Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
129 tokens/sec
GPT-4o
28 tokens/sec
Gemini 2.5 Pro Pro
42 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Élivágar: Efficient Quantum Circuit Search for Classification (2401.09393v1)

Published 17 Jan 2024 in quant-ph, cs.AR, and cs.LG

Abstract: Designing performant and noise-robust circuits for Quantum Machine Learning (QML) is challenging -- the design space scales exponentially with circuit size, and there are few well-supported guiding principles for QML circuit design. Although recent Quantum Circuit Search (QCS) methods attempt to search for performant QML circuits that are also robust to hardware noise, they directly adopt designs from classical Neural Architecture Search (NAS) that are misaligned with the unique constraints of quantum hardware, resulting in high search overheads and severe performance bottlenecks. We present \'Eliv\'agar, a novel resource-efficient, noise-guided QCS framework. \'Eliv\'agar innovates in all three major aspects of QCS -- search space, search algorithm and candidate evaluation strategy -- to address the design flaws in current classically-inspired QCS methods. \'Eliv\'agar achieves hardware-efficiency and avoids an expensive circuit-mapping co-search via noise- and device topology-aware candidate generation. By introducing two cheap-to-compute predictors, Clifford noise resilience and Representational capacity, \'Eliv\'agar decouples the evaluation of noise robustness and performance, enabling early rejection of low-fidelity circuits and reducing circuit evaluation costs. Due to its resource-efficiency, \'Eliv\'agar can further search for data embeddings, significantly improving performance. Based on a comprehensive evaluation of \'Eliv\'agar on 12 real quantum devices and 9 QML applications, \'Eliv\'agar achieves 5.3% higher accuracy and a 271$\times$ speedup compared to state-of-the-art QCS methods.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (97)
  1. The power of quantum neural networks. Nature Computational Science, 1(6):403–409, Jun 2021.
  2. Designing neural network architectures using reinforcement learning. In 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24-26, 2017, Conference Track Proceedings, 2017.
  3. Time-sliced quantum circuit partitioning for modular architectures. In Computing Frontiers, pages 98–107, 2020.
  4. Automatic differentiation in machine learning: a survey. Journal of Machine Learning Research, 18(153):1–43, 2018.
  5. Parameterized quantum circuits as machine learning models. Quantum Science and Technology, 4(4):043001, Nov 2019.
  6. Pennylane: Automatic differentiation of hybrid quantum-classical computations. arXiv preprint arXiv:1811.04968, 2018.
  7. Quantum machine learning. Nature, 549(7671):195–202, 2017.
  8. Improved classical simulation of quantum circuits dominated by clifford gates. Phys. Rev. Lett., 116:250501, Jun 2016.
  9. Bandwidth enables generalization in quantum kernel models. Transactions on Machine Learning Research, 2023.
  10. Encoding-dependent generalization bounds for parametrized quantum circuits. Quantum, 5:582, nov 2021.
  11. Variational quantum algorithms. Nature Reviews Physics, 3(9):625–644, 2021.
  12. Cost function dependent barren plateaus in shallow parametrized quantum circuits. Nature Communications, 12(1), mar 2021.
  13. Adapt: Mitigating idling errors in qubits via adaptive dynamical decoupling. In MICRO, 2021.
  14. The imitation game: Leveraging copycats for robust native gate selection in nisq programs. In HPCA 2023, 2022.
  15. ADAPT: Mitigating idling errors in qubits via adaptive dynamical decoupling. In MICRO-54: 54th Annual IEEE/ACM International Symposium on Microarchitecture. ACM, oct 2021.
  16. Jigsaw:boosting fidelity of nisq programs via measurement subsetting. In MICRO, 2021.
  17. Xuanyi Dong and Yi Yang. Searching for a robust neural architecture in four gpu hours. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 1761–1770, 2019.
  18. Quantum circuit architecture search for variational quantum algorithms. npj Quantum Information, 8(1):62, May 2022.
  19. The randomized measurement toolbox. Nature Reviews Physics, 5(1):9–24, Jan 2023.
  20. Classification with quantum neural networks on near term processors. arXiv preprint arXiv:1802.06002, 2018.
  21. Covariant quantum kernels for data with group structure. Bulletin of the American Physical Society, 2022.
  22. Optimized quantum compilation for near-term algorithms with openpulse. In MICRO, pages 186–200. IEEE, 2020.
  23. Quantum machine learning of large datasets using randomized measurements. Machine Learning: Science and Technology, 4(1):015005, jan 2023.
  24. Supervised learning with quantum-enhanced feature spaces. Nature, 567(7747):209–212, mar 2019.
  25. Quantum architecture search with meta-learning. Advanced Quantum Technologies, 5(8):2100134, 2022.
  26. Experimental quantum generative adversarial networks for image generation. Physical Review Applied, 16(2), aug 2021.
  27. Quantum advantage in learning from experiments. Science, 376(6598):1182–1186, jun 2022.
  28. Power of data in quantum machine learning. Nature Comms., 12(1):2631, 2021.
  29. Predicting many properties of a quantum system from very few measurements. Nature Physics, 16(10):1050–1057, jun 2020.
  30. Robust resource-efficient quantum variational ansatz through an evolutionary algorithm. Physical Review A, 105(5), may 2022.
  31. Exact and practical pattern matching for quantum circuit optimization. ACM Transactions on Quantum Computing, 3(1), jan 2022.
  32. Randomized benchmarking of quantum gates. Physical Review A, 77(1), jan 2008.
  33. The inductive bias of quantum kernels. In A. Beygelzimer, Y. Dauphin, P. Liang, and J. Wortman Vaughan, editors, Advances in Neural Information Processing Systems, 2021.
  34. Theory of overparametrization in quantum neural networks. Nature Computational Science, 3(6):542–551, Jun 2023.
  35. Tackling the qubit mapping problem for nisq-era quantum devices. In Proceedings of the Twenty-Fourth International Conference on Architectural Support for Programming Languages and Operating Systems, ASPLOS ’19, page 1001–1014, New York, NY, USA, 2019. Association for Computing Machinery.
  36. Random search and reproducibility for neural architecture search. In Ryan P. Adams and Vibhav Gogate, editors, Proceedings of The 35th Uncertainty in Artificial Intelligence Conference, volume 115 of Proceedings of Machine Learning Research, pages 367–377. PMLR, 22–25 Jul 2020.
  37. Pan: Pulse ansatz on nisq machines. arXiv preprint arXiv:2208.01215, 2022.
  38. DARTS: Differentiable architecture search. In International Conference on Learning Representations, 2019.
  39. Relaxed peephole optimization: A novel compiler optimization for quantum circuits. In 2021 IEEE/ACM International Symposium on Code Generation and Optimization (CGO), pages 301–314. IEEE, 2021.
  40. Not all swaps have the same cost: A case for optimization-aware qubit routing. In 2022 IEEE International Symposium on High-Performance Computer Architecture (HPCA), pages 709–725. IEEE, 2022.
  41. A rigorous and robust quantum speed-up in supervised machine learning. Nature Physics, 17(9):1013–1017, jul 2021.
  42. Quantum embeddings for machine learning. arXiv preprint arXiv:2001.03622, 2020.
  43. Volker Lohweg. banknote authentication. UCI Machine Learning Repository, 2013. DOI: https://doi.org/10.24432/C55P57.
  44. Determining the minimal number of swap gates for multi-dimensional nearest neighbor quantum circuits. In ASPDAC, pages 178–183. IEEE, 2015.
  45. Barren plateaus in quantum neural network training landscapes. Nature Communications, 9(1), Nov 2018.
  46. Pansatz: Pulse-based ansatz for variational quantum algorithms. arXiv preprint arXiv:2212.12911, 2022.
  47. Noise-adaptive compiler mappings for noisy intermediate-scale quantum computers. In ASPLOS, 2019.
  48. Software mitigation of crosstalk on noisy intermediate-scale quantum computers. In Proceedings of the Twenty-Fifth International Conference on Architectural Support for Programming Languages and Operating Systems. ACM, mar 2020.
  49. Automated optimization of large quantum circuits with continuous parameters. npj Quantum Information, 4(1):23, 2018.
  50. Optimal qubit assignment and routing via integer programming. ACM Transactions on Quantum Computing, 4(1), oct 2022.
  51. Theory for equivariant quantum neural networks. arXiv preprint arXiv:2210.08566, 2022.
  52. Quantum Computation and Quantum Information: 10th Anniversary Edition. Cambridge University Press, New York, NY, USA, 10th edition, 2011.
  53. Evaluating efficient performance estimators of neural architectures. In A. Beygelzimer, Y. Dauphin, P. Liang, and J. Wortman Vaughan, editors, Advances in Neural Information Processing Systems, 2021.
  54. Entangling quantum generative adversarial networks. Phys. Rev. Lett., 128:220505, Jun 2022.
  55. Greedy randomized search for scalable compilation of quantum circuits. In International Conference on the Integration of Constraint Programming, Artificial Intelligence, and Operations Research, pages 446–461. Springer, 2018.
  56. Geyser: a compilation framework for quantum computing with neutral atoms. In Proceedings of the 49th Annual International Symposium on Computer Architecture, pages 383–395, 2022.
  57. Optic: A practical quantum binary classifier for near-term quantum computers. In Proceedings of the 2022 Conference and Exhibition on Design, Automation and Test in Europe, DATE ’22, page 334–339, Leuven, BEL, 2022. European Design and Automation Association.
  58. Disq: A novel quantum output state classification method on ibm quantum computers using openpulse. In Proceedings of the 39th International Conference on Computer-Aided Design, ICCAD ’20, New York, NY, USA, 2020. Association for Computing Machinery.
  59. Qraft: reverse your quantum circuit and know the correct program output. In Tim Sherwood, Emery D. Berger, and Christos Kozyrakis, editors, ASPLOS ’21: 26th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Virtual Event, USA, April 19-23, 2021, pages 443–455. ACM, 2021.
  60. Quest: systematically approximating quantum circuits for higher output fidelity. In Proceedings of the 27th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, pages 514–528, 2022.
  61. Robust quantum circuit approximation for resource-efficient circuit synthesis. Bulletin of the American Physical Society, 2022.
  62. Generalization despite overfitting in quantum machine learning models. arXiv preprint arXiv:2209.05523, 2022.
  63. Optimal synthesis into fixed xx interactions. Quantum, 6:696, 2022.
  64. Efficient neural architecture search via parameters sharing. In Jennifer Dy and Andreas Krause, editors, Proceedings of the 35th International Conference on Machine Learning, volume 80 of Proceedings of Machine Learning Research, pages 4095–4104. PMLR, 10–15 Jul 2018.
  65. John Preskill. Quantum Computing in the NISQ era and beyond. Quantum, 2:79, August 2018.
  66. Qtn-vqc: An end-to-end learning framework for quantum neural networks. arXiv preprint arXiv:2110.03861, 2021.
  67. Boosting quantum fidelity with an ordered diverse ensemble of clifford canary circuits. Bulletin of the American Physical Society, 2022.
  68. Cafqa: A classical simulation bootstrap for variational quantum algorithms. In Proceedings of the 28th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 1, ASPLOS 2023, page 15–29, New York, NY, USA, 2022. Association for Computing Machinery.
  69. Regularized evolution for image classifier architecture search. In Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence and Thirty-First Innovative Applications of Artificial Intelligence Conference and Ninth AAAI Symposium on Educational Advances in Artificial Intelligence, AAAI’19/IAAI’19/EAAI’19. AAAI Press, 2019.
  70. Generation of high-resolution handwritten digits with an ion-trap quantum computer. Phys. Rev. X, 12:031010, Jul 2022.
  71. Evaluating analytic gradients on quantum hardware. Physical Review A, 99(3), mar 2019.
  72. Quantum Models as Kernel Methods, pages 217–245. Springer International Publishing, Cham, 2021.
  73. Effect of data encoding on the expressive power of variational quantum-machine-learning models. Physical Review A, 103(3), mar 2021.
  74. Optimized compilation of aggregated instructions for realistic quantum computers. In ASPLOS, 2019.
  75. Quilt: Effective multi-class classification on quantum computers using an ensemble of diverse quantum classifiers. Proceedings of the AAAI Conference on Artificial Intelligence, 36(8):8324–8332, Jun. 2022.
  76. Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies, 2(12):1900070, Oct 2019.
  77. Error mitigation in quantum computers through instruction scheduling. arXiv preprint arXiv:2105.01760, 2021.
  78. HAMMER: boosting fidelity of noisy quantum circuits by exploiting hamming behavior of erroneous outcomes. In Proceedings of the 27th ACM International Conference on Architectural Support for Programming Languages and Operating Systems. ACM, feb 2022.
  79. Not all qubits are created equal: a case for variability-aware policies for nisq-era quantum computers. In ASPLOS, 2019.
  80. Quantumnas: Noise-adaptive search for robust quantum circuits. In 2022 IEEE International Symposium on High-Performance Computer Architecture (HPCA), pages 692–708, Los Alamitos, CA, USA, apr 2022. IEEE Computer Society.
  81. Roqnn: Noise-aware training for robust quantum neural networks. arXiv preprint arXiv:2110.11331, 2021.
  82. Quantumnat: Quantum noise-aware training with noise injection, quantization and normalization. In Proceedings of the 59th ACM/IEEE Design Automation Conference, DAC ’22, page 1–6, New York, NY, USA, 2022. Association for Computing Machinery.
  83. Qoc: Quantum on-chip training with parameter shift and gradient pruning. In Proceedings of the 59th ACM/IEEE Design Automation Conference, DAC ’22, page 655–660, New York, NY, USA, 2022. Association for Computing Machinery.
  84. Noise-induced barren plateaus in variational quantum algorithms. Nature Communications, 12(1):6961, Nov 2021.
  85. Towards understanding the power of quantum kernels in the NISQ era. Quantum, 5:531, aug 2021.
  86. Topology aware unitary synthesis for scalable quantum circuit optimization. Bulletin of the American Physical Society, 2022.
  87. General parameter-shift rules for quantum gradients. Quantum, 6:677, mar 2022.
  88. Optimal swap gate insertion for nearest neighbor quantum circuits. In ASPDAC. IEEE, 2014.
  89. A single quantum cannot be cloned. Nature, 299(5886):802–803, 1982.
  90. Quartz: superoptimization of quantum circuits. In Proceedings of the 43rd ACM SIGPLAN International Conference on Programming Language Design and Implementation, pages 625–640, 2022.
  91. Monte carlo tree search based hybrid optimization of variational quantum circuits. In Bin Dong, Qianxiao Li, Lei Wang, and Zhi-Qin John Xu, editors, Proceedings of Mathematical and Scientific Machine Learning, volume 190 of Proceedings of Machine Learning Research, pages 49–64. PMLR, 15–17 Aug 2022.
  92. Esther Ye and Samuel Yen-Chi Chen. Quantum architecture search via continual reinforcement learning. arXiv preprint arXiv:2112.05779, 2021.
  93. Qfast: Conflating search and numerical optimization for scalable quantum circuit synthesis. In 2021 IEEE International Conference on Quantum Computing and Engineering (QCE), pages 232–243. IEEE, 2021.
  94. Differentiable quantum architecture search. Quantum Science and Technology, 7(4):045023, aug 2022.
  95. Neural architecture search with reinforcement learning. In 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24-26, 2017, Conference Track Proceedings, 2017.
  96. Christa Zoufal. Generative Quantum Machine Learning. PhD thesis, ETH Zurich, 2021.
  97. Variational quantum boltzmann machines. Quantum Machine Intelligence, 3(1), feb 2021.
Citations (8)
View on Semantic Scholar

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now
X Twitter Logo Streamline Icon: https://streamlinehq.com

Tweets