal of Finance 47(2):427–465 Fannes et al [1992] Fannes M, Nachtergaele B, Werner RF (1992) Finitely correlated states on quantum spin chains. Communications in mathematical physics 144(3):443–490 Gu et al [2020] Gu S, Kelly B, Xiu D (2020) Empirical Asset Pricing via Machine Learning. The Review of Financial Studies 33(5):2223–2273. 10.1093/rfs/hhaa009, URL https://doi.org/10.1093/rfs/hhaa009, https://academic.oup.com/rfs/article-pdf/33/5/2223/33209812/hhaa009.pdf Gu et al [2021] Gu S, Kelly B, Xiu D (2021) Autoencoder asset pricing models. Journal of Econometrics 222(1):429–450 Huggins et al [2019] Huggins W, Patil P, Mitchell B, et al (2019) Towards quantum machine learning with tensor networks. Quantum Science and technology 4(2):024001 Kim [2019] Kim S (2019) Enhancing the momentum strategy through deep regression. Quantitative Finance 19(7):1121–1133 Lim et al [2019] Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Eugene F, French K (1992) The cross-section of expected stock returns. Journal of Finance 47(2):427–465 Fannes et al [1992] Fannes M, Nachtergaele B, Werner RF (1992) Finitely correlated states on quantum spin chains. Communications in mathematical physics 144(3):443–490 Gu et al [2020] Gu S, Kelly B, Xiu D (2020) Empirical Asset Pricing via Machine Learning. The Review of Financial Studies 33(5):2223–2273. 10.1093/rfs/hhaa009, URL https://doi.org/10.1093/rfs/hhaa009, https://academic.oup.com/rfs/article-pdf/33/5/2223/33209812/hhaa009.pdf Gu et al [2021] Gu S, Kelly B, Xiu D (2021) Autoencoder asset pricing models. Journal of Econometrics 222(1):429–450 Huggins et al [2019] Huggins W, Patil P, Mitchell B, et al (2019) Towards quantum machine learning with tensor networks. Quantum Science and technology 4(2):024001 Kim [2019] Kim S (2019) Enhancing the momentum strategy through deep regression. Quantitative Finance 19(7):1121–1133 Lim et al [2019] Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Fannes M, Nachtergaele B, Werner RF (1992) Finitely correlated states on quantum spin chains. Communications in mathematical physics 144(3):443–490 Gu et al [2020] Gu S, Kelly B, Xiu D (2020) Empirical Asset Pricing via Machine Learning. The Review of Financial Studies 33(5):2223–2273. 10.1093/rfs/hhaa009, URL https://doi.org/10.1093/rfs/hhaa009, https://academic.oup.com/rfs/article-pdf/33/5/2223/33209812/hhaa009.pdf Gu et al [2021] Gu S, Kelly B, Xiu D (2021) Autoencoder asset pricing models. Journal of Econometrics 222(1):429–450 Huggins et al [2019] Huggins W, Patil P, Mitchell B, et al (2019) Towards quantum machine learning with tensor networks. Quantum Science and technology 4(2):024001 Kim [2019] Kim S (2019) Enhancing the momentum strategy through deep regression. Quantitative Finance 19(7):1121–1133 Lim et al [2019] Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Gu S, Kelly B, Xiu D (2020) Empirical Asset Pricing via Machine Learning. The Review of Financial Studies 33(5):2223–2273. 10.1093/rfs/hhaa009, URL https://doi.org/10.1093/rfs/hhaa009, https://academic.oup.com/rfs/article-pdf/33/5/2223/33209812/hhaa009.pdf Gu et al [2021] Gu S, Kelly B, Xiu D (2021) Autoencoder asset pricing models. Journal of Econometrics 222(1):429–450 Huggins et al [2019] Huggins W, Patil P, Mitchell B, et al (2019) Towards quantum machine learning with tensor networks. Quantum Science and technology 4(2):024001 Kim [2019] Kim S (2019) Enhancing the momentum strategy through deep regression. Quantitative Finance 19(7):1121–1133 Lim et al [2019] Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Gu S, Kelly B, Xiu D (2021) Autoencoder asset pricing models. Journal of Econometrics 222(1):429–450 Huggins et al [2019] Huggins W, Patil P, Mitchell B, et al (2019) Towards quantum machine learning with tensor networks. Quantum Science and technology 4(2):024001 Kim [2019] Kim S (2019) Enhancing the momentum strategy through deep regression. Quantitative Finance 19(7):1121–1133 Lim et al [2019] Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Huggins W, Patil P, Mitchell B, et al (2019) Towards quantum machine learning with tensor networks. Quantum Science and technology 4(2):024001 Kim [2019] Kim S (2019) Enhancing the momentum strategy through deep regression. Quantitative Finance 19(7):1121–1133 Lim et al [2019] Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Kim S (2019) Enhancing the momentum strategy through deep regression. Quantitative Finance 19(7):1121–1133 Lim et al [2019] Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. 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Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. 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International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Preskill J (2018) Quantum Computing in the NISQ era and beyond. 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In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Bausch J (2020) Recurrent quantum neural networks. Advances in neural information processing systems 33:1368–1379 Cerezo et al [2021] Cerezo M, Arrasmith A, Babbush R, et al (2021) Variational quantum algorithms. Nature Reviews Physics 3(9):625–644 Chinco et al [2019] Chinco A, Clark-Joseph AD, Ye M (2019) Sparse signals in the cross-section of returns. The Journal of Finance 74(1):449–492 Dixon and Polson [2020] Dixon M, Polson N (2020) Deep fundamental factor models. SIAM Journal on Financial Mathematics 11(3):SC26–SC37 Duan and Kashima [2021] Duan J, Kashima H (2021) Learning to rank for multi-step ahead time-series forecasting. IEEE Access 9:49372–49386 Efthymiou et al [2019] Efthymiou S, Hidary J, Leichenauer S (2019) Tensornetwork for machine learning. arXiv preprint arXiv:190606329 Eugene and French [1992] Eugene F, French K (1992) The cross-section of expected stock returns. Journal of Finance 47(2):427–465 Fannes et al [1992] Fannes M, Nachtergaele B, Werner RF (1992) Finitely correlated states on quantum spin chains. Communications in mathematical physics 144(3):443–490 Gu et al [2020] Gu S, Kelly B, Xiu D (2020) Empirical Asset Pricing via Machine Learning. The Review of Financial Studies 33(5):2223–2273. 10.1093/rfs/hhaa009, URL https://doi.org/10.1093/rfs/hhaa009, https://academic.oup.com/rfs/article-pdf/33/5/2223/33209812/hhaa009.pdf Gu et al [2021] Gu S, Kelly B, Xiu D (2021) Autoencoder asset pricing models. Journal of Econometrics 222(1):429–450 Huggins et al [2019] Huggins W, Patil P, Mitchell B, et al (2019) Towards quantum machine learning with tensor networks. Quantum Science and technology 4(2):024001 Kim [2019] Kim S (2019) Enhancing the momentum strategy through deep regression. Quantitative Finance 19(7):1121–1133 Lim et al [2019] Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Cerezo M, Arrasmith A, Babbush R, et al (2021) Variational quantum algorithms. Nature Reviews Physics 3(9):625–644 Chinco et al [2019] Chinco A, Clark-Joseph AD, Ye M (2019) Sparse signals in the cross-section of returns. The Journal of Finance 74(1):449–492 Dixon and Polson [2020] Dixon M, Polson N (2020) Deep fundamental factor models. SIAM Journal on Financial Mathematics 11(3):SC26–SC37 Duan and Kashima [2021] Duan J, Kashima H (2021) Learning to rank for multi-step ahead time-series forecasting. IEEE Access 9:49372–49386 Efthymiou et al [2019] Efthymiou S, Hidary J, Leichenauer S (2019) Tensornetwork for machine learning. arXiv preprint arXiv:190606329 Eugene and French [1992] Eugene F, French K (1992) The cross-section of expected stock returns. Journal of Finance 47(2):427–465 Fannes et al [1992] Fannes M, Nachtergaele B, Werner RF (1992) Finitely correlated states on quantum spin chains. Communications in mathematical physics 144(3):443–490 Gu et al [2020] Gu S, Kelly B, Xiu D (2020) Empirical Asset Pricing via Machine Learning. The Review of Financial Studies 33(5):2223–2273. 10.1093/rfs/hhaa009, URL https://doi.org/10.1093/rfs/hhaa009, https://academic.oup.com/rfs/article-pdf/33/5/2223/33209812/hhaa009.pdf Gu et al [2021] Gu S, Kelly B, Xiu D (2021) Autoencoder asset pricing models. Journal of Econometrics 222(1):429–450 Huggins et al [2019] Huggins W, Patil P, Mitchell B, et al (2019) Towards quantum machine learning with tensor networks. Quantum Science and technology 4(2):024001 Kim [2019] Kim S (2019) Enhancing the momentum strategy through deep regression. Quantitative Finance 19(7):1121–1133 Lim et al [2019] Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Chinco A, Clark-Joseph AD, Ye M (2019) Sparse signals in the cross-section of returns. The Journal of Finance 74(1):449–492 Dixon and Polson [2020] Dixon M, Polson N (2020) Deep fundamental factor models. SIAM Journal on Financial Mathematics 11(3):SC26–SC37 Duan and Kashima [2021] Duan J, Kashima H (2021) Learning to rank for multi-step ahead time-series forecasting. IEEE Access 9:49372–49386 Efthymiou et al [2019] Efthymiou S, Hidary J, Leichenauer S (2019) Tensornetwork for machine learning. arXiv preprint arXiv:190606329 Eugene and French [1992] Eugene F, French K (1992) The cross-section of expected stock returns. Journal of Finance 47(2):427–465 Fannes et al [1992] Fannes M, Nachtergaele B, Werner RF (1992) Finitely correlated states on quantum spin chains. Communications in mathematical physics 144(3):443–490 Gu et al [2020] Gu S, Kelly B, Xiu D (2020) Empirical Asset Pricing via Machine Learning. The Review of Financial Studies 33(5):2223–2273. 10.1093/rfs/hhaa009, URL https://doi.org/10.1093/rfs/hhaa009, https://academic.oup.com/rfs/article-pdf/33/5/2223/33209812/hhaa009.pdf Gu et al [2021] Gu S, Kelly B, Xiu D (2021) Autoencoder asset pricing models. Journal of Econometrics 222(1):429–450 Huggins et al [2019] Huggins W, Patil P, Mitchell B, et al (2019) Towards quantum machine learning with tensor networks. Quantum Science and technology 4(2):024001 Kim [2019] Kim S (2019) Enhancing the momentum strategy through deep regression. Quantitative Finance 19(7):1121–1133 Lim et al [2019] Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Dixon M, Polson N (2020) Deep fundamental factor models. SIAM Journal on Financial Mathematics 11(3):SC26–SC37 Duan and Kashima [2021] Duan J, Kashima H (2021) Learning to rank for multi-step ahead time-series forecasting. IEEE Access 9:49372–49386 Efthymiou et al [2019] Efthymiou S, Hidary J, Leichenauer S (2019) Tensornetwork for machine learning. arXiv preprint arXiv:190606329 Eugene and French [1992] Eugene F, French K (1992) The cross-section of expected stock returns. Journal of Finance 47(2):427–465 Fannes et al [1992] Fannes M, Nachtergaele B, Werner RF (1992) Finitely correlated states on quantum spin chains. Communications in mathematical physics 144(3):443–490 Gu et al [2020] Gu S, Kelly B, Xiu D (2020) Empirical Asset Pricing via Machine Learning. The Review of Financial Studies 33(5):2223–2273. 10.1093/rfs/hhaa009, URL https://doi.org/10.1093/rfs/hhaa009, https://academic.oup.com/rfs/article-pdf/33/5/2223/33209812/hhaa009.pdf Gu et al [2021] Gu S, Kelly B, Xiu D (2021) Autoencoder asset pricing models. Journal of Econometrics 222(1):429–450 Huggins et al [2019] Huggins W, Patil P, Mitchell B, et al (2019) Towards quantum machine learning with tensor networks. Quantum Science and technology 4(2):024001 Kim [2019] Kim S (2019) Enhancing the momentum strategy through deep regression. Quantitative Finance 19(7):1121–1133 Lim et al [2019] Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Duan J, Kashima H (2021) Learning to rank for multi-step ahead time-series forecasting. IEEE Access 9:49372–49386 Efthymiou et al [2019] Efthymiou S, Hidary J, Leichenauer S (2019) Tensornetwork for machine learning. arXiv preprint arXiv:190606329 Eugene and French [1992] Eugene F, French K (1992) The cross-section of expected stock returns. Journal of Finance 47(2):427–465 Fannes et al [1992] Fannes M, Nachtergaele B, Werner RF (1992) Finitely correlated states on quantum spin chains. Communications in mathematical physics 144(3):443–490 Gu et al [2020] Gu S, Kelly B, Xiu D (2020) Empirical Asset Pricing via Machine Learning. The Review of Financial Studies 33(5):2223–2273. 10.1093/rfs/hhaa009, URL https://doi.org/10.1093/rfs/hhaa009, https://academic.oup.com/rfs/article-pdf/33/5/2223/33209812/hhaa009.pdf Gu et al [2021] Gu S, Kelly B, Xiu D (2021) Autoencoder asset pricing models. Journal of Econometrics 222(1):429–450 Huggins et al [2019] Huggins W, Patil P, Mitchell B, et al (2019) Towards quantum machine learning with tensor networks. Quantum Science and technology 4(2):024001 Kim [2019] Kim S (2019) Enhancing the momentum strategy through deep regression. Quantitative Finance 19(7):1121–1133 Lim et al [2019] Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Efthymiou S, Hidary J, Leichenauer S (2019) Tensornetwork for machine learning. arXiv preprint arXiv:190606329 Eugene and French [1992] Eugene F, French K (1992) The cross-section of expected stock returns. Journal of Finance 47(2):427–465 Fannes et al [1992] Fannes M, Nachtergaele B, Werner RF (1992) Finitely correlated states on quantum spin chains. Communications in mathematical physics 144(3):443–490 Gu et al [2020] Gu S, Kelly B, Xiu D (2020) Empirical Asset Pricing via Machine Learning. The Review of Financial Studies 33(5):2223–2273. 10.1093/rfs/hhaa009, URL https://doi.org/10.1093/rfs/hhaa009, https://academic.oup.com/rfs/article-pdf/33/5/2223/33209812/hhaa009.pdf Gu et al [2021] Gu S, Kelly B, Xiu D (2021) Autoencoder asset pricing models. Journal of Econometrics 222(1):429–450 Huggins et al [2019] Huggins W, Patil P, Mitchell B, et al (2019) Towards quantum machine learning with tensor networks. Quantum Science and technology 4(2):024001 Kim [2019] Kim S (2019) Enhancing the momentum strategy through deep regression. Quantitative Finance 19(7):1121–1133 Lim et al [2019] Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Eugene F, French K (1992) The cross-section of expected stock returns. Journal of Finance 47(2):427–465 Fannes et al [1992] Fannes M, Nachtergaele B, Werner RF (1992) Finitely correlated states on quantum spin chains. Communications in mathematical physics 144(3):443–490 Gu et al [2020] Gu S, Kelly B, Xiu D (2020) Empirical Asset Pricing via Machine Learning. The Review of Financial Studies 33(5):2223–2273. 10.1093/rfs/hhaa009, URL https://doi.org/10.1093/rfs/hhaa009, https://academic.oup.com/rfs/article-pdf/33/5/2223/33209812/hhaa009.pdf Gu et al [2021] Gu S, Kelly B, Xiu D (2021) Autoencoder asset pricing models. Journal of Econometrics 222(1):429–450 Huggins et al [2019] Huggins W, Patil P, Mitchell B, et al (2019) Towards quantum machine learning with tensor networks. Quantum Science and technology 4(2):024001 Kim [2019] Kim S (2019) Enhancing the momentum strategy through deep regression. Quantitative Finance 19(7):1121–1133 Lim et al [2019] Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Fannes M, Nachtergaele B, Werner RF (1992) Finitely correlated states on quantum spin chains. Communications in mathematical physics 144(3):443–490 Gu et al [2020] Gu S, Kelly B, Xiu D (2020) Empirical Asset Pricing via Machine Learning. The Review of Financial Studies 33(5):2223–2273. 10.1093/rfs/hhaa009, URL https://doi.org/10.1093/rfs/hhaa009, https://academic.oup.com/rfs/article-pdf/33/5/2223/33209812/hhaa009.pdf Gu et al [2021] Gu S, Kelly B, Xiu D (2021) Autoencoder asset pricing models. Journal of Econometrics 222(1):429–450 Huggins et al [2019] Huggins W, Patil P, Mitchell B, et al (2019) Towards quantum machine learning with tensor networks. Quantum Science and technology 4(2):024001 Kim [2019] Kim S (2019) Enhancing the momentum strategy through deep regression. Quantitative Finance 19(7):1121–1133 Lim et al [2019] Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Gu S, Kelly B, Xiu D (2020) Empirical Asset Pricing via Machine Learning. The Review of Financial Studies 33(5):2223–2273. 10.1093/rfs/hhaa009, URL https://doi.org/10.1093/rfs/hhaa009, https://academic.oup.com/rfs/article-pdf/33/5/2223/33209812/hhaa009.pdf Gu et al [2021] Gu S, Kelly B, Xiu D (2021) Autoencoder asset pricing models. Journal of Econometrics 222(1):429–450 Huggins et al [2019] Huggins W, Patil P, Mitchell B, et al (2019) Towards quantum machine learning with tensor networks. Quantum Science and technology 4(2):024001 Kim [2019] Kim S (2019) Enhancing the momentum strategy through deep regression. Quantitative Finance 19(7):1121–1133 Lim et al [2019] Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Gu S, Kelly B, Xiu D (2021) Autoencoder asset pricing models. Journal of Econometrics 222(1):429–450 Huggins et al [2019] Huggins W, Patil P, Mitchell B, et al (2019) Towards quantum machine learning with tensor networks. Quantum Science and technology 4(2):024001 Kim [2019] Kim S (2019) Enhancing the momentum strategy through deep regression. Quantitative Finance 19(7):1121–1133 Lim et al [2019] Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Huggins W, Patil P, Mitchell B, et al (2019) Towards quantum machine learning with tensor networks. Quantum Science and technology 4(2):024001 Kim [2019] Kim S (2019) Enhancing the momentum strategy through deep regression. Quantitative Finance 19(7):1121–1133 Lim et al [2019] Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Kim S (2019) Enhancing the momentum strategy through deep regression. Quantitative Finance 19(7):1121–1133 Lim et al [2019] Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863
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Journal of Finance 47(2):427–465 Fannes et al [1992] Fannes M, Nachtergaele B, Werner RF (1992) Finitely correlated states on quantum spin chains. Communications in mathematical physics 144(3):443–490 Gu et al [2020] Gu S, Kelly B, Xiu D (2020) Empirical Asset Pricing via Machine Learning. The Review of Financial Studies 33(5):2223–2273. 10.1093/rfs/hhaa009, URL https://doi.org/10.1093/rfs/hhaa009, https://academic.oup.com/rfs/article-pdf/33/5/2223/33209812/hhaa009.pdf Gu et al [2021] Gu S, Kelly B, Xiu D (2021) Autoencoder asset pricing models. Journal of Econometrics 222(1):429–450 Huggins et al [2019] Huggins W, Patil P, Mitchell B, et al (2019) Towards quantum machine learning with tensor networks. Quantum Science and technology 4(2):024001 Kim [2019] Kim S (2019) Enhancing the momentum strategy through deep regression. Quantitative Finance 19(7):1121–1133 Lim et al [2019] Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Cerezo M, Arrasmith A, Babbush R, et al (2021) Variational quantum algorithms. Nature Reviews Physics 3(9):625–644 Chinco et al [2019] Chinco A, Clark-Joseph AD, Ye M (2019) Sparse signals in the cross-section of returns. The Journal of Finance 74(1):449–492 Dixon and Polson [2020] Dixon M, Polson N (2020) Deep fundamental factor models. SIAM Journal on Financial Mathematics 11(3):SC26–SC37 Duan and Kashima [2021] Duan J, Kashima H (2021) Learning to rank for multi-step ahead time-series forecasting. IEEE Access 9:49372–49386 Efthymiou et al [2019] Efthymiou S, Hidary J, Leichenauer S (2019) Tensornetwork for machine learning. arXiv preprint arXiv:190606329 Eugene and French [1992] Eugene F, French K (1992) The cross-section of expected stock returns. Journal of Finance 47(2):427–465 Fannes et al [1992] Fannes M, Nachtergaele B, Werner RF (1992) Finitely correlated states on quantum spin chains. Communications in mathematical physics 144(3):443–490 Gu et al [2020] Gu S, Kelly B, Xiu D (2020) Empirical Asset Pricing via Machine Learning. The Review of Financial Studies 33(5):2223–2273. 10.1093/rfs/hhaa009, URL https://doi.org/10.1093/rfs/hhaa009, https://academic.oup.com/rfs/article-pdf/33/5/2223/33209812/hhaa009.pdf Gu et al [2021] Gu S, Kelly B, Xiu D (2021) Autoencoder asset pricing models. Journal of Econometrics 222(1):429–450 Huggins et al [2019] Huggins W, Patil P, Mitchell B, et al (2019) Towards quantum machine learning with tensor networks. Quantum Science and technology 4(2):024001 Kim [2019] Kim S (2019) Enhancing the momentum strategy through deep regression. Quantitative Finance 19(7):1121–1133 Lim et al [2019] Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Chinco A, Clark-Joseph AD, Ye M (2019) Sparse signals in the cross-section of returns. The Journal of Finance 74(1):449–492 Dixon and Polson [2020] Dixon M, Polson N (2020) Deep fundamental factor models. SIAM Journal on Financial Mathematics 11(3):SC26–SC37 Duan and Kashima [2021] Duan J, Kashima H (2021) Learning to rank for multi-step ahead time-series forecasting. IEEE Access 9:49372–49386 Efthymiou et al [2019] Efthymiou S, Hidary J, Leichenauer S (2019) Tensornetwork for machine learning. arXiv preprint arXiv:190606329 Eugene and French [1992] Eugene F, French K (1992) The cross-section of expected stock returns. Journal of Finance 47(2):427–465 Fannes et al [1992] Fannes M, Nachtergaele B, Werner RF (1992) Finitely correlated states on quantum spin chains. Communications in mathematical physics 144(3):443–490 Gu et al [2020] Gu S, Kelly B, Xiu D (2020) Empirical Asset Pricing via Machine Learning. The Review of Financial Studies 33(5):2223–2273. 10.1093/rfs/hhaa009, URL https://doi.org/10.1093/rfs/hhaa009, https://academic.oup.com/rfs/article-pdf/33/5/2223/33209812/hhaa009.pdf Gu et al [2021] Gu S, Kelly B, Xiu D (2021) Autoencoder asset pricing models. Journal of Econometrics 222(1):429–450 Huggins et al [2019] Huggins W, Patil P, Mitchell B, et al (2019) Towards quantum machine learning with tensor networks. Quantum Science and technology 4(2):024001 Kim [2019] Kim S (2019) Enhancing the momentum strategy through deep regression. Quantitative Finance 19(7):1121–1133 Lim et al [2019] Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Dixon M, Polson N (2020) Deep fundamental factor models. SIAM Journal on Financial Mathematics 11(3):SC26–SC37 Duan and Kashima [2021] Duan J, Kashima H (2021) Learning to rank for multi-step ahead time-series forecasting. IEEE Access 9:49372–49386 Efthymiou et al [2019] Efthymiou S, Hidary J, Leichenauer S (2019) Tensornetwork for machine learning. arXiv preprint arXiv:190606329 Eugene and French [1992] Eugene F, French K (1992) The cross-section of expected stock returns. Journal of Finance 47(2):427–465 Fannes et al [1992] Fannes M, Nachtergaele B, Werner RF (1992) Finitely correlated states on quantum spin chains. Communications in mathematical physics 144(3):443–490 Gu et al [2020] Gu S, Kelly B, Xiu D (2020) Empirical Asset Pricing via Machine Learning. The Review of Financial Studies 33(5):2223–2273. 10.1093/rfs/hhaa009, URL https://doi.org/10.1093/rfs/hhaa009, https://academic.oup.com/rfs/article-pdf/33/5/2223/33209812/hhaa009.pdf Gu et al [2021] Gu S, Kelly B, Xiu D (2021) Autoencoder asset pricing models. Journal of Econometrics 222(1):429–450 Huggins et al [2019] Huggins W, Patil P, Mitchell B, et al (2019) Towards quantum machine learning with tensor networks. Quantum Science and technology 4(2):024001 Kim [2019] Kim S (2019) Enhancing the momentum strategy through deep regression. Quantitative Finance 19(7):1121–1133 Lim et al [2019] Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Duan J, Kashima H (2021) Learning to rank for multi-step ahead time-series forecasting. IEEE Access 9:49372–49386 Efthymiou et al [2019] Efthymiou S, Hidary J, Leichenauer S (2019) Tensornetwork for machine learning. arXiv preprint arXiv:190606329 Eugene and French [1992] Eugene F, French K (1992) The cross-section of expected stock returns. Journal of Finance 47(2):427–465 Fannes et al [1992] Fannes M, Nachtergaele B, Werner RF (1992) Finitely correlated states on quantum spin chains. Communications in mathematical physics 144(3):443–490 Gu et al [2020] Gu S, Kelly B, Xiu D (2020) Empirical Asset Pricing via Machine Learning. The Review of Financial Studies 33(5):2223–2273. 10.1093/rfs/hhaa009, URL https://doi.org/10.1093/rfs/hhaa009, https://academic.oup.com/rfs/article-pdf/33/5/2223/33209812/hhaa009.pdf Gu et al [2021] Gu S, Kelly B, Xiu D (2021) Autoencoder asset pricing models. Journal of Econometrics 222(1):429–450 Huggins et al [2019] Huggins W, Patil P, Mitchell B, et al (2019) Towards quantum machine learning with tensor networks. Quantum Science and technology 4(2):024001 Kim [2019] Kim S (2019) Enhancing the momentum strategy through deep regression. Quantitative Finance 19(7):1121–1133 Lim et al [2019] Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Efthymiou S, Hidary J, Leichenauer S (2019) Tensornetwork for machine learning. arXiv preprint arXiv:190606329 Eugene and French [1992] Eugene F, French K (1992) The cross-section of expected stock returns. Journal of Finance 47(2):427–465 Fannes et al [1992] Fannes M, Nachtergaele B, Werner RF (1992) Finitely correlated states on quantum spin chains. Communications in mathematical physics 144(3):443–490 Gu et al [2020] Gu S, Kelly B, Xiu D (2020) Empirical Asset Pricing via Machine Learning. The Review of Financial Studies 33(5):2223–2273. 10.1093/rfs/hhaa009, URL https://doi.org/10.1093/rfs/hhaa009, https://academic.oup.com/rfs/article-pdf/33/5/2223/33209812/hhaa009.pdf Gu et al [2021] Gu S, Kelly B, Xiu D (2021) Autoencoder asset pricing models. Journal of Econometrics 222(1):429–450 Huggins et al [2019] Huggins W, Patil P, Mitchell B, et al (2019) Towards quantum machine learning with tensor networks. Quantum Science and technology 4(2):024001 Kim [2019] Kim S (2019) Enhancing the momentum strategy through deep regression. Quantitative Finance 19(7):1121–1133 Lim et al [2019] Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Eugene F, French K (1992) The cross-section of expected stock returns. Journal of Finance 47(2):427–465 Fannes et al [1992] Fannes M, Nachtergaele B, Werner RF (1992) Finitely correlated states on quantum spin chains. Communications in mathematical physics 144(3):443–490 Gu et al [2020] Gu S, Kelly B, Xiu D (2020) Empirical Asset Pricing via Machine Learning. The Review of Financial Studies 33(5):2223–2273. 10.1093/rfs/hhaa009, URL https://doi.org/10.1093/rfs/hhaa009, https://academic.oup.com/rfs/article-pdf/33/5/2223/33209812/hhaa009.pdf Gu et al [2021] Gu S, Kelly B, Xiu D (2021) Autoencoder asset pricing models. Journal of Econometrics 222(1):429–450 Huggins et al [2019] Huggins W, Patil P, Mitchell B, et al (2019) Towards quantum machine learning with tensor networks. Quantum Science and technology 4(2):024001 Kim [2019] Kim S (2019) Enhancing the momentum strategy through deep regression. Quantitative Finance 19(7):1121–1133 Lim et al [2019] Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Fannes M, Nachtergaele B, Werner RF (1992) Finitely correlated states on quantum spin chains. Communications in mathematical physics 144(3):443–490 Gu et al [2020] Gu S, Kelly B, Xiu D (2020) Empirical Asset Pricing via Machine Learning. The Review of Financial Studies 33(5):2223–2273. 10.1093/rfs/hhaa009, URL https://doi.org/10.1093/rfs/hhaa009, https://academic.oup.com/rfs/article-pdf/33/5/2223/33209812/hhaa009.pdf Gu et al [2021] Gu S, Kelly B, Xiu D (2021) Autoencoder asset pricing models. Journal of Econometrics 222(1):429–450 Huggins et al [2019] Huggins W, Patil P, Mitchell B, et al (2019) Towards quantum machine learning with tensor networks. Quantum Science and technology 4(2):024001 Kim [2019] Kim S (2019) Enhancing the momentum strategy through deep regression. Quantitative Finance 19(7):1121–1133 Lim et al [2019] Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Gu S, Kelly B, Xiu D (2020) Empirical Asset Pricing via Machine Learning. The Review of Financial Studies 33(5):2223–2273. 10.1093/rfs/hhaa009, URL https://doi.org/10.1093/rfs/hhaa009, https://academic.oup.com/rfs/article-pdf/33/5/2223/33209812/hhaa009.pdf Gu et al [2021] Gu S, Kelly B, Xiu D (2021) Autoencoder asset pricing models. Journal of Econometrics 222(1):429–450 Huggins et al [2019] Huggins W, Patil P, Mitchell B, et al (2019) Towards quantum machine learning with tensor networks. Quantum Science and technology 4(2):024001 Kim [2019] Kim S (2019) Enhancing the momentum strategy through deep regression. Quantitative Finance 19(7):1121–1133 Lim et al [2019] Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Gu S, Kelly B, Xiu D (2021) Autoencoder asset pricing models. Journal of Econometrics 222(1):429–450 Huggins et al [2019] Huggins W, Patil P, Mitchell B, et al (2019) Towards quantum machine learning with tensor networks. Quantum Science and technology 4(2):024001 Kim [2019] Kim S (2019) Enhancing the momentum strategy through deep regression. Quantitative Finance 19(7):1121–1133 Lim et al [2019] Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Huggins W, Patil P, Mitchell B, et al (2019) Towards quantum machine learning with tensor networks. Quantum Science and technology 4(2):024001 Kim [2019] Kim S (2019) Enhancing the momentum strategy through deep regression. Quantitative Finance 19(7):1121–1133 Lim et al [2019] Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Kim S (2019) Enhancing the momentum strategy through deep regression. Quantitative Finance 19(7):1121–1133 Lim et al [2019] Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863
- Cerezo M, Arrasmith A, Babbush R, et al (2021) Variational quantum algorithms. Nature Reviews Physics 3(9):625–644 Chinco et al [2019] Chinco A, Clark-Joseph AD, Ye M (2019) Sparse signals in the cross-section of returns. The Journal of Finance 74(1):449–492 Dixon and Polson [2020] Dixon M, Polson N (2020) Deep fundamental factor models. SIAM Journal on Financial Mathematics 11(3):SC26–SC37 Duan and Kashima [2021] Duan J, Kashima H (2021) Learning to rank for multi-step ahead time-series forecasting. IEEE Access 9:49372–49386 Efthymiou et al [2019] Efthymiou S, Hidary J, Leichenauer S (2019) Tensornetwork for machine learning. arXiv preprint arXiv:190606329 Eugene and French [1992] Eugene F, French K (1992) The cross-section of expected stock returns. Journal of Finance 47(2):427–465 Fannes et al [1992] Fannes M, Nachtergaele B, Werner RF (1992) Finitely correlated states on quantum spin chains. Communications in mathematical physics 144(3):443–490 Gu et al [2020] Gu S, Kelly B, Xiu D (2020) Empirical Asset Pricing via Machine Learning. The Review of Financial Studies 33(5):2223–2273. 10.1093/rfs/hhaa009, URL https://doi.org/10.1093/rfs/hhaa009, https://academic.oup.com/rfs/article-pdf/33/5/2223/33209812/hhaa009.pdf Gu et al [2021] Gu S, Kelly B, Xiu D (2021) Autoencoder asset pricing models. Journal of Econometrics 222(1):429–450 Huggins et al [2019] Huggins W, Patil P, Mitchell B, et al (2019) Towards quantum machine learning with tensor networks. Quantum Science and technology 4(2):024001 Kim [2019] Kim S (2019) Enhancing the momentum strategy through deep regression. Quantitative Finance 19(7):1121–1133 Lim et al [2019] Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Chinco A, Clark-Joseph AD, Ye M (2019) Sparse signals in the cross-section of returns. The Journal of Finance 74(1):449–492 Dixon and Polson [2020] Dixon M, Polson N (2020) Deep fundamental factor models. SIAM Journal on Financial Mathematics 11(3):SC26–SC37 Duan and Kashima [2021] Duan J, Kashima H (2021) Learning to rank for multi-step ahead time-series forecasting. IEEE Access 9:49372–49386 Efthymiou et al [2019] Efthymiou S, Hidary J, Leichenauer S (2019) Tensornetwork for machine learning. arXiv preprint arXiv:190606329 Eugene and French [1992] Eugene F, French K (1992) The cross-section of expected stock returns. Journal of Finance 47(2):427–465 Fannes et al [1992] Fannes M, Nachtergaele B, Werner RF (1992) Finitely correlated states on quantum spin chains. Communications in mathematical physics 144(3):443–490 Gu et al [2020] Gu S, Kelly B, Xiu D (2020) Empirical Asset Pricing via Machine Learning. The Review of Financial Studies 33(5):2223–2273. 10.1093/rfs/hhaa009, URL https://doi.org/10.1093/rfs/hhaa009, https://academic.oup.com/rfs/article-pdf/33/5/2223/33209812/hhaa009.pdf Gu et al [2021] Gu S, Kelly B, Xiu D (2021) Autoencoder asset pricing models. Journal of Econometrics 222(1):429–450 Huggins et al [2019] Huggins W, Patil P, Mitchell B, et al (2019) Towards quantum machine learning with tensor networks. Quantum Science and technology 4(2):024001 Kim [2019] Kim S (2019) Enhancing the momentum strategy through deep regression. Quantitative Finance 19(7):1121–1133 Lim et al [2019] Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Dixon M, Polson N (2020) Deep fundamental factor models. SIAM Journal on Financial Mathematics 11(3):SC26–SC37 Duan and Kashima [2021] Duan J, Kashima H (2021) Learning to rank for multi-step ahead time-series forecasting. IEEE Access 9:49372–49386 Efthymiou et al [2019] Efthymiou S, Hidary J, Leichenauer S (2019) Tensornetwork for machine learning. arXiv preprint arXiv:190606329 Eugene and French [1992] Eugene F, French K (1992) The cross-section of expected stock returns. Journal of Finance 47(2):427–465 Fannes et al [1992] Fannes M, Nachtergaele B, Werner RF (1992) Finitely correlated states on quantum spin chains. Communications in mathematical physics 144(3):443–490 Gu et al [2020] Gu S, Kelly B, Xiu D (2020) Empirical Asset Pricing via Machine Learning. The Review of Financial Studies 33(5):2223–2273. 10.1093/rfs/hhaa009, URL https://doi.org/10.1093/rfs/hhaa009, https://academic.oup.com/rfs/article-pdf/33/5/2223/33209812/hhaa009.pdf Gu et al [2021] Gu S, Kelly B, Xiu D (2021) Autoencoder asset pricing models. Journal of Econometrics 222(1):429–450 Huggins et al [2019] Huggins W, Patil P, Mitchell B, et al (2019) Towards quantum machine learning with tensor networks. Quantum Science and technology 4(2):024001 Kim [2019] Kim S (2019) Enhancing the momentum strategy through deep regression. Quantitative Finance 19(7):1121–1133 Lim et al [2019] Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Duan J, Kashima H (2021) Learning to rank for multi-step ahead time-series forecasting. IEEE Access 9:49372–49386 Efthymiou et al [2019] Efthymiou S, Hidary J, Leichenauer S (2019) Tensornetwork for machine learning. arXiv preprint arXiv:190606329 Eugene and French [1992] Eugene F, French K (1992) The cross-section of expected stock returns. Journal of Finance 47(2):427–465 Fannes et al [1992] Fannes M, Nachtergaele B, Werner RF (1992) Finitely correlated states on quantum spin chains. Communications in mathematical physics 144(3):443–490 Gu et al [2020] Gu S, Kelly B, Xiu D (2020) Empirical Asset Pricing via Machine Learning. The Review of Financial Studies 33(5):2223–2273. 10.1093/rfs/hhaa009, URL https://doi.org/10.1093/rfs/hhaa009, https://academic.oup.com/rfs/article-pdf/33/5/2223/33209812/hhaa009.pdf Gu et al [2021] Gu S, Kelly B, Xiu D (2021) Autoencoder asset pricing models. Journal of Econometrics 222(1):429–450 Huggins et al [2019] Huggins W, Patil P, Mitchell B, et al (2019) Towards quantum machine learning with tensor networks. Quantum Science and technology 4(2):024001 Kim [2019] Kim S (2019) Enhancing the momentum strategy through deep regression. Quantitative Finance 19(7):1121–1133 Lim et al [2019] Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Efthymiou S, Hidary J, Leichenauer S (2019) Tensornetwork for machine learning. arXiv preprint arXiv:190606329 Eugene and French [1992] Eugene F, French K (1992) The cross-section of expected stock returns. Journal of Finance 47(2):427–465 Fannes et al [1992] Fannes M, Nachtergaele B, Werner RF (1992) Finitely correlated states on quantum spin chains. Communications in mathematical physics 144(3):443–490 Gu et al [2020] Gu S, Kelly B, Xiu D (2020) Empirical Asset Pricing via Machine Learning. The Review of Financial Studies 33(5):2223–2273. 10.1093/rfs/hhaa009, URL https://doi.org/10.1093/rfs/hhaa009, https://academic.oup.com/rfs/article-pdf/33/5/2223/33209812/hhaa009.pdf Gu et al [2021] Gu S, Kelly B, Xiu D (2021) Autoencoder asset pricing models. Journal of Econometrics 222(1):429–450 Huggins et al [2019] Huggins W, Patil P, Mitchell B, et al (2019) Towards quantum machine learning with tensor networks. Quantum Science and technology 4(2):024001 Kim [2019] Kim S (2019) Enhancing the momentum strategy through deep regression. Quantitative Finance 19(7):1121–1133 Lim et al [2019] Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Eugene F, French K (1992) The cross-section of expected stock returns. Journal of Finance 47(2):427–465 Fannes et al [1992] Fannes M, Nachtergaele B, Werner RF (1992) Finitely correlated states on quantum spin chains. Communications in mathematical physics 144(3):443–490 Gu et al [2020] Gu S, Kelly B, Xiu D (2020) Empirical Asset Pricing via Machine Learning. The Review of Financial Studies 33(5):2223–2273. 10.1093/rfs/hhaa009, URL https://doi.org/10.1093/rfs/hhaa009, https://academic.oup.com/rfs/article-pdf/33/5/2223/33209812/hhaa009.pdf Gu et al [2021] Gu S, Kelly B, Xiu D (2021) Autoencoder asset pricing models. Journal of Econometrics 222(1):429–450 Huggins et al [2019] Huggins W, Patil P, Mitchell B, et al (2019) Towards quantum machine learning with tensor networks. Quantum Science and technology 4(2):024001 Kim [2019] Kim S (2019) Enhancing the momentum strategy through deep regression. Quantitative Finance 19(7):1121–1133 Lim et al [2019] Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Fannes M, Nachtergaele B, Werner RF (1992) Finitely correlated states on quantum spin chains. Communications in mathematical physics 144(3):443–490 Gu et al [2020] Gu S, Kelly B, Xiu D (2020) Empirical Asset Pricing via Machine Learning. The Review of Financial Studies 33(5):2223–2273. 10.1093/rfs/hhaa009, URL https://doi.org/10.1093/rfs/hhaa009, https://academic.oup.com/rfs/article-pdf/33/5/2223/33209812/hhaa009.pdf Gu et al [2021] Gu S, Kelly B, Xiu D (2021) Autoencoder asset pricing models. Journal of Econometrics 222(1):429–450 Huggins et al [2019] Huggins W, Patil P, Mitchell B, et al (2019) Towards quantum machine learning with tensor networks. Quantum Science and technology 4(2):024001 Kim [2019] Kim S (2019) Enhancing the momentum strategy through deep regression. Quantitative Finance 19(7):1121–1133 Lim et al [2019] Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Gu S, Kelly B, Xiu D (2020) Empirical Asset Pricing via Machine Learning. The Review of Financial Studies 33(5):2223–2273. 10.1093/rfs/hhaa009, URL https://doi.org/10.1093/rfs/hhaa009, https://academic.oup.com/rfs/article-pdf/33/5/2223/33209812/hhaa009.pdf Gu et al [2021] Gu S, Kelly B, Xiu D (2021) Autoencoder asset pricing models. Journal of Econometrics 222(1):429–450 Huggins et al [2019] Huggins W, Patil P, Mitchell B, et al (2019) Towards quantum machine learning with tensor networks. Quantum Science and technology 4(2):024001 Kim [2019] Kim S (2019) Enhancing the momentum strategy through deep regression. Quantitative Finance 19(7):1121–1133 Lim et al [2019] Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Gu S, Kelly B, Xiu D (2021) Autoencoder asset pricing models. Journal of Econometrics 222(1):429–450 Huggins et al [2019] Huggins W, Patil P, Mitchell B, et al (2019) Towards quantum machine learning with tensor networks. Quantum Science and technology 4(2):024001 Kim [2019] Kim S (2019) Enhancing the momentum strategy through deep regression. Quantitative Finance 19(7):1121–1133 Lim et al [2019] Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Huggins W, Patil P, Mitchell B, et al (2019) Towards quantum machine learning with tensor networks. Quantum Science and technology 4(2):024001 Kim [2019] Kim S (2019) Enhancing the momentum strategy through deep regression. Quantitative Finance 19(7):1121–1133 Lim et al [2019] Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Kim S (2019) Enhancing the momentum strategy through deep regression. Quantitative Finance 19(7):1121–1133 Lim et al [2019] Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863
- Chinco A, Clark-Joseph AD, Ye M (2019) Sparse signals in the cross-section of returns. The Journal of Finance 74(1):449–492 Dixon and Polson [2020] Dixon M, Polson N (2020) Deep fundamental factor models. SIAM Journal on Financial Mathematics 11(3):SC26–SC37 Duan and Kashima [2021] Duan J, Kashima H (2021) Learning to rank for multi-step ahead time-series forecasting. IEEE Access 9:49372–49386 Efthymiou et al [2019] Efthymiou S, Hidary J, Leichenauer S (2019) Tensornetwork for machine learning. arXiv preprint arXiv:190606329 Eugene and French [1992] Eugene F, French K (1992) The cross-section of expected stock returns. Journal of Finance 47(2):427–465 Fannes et al [1992] Fannes M, Nachtergaele B, Werner RF (1992) Finitely correlated states on quantum spin chains. Communications in mathematical physics 144(3):443–490 Gu et al [2020] Gu S, Kelly B, Xiu D (2020) Empirical Asset Pricing via Machine Learning. The Review of Financial Studies 33(5):2223–2273. 10.1093/rfs/hhaa009, URL https://doi.org/10.1093/rfs/hhaa009, https://academic.oup.com/rfs/article-pdf/33/5/2223/33209812/hhaa009.pdf Gu et al [2021] Gu S, Kelly B, Xiu D (2021) Autoencoder asset pricing models. Journal of Econometrics 222(1):429–450 Huggins et al [2019] Huggins W, Patil P, Mitchell B, et al (2019) Towards quantum machine learning with tensor networks. Quantum Science and technology 4(2):024001 Kim [2019] Kim S (2019) Enhancing the momentum strategy through deep regression. Quantitative Finance 19(7):1121–1133 Lim et al [2019] Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Dixon M, Polson N (2020) Deep fundamental factor models. SIAM Journal on Financial Mathematics 11(3):SC26–SC37 Duan and Kashima [2021] Duan J, Kashima H (2021) Learning to rank for multi-step ahead time-series forecasting. IEEE Access 9:49372–49386 Efthymiou et al [2019] Efthymiou S, Hidary J, Leichenauer S (2019) Tensornetwork for machine learning. arXiv preprint arXiv:190606329 Eugene and French [1992] Eugene F, French K (1992) The cross-section of expected stock returns. Journal of Finance 47(2):427–465 Fannes et al [1992] Fannes M, Nachtergaele B, Werner RF (1992) Finitely correlated states on quantum spin chains. Communications in mathematical physics 144(3):443–490 Gu et al [2020] Gu S, Kelly B, Xiu D (2020) Empirical Asset Pricing via Machine Learning. The Review of Financial Studies 33(5):2223–2273. 10.1093/rfs/hhaa009, URL https://doi.org/10.1093/rfs/hhaa009, https://academic.oup.com/rfs/article-pdf/33/5/2223/33209812/hhaa009.pdf Gu et al [2021] Gu S, Kelly B, Xiu D (2021) Autoencoder asset pricing models. Journal of Econometrics 222(1):429–450 Huggins et al [2019] Huggins W, Patil P, Mitchell B, et al (2019) Towards quantum machine learning with tensor networks. Quantum Science and technology 4(2):024001 Kim [2019] Kim S (2019) Enhancing the momentum strategy through deep regression. Quantitative Finance 19(7):1121–1133 Lim et al [2019] Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Duan J, Kashima H (2021) Learning to rank for multi-step ahead time-series forecasting. IEEE Access 9:49372–49386 Efthymiou et al [2019] Efthymiou S, Hidary J, Leichenauer S (2019) Tensornetwork for machine learning. arXiv preprint arXiv:190606329 Eugene and French [1992] Eugene F, French K (1992) The cross-section of expected stock returns. Journal of Finance 47(2):427–465 Fannes et al [1992] Fannes M, Nachtergaele B, Werner RF (1992) Finitely correlated states on quantum spin chains. Communications in mathematical physics 144(3):443–490 Gu et al [2020] Gu S, Kelly B, Xiu D (2020) Empirical Asset Pricing via Machine Learning. The Review of Financial Studies 33(5):2223–2273. 10.1093/rfs/hhaa009, URL https://doi.org/10.1093/rfs/hhaa009, https://academic.oup.com/rfs/article-pdf/33/5/2223/33209812/hhaa009.pdf Gu et al [2021] Gu S, Kelly B, Xiu D (2021) Autoencoder asset pricing models. Journal of Econometrics 222(1):429–450 Huggins et al [2019] Huggins W, Patil P, Mitchell B, et al (2019) Towards quantum machine learning with tensor networks. Quantum Science and technology 4(2):024001 Kim [2019] Kim S (2019) Enhancing the momentum strategy through deep regression. Quantitative Finance 19(7):1121–1133 Lim et al [2019] Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Efthymiou S, Hidary J, Leichenauer S (2019) Tensornetwork for machine learning. arXiv preprint arXiv:190606329 Eugene and French [1992] Eugene F, French K (1992) The cross-section of expected stock returns. Journal of Finance 47(2):427–465 Fannes et al [1992] Fannes M, Nachtergaele B, Werner RF (1992) Finitely correlated states on quantum spin chains. Communications in mathematical physics 144(3):443–490 Gu et al [2020] Gu S, Kelly B, Xiu D (2020) Empirical Asset Pricing via Machine Learning. The Review of Financial Studies 33(5):2223–2273. 10.1093/rfs/hhaa009, URL https://doi.org/10.1093/rfs/hhaa009, https://academic.oup.com/rfs/article-pdf/33/5/2223/33209812/hhaa009.pdf Gu et al [2021] Gu S, Kelly B, Xiu D (2021) Autoencoder asset pricing models. Journal of Econometrics 222(1):429–450 Huggins et al [2019] Huggins W, Patil P, Mitchell B, et al (2019) Towards quantum machine learning with tensor networks. Quantum Science and technology 4(2):024001 Kim [2019] Kim S (2019) Enhancing the momentum strategy through deep regression. Quantitative Finance 19(7):1121–1133 Lim et al [2019] Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Eugene F, French K (1992) The cross-section of expected stock returns. Journal of Finance 47(2):427–465 Fannes et al [1992] Fannes M, Nachtergaele B, Werner RF (1992) Finitely correlated states on quantum spin chains. Communications in mathematical physics 144(3):443–490 Gu et al [2020] Gu S, Kelly B, Xiu D (2020) Empirical Asset Pricing via Machine Learning. The Review of Financial Studies 33(5):2223–2273. 10.1093/rfs/hhaa009, URL https://doi.org/10.1093/rfs/hhaa009, https://academic.oup.com/rfs/article-pdf/33/5/2223/33209812/hhaa009.pdf Gu et al [2021] Gu S, Kelly B, Xiu D (2021) Autoencoder asset pricing models. Journal of Econometrics 222(1):429–450 Huggins et al [2019] Huggins W, Patil P, Mitchell B, et al (2019) Towards quantum machine learning with tensor networks. Quantum Science and technology 4(2):024001 Kim [2019] Kim S (2019) Enhancing the momentum strategy through deep regression. Quantitative Finance 19(7):1121–1133 Lim et al [2019] Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Fannes M, Nachtergaele B, Werner RF (1992) Finitely correlated states on quantum spin chains. Communications in mathematical physics 144(3):443–490 Gu et al [2020] Gu S, Kelly B, Xiu D (2020) Empirical Asset Pricing via Machine Learning. The Review of Financial Studies 33(5):2223–2273. 10.1093/rfs/hhaa009, URL https://doi.org/10.1093/rfs/hhaa009, https://academic.oup.com/rfs/article-pdf/33/5/2223/33209812/hhaa009.pdf Gu et al [2021] Gu S, Kelly B, Xiu D (2021) Autoencoder asset pricing models. Journal of Econometrics 222(1):429–450 Huggins et al [2019] Huggins W, Patil P, Mitchell B, et al (2019) Towards quantum machine learning with tensor networks. Quantum Science and technology 4(2):024001 Kim [2019] Kim S (2019) Enhancing the momentum strategy through deep regression. Quantitative Finance 19(7):1121–1133 Lim et al [2019] Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Gu S, Kelly B, Xiu D (2020) Empirical Asset Pricing via Machine Learning. The Review of Financial Studies 33(5):2223–2273. 10.1093/rfs/hhaa009, URL https://doi.org/10.1093/rfs/hhaa009, https://academic.oup.com/rfs/article-pdf/33/5/2223/33209812/hhaa009.pdf Gu et al [2021] Gu S, Kelly B, Xiu D (2021) Autoencoder asset pricing models. Journal of Econometrics 222(1):429–450 Huggins et al [2019] Huggins W, Patil P, Mitchell B, et al (2019) Towards quantum machine learning with tensor networks. Quantum Science and technology 4(2):024001 Kim [2019] Kim S (2019) Enhancing the momentum strategy through deep regression. Quantitative Finance 19(7):1121–1133 Lim et al [2019] Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Gu S, Kelly B, Xiu D (2021) Autoencoder asset pricing models. Journal of Econometrics 222(1):429–450 Huggins et al [2019] Huggins W, Patil P, Mitchell B, et al (2019) Towards quantum machine learning with tensor networks. Quantum Science and technology 4(2):024001 Kim [2019] Kim S (2019) Enhancing the momentum strategy through deep regression. Quantitative Finance 19(7):1121–1133 Lim et al [2019] Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Huggins W, Patil P, Mitchell B, et al (2019) Towards quantum machine learning with tensor networks. Quantum Science and technology 4(2):024001 Kim [2019] Kim S (2019) Enhancing the momentum strategy through deep regression. Quantitative Finance 19(7):1121–1133 Lim et al [2019] Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Kim S (2019) Enhancing the momentum strategy through deep regression. Quantitative Finance 19(7):1121–1133 Lim et al [2019] Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863
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Journal of Finance 47(2):427–465 Fannes et al [1992] Fannes M, Nachtergaele B, Werner RF (1992) Finitely correlated states on quantum spin chains. Communications in mathematical physics 144(3):443–490 Gu et al [2020] Gu S, Kelly B, Xiu D (2020) Empirical Asset Pricing via Machine Learning. The Review of Financial Studies 33(5):2223–2273. 10.1093/rfs/hhaa009, URL https://doi.org/10.1093/rfs/hhaa009, https://academic.oup.com/rfs/article-pdf/33/5/2223/33209812/hhaa009.pdf Gu et al [2021] Gu S, Kelly B, Xiu D (2021) Autoencoder asset pricing models. Journal of Econometrics 222(1):429–450 Huggins et al [2019] Huggins W, Patil P, Mitchell B, et al (2019) Towards quantum machine learning with tensor networks. Quantum Science and technology 4(2):024001 Kim [2019] Kim S (2019) Enhancing the momentum strategy through deep regression. Quantitative Finance 19(7):1121–1133 Lim et al [2019] Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Efthymiou S, Hidary J, Leichenauer S (2019) Tensornetwork for machine learning. arXiv preprint arXiv:190606329 Eugene and French [1992] Eugene F, French K (1992) The cross-section of expected stock returns. Journal of Finance 47(2):427–465 Fannes et al [1992] Fannes M, Nachtergaele B, Werner RF (1992) Finitely correlated states on quantum spin chains. Communications in mathematical physics 144(3):443–490 Gu et al [2020] Gu S, Kelly B, Xiu D (2020) Empirical Asset Pricing via Machine Learning. The Review of Financial Studies 33(5):2223–2273. 10.1093/rfs/hhaa009, URL https://doi.org/10.1093/rfs/hhaa009, https://academic.oup.com/rfs/article-pdf/33/5/2223/33209812/hhaa009.pdf Gu et al [2021] Gu S, Kelly B, Xiu D (2021) Autoencoder asset pricing models. Journal of Econometrics 222(1):429–450 Huggins et al [2019] Huggins W, Patil P, Mitchell B, et al (2019) Towards quantum machine learning with tensor networks. Quantum Science and technology 4(2):024001 Kim [2019] Kim S (2019) Enhancing the momentum strategy through deep regression. Quantitative Finance 19(7):1121–1133 Lim et al [2019] Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Eugene F, French K (1992) The cross-section of expected stock returns. Journal of Finance 47(2):427–465 Fannes et al [1992] Fannes M, Nachtergaele B, Werner RF (1992) Finitely correlated states on quantum spin chains. Communications in mathematical physics 144(3):443–490 Gu et al [2020] Gu S, Kelly B, Xiu D (2020) Empirical Asset Pricing via Machine Learning. The Review of Financial Studies 33(5):2223–2273. 10.1093/rfs/hhaa009, URL https://doi.org/10.1093/rfs/hhaa009, https://academic.oup.com/rfs/article-pdf/33/5/2223/33209812/hhaa009.pdf Gu et al [2021] Gu S, Kelly B, Xiu D (2021) Autoencoder asset pricing models. Journal of Econometrics 222(1):429–450 Huggins et al [2019] Huggins W, Patil P, Mitchell B, et al (2019) Towards quantum machine learning with tensor networks. Quantum Science and technology 4(2):024001 Kim [2019] Kim S (2019) Enhancing the momentum strategy through deep regression. Quantitative Finance 19(7):1121–1133 Lim et al [2019] Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Fannes M, Nachtergaele B, Werner RF (1992) Finitely correlated states on quantum spin chains. Communications in mathematical physics 144(3):443–490 Gu et al [2020] Gu S, Kelly B, Xiu D (2020) Empirical Asset Pricing via Machine Learning. The Review of Financial Studies 33(5):2223–2273. 10.1093/rfs/hhaa009, URL https://doi.org/10.1093/rfs/hhaa009, https://academic.oup.com/rfs/article-pdf/33/5/2223/33209812/hhaa009.pdf Gu et al [2021] Gu S, Kelly B, Xiu D (2021) Autoencoder asset pricing models. Journal of Econometrics 222(1):429–450 Huggins et al [2019] Huggins W, Patil P, Mitchell B, et al (2019) Towards quantum machine learning with tensor networks. Quantum Science and technology 4(2):024001 Kim [2019] Kim S (2019) Enhancing the momentum strategy through deep regression. Quantitative Finance 19(7):1121–1133 Lim et al [2019] Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Gu S, Kelly B, Xiu D (2020) Empirical Asset Pricing via Machine Learning. The Review of Financial Studies 33(5):2223–2273. 10.1093/rfs/hhaa009, URL https://doi.org/10.1093/rfs/hhaa009, https://academic.oup.com/rfs/article-pdf/33/5/2223/33209812/hhaa009.pdf Gu et al [2021] Gu S, Kelly B, Xiu D (2021) Autoencoder asset pricing models. Journal of Econometrics 222(1):429–450 Huggins et al [2019] Huggins W, Patil P, Mitchell B, et al (2019) Towards quantum machine learning with tensor networks. Quantum Science and technology 4(2):024001 Kim [2019] Kim S (2019) Enhancing the momentum strategy through deep regression. Quantitative Finance 19(7):1121–1133 Lim et al [2019] Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Gu S, Kelly B, Xiu D (2021) Autoencoder asset pricing models. Journal of Econometrics 222(1):429–450 Huggins et al [2019] Huggins W, Patil P, Mitchell B, et al (2019) Towards quantum machine learning with tensor networks. Quantum Science and technology 4(2):024001 Kim [2019] Kim S (2019) Enhancing the momentum strategy through deep regression. Quantitative Finance 19(7):1121–1133 Lim et al [2019] Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Huggins W, Patil P, Mitchell B, et al (2019) Towards quantum machine learning with tensor networks. Quantum Science and technology 4(2):024001 Kim [2019] Kim S (2019) Enhancing the momentum strategy through deep regression. Quantitative Finance 19(7):1121–1133 Lim et al [2019] Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Kim S (2019) Enhancing the momentum strategy through deep regression. Quantitative Finance 19(7):1121–1133 Lim et al [2019] Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863
- Duan J, Kashima H (2021) Learning to rank for multi-step ahead time-series forecasting. IEEE Access 9:49372–49386 Efthymiou et al [2019] Efthymiou S, Hidary J, Leichenauer S (2019) Tensornetwork for machine learning. arXiv preprint arXiv:190606329 Eugene and French [1992] Eugene F, French K (1992) The cross-section of expected stock returns. Journal of Finance 47(2):427–465 Fannes et al [1992] Fannes M, Nachtergaele B, Werner RF (1992) Finitely correlated states on quantum spin chains. Communications in mathematical physics 144(3):443–490 Gu et al [2020] Gu S, Kelly B, Xiu D (2020) Empirical Asset Pricing via Machine Learning. The Review of Financial Studies 33(5):2223–2273. 10.1093/rfs/hhaa009, URL https://doi.org/10.1093/rfs/hhaa009, https://academic.oup.com/rfs/article-pdf/33/5/2223/33209812/hhaa009.pdf Gu et al [2021] Gu S, Kelly B, Xiu D (2021) Autoencoder asset pricing models. Journal of Econometrics 222(1):429–450 Huggins et al [2019] Huggins W, Patil P, Mitchell B, et al (2019) Towards quantum machine learning with tensor networks. Quantum Science and technology 4(2):024001 Kim [2019] Kim S (2019) Enhancing the momentum strategy through deep regression. Quantitative Finance 19(7):1121–1133 Lim et al [2019] Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Efthymiou S, Hidary J, Leichenauer S (2019) Tensornetwork for machine learning. arXiv preprint arXiv:190606329 Eugene and French [1992] Eugene F, French K (1992) The cross-section of expected stock returns. Journal of Finance 47(2):427–465 Fannes et al [1992] Fannes M, Nachtergaele B, Werner RF (1992) Finitely correlated states on quantum spin chains. Communications in mathematical physics 144(3):443–490 Gu et al [2020] Gu S, Kelly B, Xiu D (2020) Empirical Asset Pricing via Machine Learning. The Review of Financial Studies 33(5):2223–2273. 10.1093/rfs/hhaa009, URL https://doi.org/10.1093/rfs/hhaa009, https://academic.oup.com/rfs/article-pdf/33/5/2223/33209812/hhaa009.pdf Gu et al [2021] Gu S, Kelly B, Xiu D (2021) Autoencoder asset pricing models. Journal of Econometrics 222(1):429–450 Huggins et al [2019] Huggins W, Patil P, Mitchell B, et al (2019) Towards quantum machine learning with tensor networks. Quantum Science and technology 4(2):024001 Kim [2019] Kim S (2019) Enhancing the momentum strategy through deep regression. Quantitative Finance 19(7):1121–1133 Lim et al [2019] Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Eugene F, French K (1992) The cross-section of expected stock returns. Journal of Finance 47(2):427–465 Fannes et al [1992] Fannes M, Nachtergaele B, Werner RF (1992) Finitely correlated states on quantum spin chains. Communications in mathematical physics 144(3):443–490 Gu et al [2020] Gu S, Kelly B, Xiu D (2020) Empirical Asset Pricing via Machine Learning. The Review of Financial Studies 33(5):2223–2273. 10.1093/rfs/hhaa009, URL https://doi.org/10.1093/rfs/hhaa009, https://academic.oup.com/rfs/article-pdf/33/5/2223/33209812/hhaa009.pdf Gu et al [2021] Gu S, Kelly B, Xiu D (2021) Autoencoder asset pricing models. Journal of Econometrics 222(1):429–450 Huggins et al [2019] Huggins W, Patil P, Mitchell B, et al (2019) Towards quantum machine learning with tensor networks. Quantum Science and technology 4(2):024001 Kim [2019] Kim S (2019) Enhancing the momentum strategy through deep regression. Quantitative Finance 19(7):1121–1133 Lim et al [2019] Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Fannes M, Nachtergaele B, Werner RF (1992) Finitely correlated states on quantum spin chains. Communications in mathematical physics 144(3):443–490 Gu et al [2020] Gu S, Kelly B, Xiu D (2020) Empirical Asset Pricing via Machine Learning. The Review of Financial Studies 33(5):2223–2273. 10.1093/rfs/hhaa009, URL https://doi.org/10.1093/rfs/hhaa009, https://academic.oup.com/rfs/article-pdf/33/5/2223/33209812/hhaa009.pdf Gu et al [2021] Gu S, Kelly B, Xiu D (2021) Autoencoder asset pricing models. Journal of Econometrics 222(1):429–450 Huggins et al [2019] Huggins W, Patil P, Mitchell B, et al (2019) Towards quantum machine learning with tensor networks. Quantum Science and technology 4(2):024001 Kim [2019] Kim S (2019) Enhancing the momentum strategy through deep regression. Quantitative Finance 19(7):1121–1133 Lim et al [2019] Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Gu S, Kelly B, Xiu D (2020) Empirical Asset Pricing via Machine Learning. The Review of Financial Studies 33(5):2223–2273. 10.1093/rfs/hhaa009, URL https://doi.org/10.1093/rfs/hhaa009, https://academic.oup.com/rfs/article-pdf/33/5/2223/33209812/hhaa009.pdf Gu et al [2021] Gu S, Kelly B, Xiu D (2021) Autoencoder asset pricing models. Journal of Econometrics 222(1):429–450 Huggins et al [2019] Huggins W, Patil P, Mitchell B, et al (2019) Towards quantum machine learning with tensor networks. Quantum Science and technology 4(2):024001 Kim [2019] Kim S (2019) Enhancing the momentum strategy through deep regression. Quantitative Finance 19(7):1121–1133 Lim et al [2019] Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Gu S, Kelly B, Xiu D (2021) Autoencoder asset pricing models. Journal of Econometrics 222(1):429–450 Huggins et al [2019] Huggins W, Patil P, Mitchell B, et al (2019) Towards quantum machine learning with tensor networks. Quantum Science and technology 4(2):024001 Kim [2019] Kim S (2019) Enhancing the momentum strategy through deep regression. Quantitative Finance 19(7):1121–1133 Lim et al [2019] Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Huggins W, Patil P, Mitchell B, et al (2019) Towards quantum machine learning with tensor networks. Quantum Science and technology 4(2):024001 Kim [2019] Kim S (2019) Enhancing the momentum strategy through deep regression. Quantitative Finance 19(7):1121–1133 Lim et al [2019] Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Kim S (2019) Enhancing the momentum strategy through deep regression. Quantitative Finance 19(7):1121–1133 Lim et al [2019] Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863
- Efthymiou S, Hidary J, Leichenauer S (2019) Tensornetwork for machine learning. arXiv preprint arXiv:190606329 Eugene and French [1992] Eugene F, French K (1992) The cross-section of expected stock returns. Journal of Finance 47(2):427–465 Fannes et al [1992] Fannes M, Nachtergaele B, Werner RF (1992) Finitely correlated states on quantum spin chains. Communications in mathematical physics 144(3):443–490 Gu et al [2020] Gu S, Kelly B, Xiu D (2020) Empirical Asset Pricing via Machine Learning. The Review of Financial Studies 33(5):2223–2273. 10.1093/rfs/hhaa009, URL https://doi.org/10.1093/rfs/hhaa009, https://academic.oup.com/rfs/article-pdf/33/5/2223/33209812/hhaa009.pdf Gu et al [2021] Gu S, Kelly B, Xiu D (2021) Autoencoder asset pricing models. Journal of Econometrics 222(1):429–450 Huggins et al [2019] Huggins W, Patil P, Mitchell B, et al (2019) Towards quantum machine learning with tensor networks. Quantum Science and technology 4(2):024001 Kim [2019] Kim S (2019) Enhancing the momentum strategy through deep regression. Quantitative Finance 19(7):1121–1133 Lim et al [2019] Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Eugene F, French K (1992) The cross-section of expected stock returns. Journal of Finance 47(2):427–465 Fannes et al [1992] Fannes M, Nachtergaele B, Werner RF (1992) Finitely correlated states on quantum spin chains. Communications in mathematical physics 144(3):443–490 Gu et al [2020] Gu S, Kelly B, Xiu D (2020) Empirical Asset Pricing via Machine Learning. The Review of Financial Studies 33(5):2223–2273. 10.1093/rfs/hhaa009, URL https://doi.org/10.1093/rfs/hhaa009, https://academic.oup.com/rfs/article-pdf/33/5/2223/33209812/hhaa009.pdf Gu et al [2021] Gu S, Kelly B, Xiu D (2021) Autoencoder asset pricing models. Journal of Econometrics 222(1):429–450 Huggins et al [2019] Huggins W, Patil P, Mitchell B, et al (2019) Towards quantum machine learning with tensor networks. Quantum Science and technology 4(2):024001 Kim [2019] Kim S (2019) Enhancing the momentum strategy through deep regression. Quantitative Finance 19(7):1121–1133 Lim et al [2019] Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Fannes M, Nachtergaele B, Werner RF (1992) Finitely correlated states on quantum spin chains. Communications in mathematical physics 144(3):443–490 Gu et al [2020] Gu S, Kelly B, Xiu D (2020) Empirical Asset Pricing via Machine Learning. The Review of Financial Studies 33(5):2223–2273. 10.1093/rfs/hhaa009, URL https://doi.org/10.1093/rfs/hhaa009, https://academic.oup.com/rfs/article-pdf/33/5/2223/33209812/hhaa009.pdf Gu et al [2021] Gu S, Kelly B, Xiu D (2021) Autoencoder asset pricing models. Journal of Econometrics 222(1):429–450 Huggins et al [2019] Huggins W, Patil P, Mitchell B, et al (2019) Towards quantum machine learning with tensor networks. Quantum Science and technology 4(2):024001 Kim [2019] Kim S (2019) Enhancing the momentum strategy through deep regression. Quantitative Finance 19(7):1121–1133 Lim et al [2019] Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Gu S, Kelly B, Xiu D (2020) Empirical Asset Pricing via Machine Learning. The Review of Financial Studies 33(5):2223–2273. 10.1093/rfs/hhaa009, URL https://doi.org/10.1093/rfs/hhaa009, https://academic.oup.com/rfs/article-pdf/33/5/2223/33209812/hhaa009.pdf Gu et al [2021] Gu S, Kelly B, Xiu D (2021) Autoencoder asset pricing models. Journal of Econometrics 222(1):429–450 Huggins et al [2019] Huggins W, Patil P, Mitchell B, et al (2019) Towards quantum machine learning with tensor networks. Quantum Science and technology 4(2):024001 Kim [2019] Kim S (2019) Enhancing the momentum strategy through deep regression. Quantitative Finance 19(7):1121–1133 Lim et al [2019] Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Gu S, Kelly B, Xiu D (2021) Autoencoder asset pricing models. Journal of Econometrics 222(1):429–450 Huggins et al [2019] Huggins W, Patil P, Mitchell B, et al (2019) Towards quantum machine learning with tensor networks. Quantum Science and technology 4(2):024001 Kim [2019] Kim S (2019) Enhancing the momentum strategy through deep regression. Quantitative Finance 19(7):1121–1133 Lim et al [2019] Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Huggins W, Patil P, Mitchell B, et al (2019) Towards quantum machine learning with tensor networks. Quantum Science and technology 4(2):024001 Kim [2019] Kim S (2019) Enhancing the momentum strategy through deep regression. Quantitative Finance 19(7):1121–1133 Lim et al [2019] Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Kim S (2019) Enhancing the momentum strategy through deep regression. Quantitative Finance 19(7):1121–1133 Lim et al [2019] Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863
- Eugene F, French K (1992) The cross-section of expected stock returns. Journal of Finance 47(2):427–465 Fannes et al [1992] Fannes M, Nachtergaele B, Werner RF (1992) Finitely correlated states on quantum spin chains. Communications in mathematical physics 144(3):443–490 Gu et al [2020] Gu S, Kelly B, Xiu D (2020) Empirical Asset Pricing via Machine Learning. The Review of Financial Studies 33(5):2223–2273. 10.1093/rfs/hhaa009, URL https://doi.org/10.1093/rfs/hhaa009, https://academic.oup.com/rfs/article-pdf/33/5/2223/33209812/hhaa009.pdf Gu et al [2021] Gu S, Kelly B, Xiu D (2021) Autoencoder asset pricing models. Journal of Econometrics 222(1):429–450 Huggins et al [2019] Huggins W, Patil P, Mitchell B, et al (2019) Towards quantum machine learning with tensor networks. Quantum Science and technology 4(2):024001 Kim [2019] Kim S (2019) Enhancing the momentum strategy through deep regression. Quantitative Finance 19(7):1121–1133 Lim et al [2019] Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Fannes M, Nachtergaele B, Werner RF (1992) Finitely correlated states on quantum spin chains. Communications in mathematical physics 144(3):443–490 Gu et al [2020] Gu S, Kelly B, Xiu D (2020) Empirical Asset Pricing via Machine Learning. The Review of Financial Studies 33(5):2223–2273. 10.1093/rfs/hhaa009, URL https://doi.org/10.1093/rfs/hhaa009, https://academic.oup.com/rfs/article-pdf/33/5/2223/33209812/hhaa009.pdf Gu et al [2021] Gu S, Kelly B, Xiu D (2021) Autoencoder asset pricing models. Journal of Econometrics 222(1):429–450 Huggins et al [2019] Huggins W, Patil P, Mitchell B, et al (2019) Towards quantum machine learning with tensor networks. Quantum Science and technology 4(2):024001 Kim [2019] Kim S (2019) Enhancing the momentum strategy through deep regression. Quantitative Finance 19(7):1121–1133 Lim et al [2019] Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Gu S, Kelly B, Xiu D (2020) Empirical Asset Pricing via Machine Learning. The Review of Financial Studies 33(5):2223–2273. 10.1093/rfs/hhaa009, URL https://doi.org/10.1093/rfs/hhaa009, https://academic.oup.com/rfs/article-pdf/33/5/2223/33209812/hhaa009.pdf Gu et al [2021] Gu S, Kelly B, Xiu D (2021) Autoencoder asset pricing models. Journal of Econometrics 222(1):429–450 Huggins et al [2019] Huggins W, Patil P, Mitchell B, et al (2019) Towards quantum machine learning with tensor networks. Quantum Science and technology 4(2):024001 Kim [2019] Kim S (2019) Enhancing the momentum strategy through deep regression. Quantitative Finance 19(7):1121–1133 Lim et al [2019] Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Gu S, Kelly B, Xiu D (2021) Autoencoder asset pricing models. Journal of Econometrics 222(1):429–450 Huggins et al [2019] Huggins W, Patil P, Mitchell B, et al (2019) Towards quantum machine learning with tensor networks. Quantum Science and technology 4(2):024001 Kim [2019] Kim S (2019) Enhancing the momentum strategy through deep regression. Quantitative Finance 19(7):1121–1133 Lim et al [2019] Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Huggins W, Patil P, Mitchell B, et al (2019) Towards quantum machine learning with tensor networks. Quantum Science and technology 4(2):024001 Kim [2019] Kim S (2019) Enhancing the momentum strategy through deep regression. Quantitative Finance 19(7):1121–1133 Lim et al [2019] Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Kim S (2019) Enhancing the momentum strategy through deep regression. Quantitative Finance 19(7):1121–1133 Lim et al [2019] Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863
- Fannes M, Nachtergaele B, Werner RF (1992) Finitely correlated states on quantum spin chains. Communications in mathematical physics 144(3):443–490 Gu et al [2020] Gu S, Kelly B, Xiu D (2020) Empirical Asset Pricing via Machine Learning. The Review of Financial Studies 33(5):2223–2273. 10.1093/rfs/hhaa009, URL https://doi.org/10.1093/rfs/hhaa009, https://academic.oup.com/rfs/article-pdf/33/5/2223/33209812/hhaa009.pdf Gu et al [2021] Gu S, Kelly B, Xiu D (2021) Autoencoder asset pricing models. Journal of Econometrics 222(1):429–450 Huggins et al [2019] Huggins W, Patil P, Mitchell B, et al (2019) Towards quantum machine learning with tensor networks. Quantum Science and technology 4(2):024001 Kim [2019] Kim S (2019) Enhancing the momentum strategy through deep regression. Quantitative Finance 19(7):1121–1133 Lim et al [2019] Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Gu S, Kelly B, Xiu D (2020) Empirical Asset Pricing via Machine Learning. The Review of Financial Studies 33(5):2223–2273. 10.1093/rfs/hhaa009, URL https://doi.org/10.1093/rfs/hhaa009, https://academic.oup.com/rfs/article-pdf/33/5/2223/33209812/hhaa009.pdf Gu et al [2021] Gu S, Kelly B, Xiu D (2021) Autoencoder asset pricing models. Journal of Econometrics 222(1):429–450 Huggins et al [2019] Huggins W, Patil P, Mitchell B, et al (2019) Towards quantum machine learning with tensor networks. Quantum Science and technology 4(2):024001 Kim [2019] Kim S (2019) Enhancing the momentum strategy through deep regression. Quantitative Finance 19(7):1121–1133 Lim et al [2019] Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Gu S, Kelly B, Xiu D (2021) Autoencoder asset pricing models. Journal of Econometrics 222(1):429–450 Huggins et al [2019] Huggins W, Patil P, Mitchell B, et al (2019) Towards quantum machine learning with tensor networks. Quantum Science and technology 4(2):024001 Kim [2019] Kim S (2019) Enhancing the momentum strategy through deep regression. Quantitative Finance 19(7):1121–1133 Lim et al [2019] Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Huggins W, Patil P, Mitchell B, et al (2019) Towards quantum machine learning with tensor networks. Quantum Science and technology 4(2):024001 Kim [2019] Kim S (2019) Enhancing the momentum strategy through deep regression. Quantitative Finance 19(7):1121–1133 Lim et al [2019] Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Kim S (2019) Enhancing the momentum strategy through deep regression. Quantitative Finance 19(7):1121–1133 Lim et al [2019] Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. 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International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863
- Gu S, Kelly B, Xiu D (2020) Empirical Asset Pricing via Machine Learning. The Review of Financial Studies 33(5):2223–2273. 10.1093/rfs/hhaa009, URL https://doi.org/10.1093/rfs/hhaa009, https://academic.oup.com/rfs/article-pdf/33/5/2223/33209812/hhaa009.pdf Gu et al [2021] Gu S, Kelly B, Xiu D (2021) Autoencoder asset pricing models. Journal of Econometrics 222(1):429–450 Huggins et al [2019] Huggins W, Patil P, Mitchell B, et al (2019) Towards quantum machine learning with tensor networks. Quantum Science and technology 4(2):024001 Kim [2019] Kim S (2019) Enhancing the momentum strategy through deep regression. Quantitative Finance 19(7):1121–1133 Lim et al [2019] Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Gu S, Kelly B, Xiu D (2021) Autoencoder asset pricing models. Journal of Econometrics 222(1):429–450 Huggins et al [2019] Huggins W, Patil P, Mitchell B, et al (2019) Towards quantum machine learning with tensor networks. Quantum Science and technology 4(2):024001 Kim [2019] Kim S (2019) Enhancing the momentum strategy through deep regression. Quantitative Finance 19(7):1121–1133 Lim et al [2019] Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Huggins W, Patil P, Mitchell B, et al (2019) Towards quantum machine learning with tensor networks. Quantum Science and technology 4(2):024001 Kim [2019] Kim S (2019) Enhancing the momentum strategy through deep regression. Quantitative Finance 19(7):1121–1133 Lim et al [2019] Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Kim S (2019) Enhancing the momentum strategy through deep regression. Quantitative Finance 19(7):1121–1133 Lim et al [2019] Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863
- Gu S, Kelly B, Xiu D (2021) Autoencoder asset pricing models. Journal of Econometrics 222(1):429–450 Huggins et al [2019] Huggins W, Patil P, Mitchell B, et al (2019) Towards quantum machine learning with tensor networks. Quantum Science and technology 4(2):024001 Kim [2019] Kim S (2019) Enhancing the momentum strategy through deep regression. Quantitative Finance 19(7):1121–1133 Lim et al [2019] Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. 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Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Huggins W, Patil P, Mitchell B, et al (2019) Towards quantum machine learning with tensor networks. Quantum Science and technology 4(2):024001 Kim [2019] Kim S (2019) Enhancing the momentum strategy through deep regression. Quantitative Finance 19(7):1121–1133 Lim et al [2019] Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Kim S (2019) Enhancing the momentum strategy through deep regression. Quantitative Finance 19(7):1121–1133 Lim et al [2019] Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863
- Huggins W, Patil P, Mitchell B, et al (2019) Towards quantum machine learning with tensor networks. Quantum Science and technology 4(2):024001 Kim [2019] Kim S (2019) Enhancing the momentum strategy through deep regression. Quantitative Finance 19(7):1121–1133 Lim et al [2019] Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Kim S (2019) Enhancing the momentum strategy through deep regression. Quantitative Finance 19(7):1121–1133 Lim et al [2019] Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863
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Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. 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Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863
- Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4):19–38 Madsen et al [2022] Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863
- Madsen LS, Laudenbach F, Askarani MF, et al (2022) Quantum computational advantage with a programmable photonic processor. Nature 606(7912):75–81 Mitarai et al [2018] Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863
- Mitarai K, Negoro M, Kitagawa M, et al (2018) Quantum circuit learning. Physical Review A 98(3):032309 Nakagawa et al [2020] Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863
- Nakagawa K, Abe M, Komiyama J (2020) Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 370–379 Novikov et al [2016] Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. 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International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863
- Novikov A, Trofimov M, Oseledets I (2016) Exponential machines. arXiv preprint arXiv:160503795 Poh et al [2022] Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863
- Poh D, Lim B, Zohren S, et al (2022) Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. The Journal of Financial Data Science 4(3):89–107. 10.3905/jfds.2022.1.099, URL https://jfds.pm-research.com/content/4/3/89, https://jfds.pm-research.com/content/4/3/89.full.pdf Preskill [2018] Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863
- Preskill J (2018) Quantum Computing in the NISQ era and beyond. Quantum 2:79. 10.22331/q-2018-08-06-79, URL https://doi.org/10.22331/q-2018-08-06-79 Roberts et al [2019] Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863
- Roberts C, Milsted A, Ganahl M, et al (2019) Tensornetwork: A library for physics and machine learning. 1905.01330 Schuld et al [2019] Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863
- Schuld M, Bergholm V, Gogolin C, et al (2019) Evaluating analytic gradients on quantum hardware. Physical Review A 99(3):032331 Stoudenmire and Schwab [2016] Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863
- Stoudenmire E, Schwab DJ (2016) Supervised learning with tensor networks. Advances in Neural Information Processing Systems 29 Stoudenmire [2018] Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863
- Stoudenmire EM (2018) Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology 3(3):034003 Suimon et al [2020] Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863
- Suimon Y, Sakaji H, Izumi K, et al (2020) Japanese interest rate forecast considering the linkage of global markets using machine learning methods. International Journal of Smart Computing and Artificial Intelligence 4(1):1–17. 10.52731/ijscai.v4.i1.500 Suzuki et al [2020] Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863
- Suzuki Y, Kawase Y, Masumura Y, et al (2020) Qulacs: a fast and versatile quantum circuit simulator for research purpose. arXiv preprint arXiv:201113524 Takaki et al [2021] Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863
- Takaki Y, Mitarai K, Negoro M, et al (2021) Learning temporal data with a variational quantum recurrent neural network. Physical Review A 103(5):052414 White [1992] White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863 White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863
- White SR (1992) Density matrix formulation for quantum renormalization groups. Physical review letters 69(19):2863