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2025年5月2日 15:51 星期五
首页 > 过刊浏览>2024年第33卷第6期 >16-27. DOI:10.15888/j.cnki.csa.009559
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量子模拟器优化综述
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Overview on Quantum Simulator Optimization
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    摘要:

    近年来, 不断发展的量子计算已成为众人关注的焦点. 然而, 量子硬件存在稀缺性和噪声等问题, 这使得研究量子算法、验证量子芯片等行为都依赖运行在经典计算机上的量子模拟器. 本文讨论了不同量子模拟器使用的主要模拟方法, 并讨论了主流的全振幅状态向量模拟器和基于张量网络的量子模拟器的各种优化. 最后, 我们总结了量子模拟器的现状和未来发展方向.

    Abstract:

    In recent years, the rapidly evolving quantum computing has become the focus of attention. However, quantum hardware suffers from scarcity and noise, which makes the study of quantum algorithms and the verification of quantum chips rely on quantum simulators running on classical computers. In this study, the main simulation methods used by different quantum simulators are discussed, and various optimizations of mainstream full-amplitude state vector simulators and tensor network-based quantum simulators are explored. Finally, the current status and future directions of quantum simulators are summarized.

    参考文献
    [1] Shor PW. Polynomial-time algorithms for prime factorization and discrete logarithms on a quantum computer. SIAM review, 1999, 41(2): 303–332.
    [2] Grover LK. A fast quantum mechanical algorithm for database search. Proceedings of the 28th Annual ACM Symposium on Theory of Computing. Philadelphia: ACM, 1996. 212–219.
    [3] Boixo S, Isakov SV, Smelyanskiy VN, et al. Characterizing quantum supremacy in near-term devices. Nature Physics, 2018, 14(6): 595–600.
    [4] Preskill J. Quantum computing in the NISQ era and beyond. Quantum, 2018, 2: 79.
    [5] Farhi E, Goldstone J, Gutmann S, et al. Quantum computation by adiabatic evolution. arXiv:quant-ph/0001106, 2000.
    [6] Raussendorf R, Briegel HJ. A one-way quantum computer. Physical Review Letters, 2001, 86(22): 5188–5191.
    [7] Kitaev AY. Quantum error correction with imperfect gates. In: Hirota O, Holevo AS, Caves CM, eds. Quantum Communication, Computing, and Measurement. Boston: Springer, 1997. 181–188.
    [8] Deutsch DE. Quantum computational networks. Proceedings of the 1989 Royal Society A: Mathematical and Physical Sciences, 1989, 425(1868): 73–90.
    [9] Nielsen MA, Chuang IL. Quantum Computation and Quantum Information. Cambridge Press. 2002. http://almuhammadi.com/sultan/books_2020/Nielsen_Chuang.pdf.
    [10] Kaye P, Laflamme R, Mosca M. An Introduction to Quantum Computing. Oxford: Oxford University Press, 2007.
    [11] Jozsa R. Quantum algorithms and the Fourier transform. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 1998, 454(1969): 323–337.
    [12] Harrow AW, Hassidim A, Lloyd S. Quantum algorithm for linear systems of equations. Physical Review Letters, 2009, 103(15): 150502.
    [13] Coppersmith D. An approximate Fourier transform useful in quantum factoring. arXiv:quant-ph/0201067, 2002.
    [14] Brassard G, Hoyer P, Mosca M, et al. Quantum amplitude amplification and estimation. Contemporary Mathematics, 2002, 305: 53–74.
    [15] Farhi E, Goldstone J, Gutmann S. A quantum approximate optimization algorithm. arXiv:1411.4028, 2014.
    [16] Cerezo M, Arrasmith A, Babbush R, et al. Variational quantum algorithms. Nature Reviews Physics, 2021, 3(9): 625–644.
    [17] Tilly J, Chen HX, Cao SX, et al. The variational quantum eigensolver: A review of methods and best practices. Physics Reports, 2022, 986: 1–128.
    [18] Biamonte J, Wittek P, Pancotti N, et al. Quantum machine learning. Nature, 2017, 549(7671): 195–202.
    [19] Lloyd S, Mohseni M, Rebentrost P. Quantum algorithms for supervised and unsupervised machine learning. arXiv:1307.0411, 2013.
    [20] Rebentrost P, Mohseni M, Lloyd S. Quantum support vector machine for big data classification. Physical Review Letters, 2014, 113(13): 130503.
    [21] Aaronson S, Chen L J. Complexity-theoretic foundations of quantum supremacy experiments. Proceedings of the 32nd Computational Complexity Conference. Riga, 2017. 1–67.
    [22] Arute F, Arya K, Babbush R, et al. Quantum supremacy using a programmable superconducting processor. Nature, 2019, 574(7779): 505–510.
    [23] Zlokapa A, Villalonga B, Boixo S, et al. Boundaries of quantum supremacy via random circuit sampling. npj Quantum Information, 2023, 9(1): 36.
    [24] Villalonga B, Lyakh D, Boixo S, et al. Establishing the quantum supremacy frontier with a 281 Pflop/s simulation. Quantum Science and Technology, 2020, 5(3): 034003.
    [25] Huang C, Zhang F, Newman M, et al. Classical simulation of quantum supremacy circuits. arXiv:2005.06787, 2020.
    [26] Liu Y, Liu X, Li F, et al. Closing the “quantum supremacy” gap: Achieving real-time simulation of a random quantum circuit using a new Sunway supercomputer. Proceedings of the 2021 International Conference for High Performance Computing, Networking, Storage and Analysis. St. Louis: IEEE, 2021. 1–12.
    [27] Li RL, Wu BJ, Ying MS, et al. Quantum supremacy circuit simulation on Sunway TaihuLight. IEEE Transactions on Parallel and Distributed Systems, 2020, 31(4): 805–816.
    [28] Pan F, Chen KY, Zhang P. Solving the sampling problem of the sycamore quantum circuits. Physical Review Letters, 2022, 129(9): 090502.
    [29] Smelyanskiy M, Sawaya NPD, Aspuru-Guzik A. qHiPSTER: The quantum high performance software testing environment. arXiv:1601.07195, 2016.
    [30] H?ner T, Steiger DS. 0.5 petabyte simulation of a 45-qubit quantum circuit. Proceedings of the 2017 International Conference for High Performance Computing, Networking, Storage and Analysis. Denver: IEEE, 2017. 1–10.
    [31] Boixo S, Isakov SV, Smelyanskiy VN, et al. Simulation of low-depth quantum circuits as complex undirected graphical models. arXiv:1712.05384, 2017.
    [32] Gottesman D. The Heisenberg representation of quantum computers. Proceedings of the 22nd International Colloquium on Group Theoretical Methods in Physics. Hobart, 1999. 32–43.
    [33] Aaronson S, Gottesman D. Improved simulation of stabilizer circuits. Physical Review A, 2004, 70(5): 052328.
    [34] Hillmich S, Markov IL, Wille R. Just like the real thing: Fast weak simulation of quantum computation. Proceedings of the 57th ACM/IEEE Design Automation Conference (DAC). San Francisco: IEEE, 2020. 1–6.
    [35] Markov IL, Fatima A, Isakov SV, et al. Massively parallel approximate simulation of hard quantum circuits. Proceedings of the 57th ACM/IEEE Design Automation Conference (DAC). San Francisco: IEEE, 2020. 1–6.
    [36] Anders S, Briegel HJ. Fast simulation of stabilizer circuits using a graph-state representation. Physical Review A, 2006, 73(2): 022334.
    [37] Bravyi S, Browne D, Calpin P, et al. Simulation of quantum circuits by low-rank stabilizer decompositions. Quantum, 2019, 3: 181.
    [38] Biamonte J, Bergholm V. Tensor networks in a nutshell. arXiv:1708.00006, 2017.
    [39] Barends R, Kelly J, Megrant A, et al. Superconducting quantum circuits at the surface code threshold for fault tolerance. Nature, 2014, 508(7497): 500–503.
    [40] Bennett CH, Divincenzo DP, Smolin JA, et al. Mixed-state entanglement and quantum error correction. Physical Review A, 1996, 54(5): 3824–3851.
    [41] Brassard G, Braunstein SL, Cleve R. Teleportation as a quantum computation. Physica D: Nonlinear Phenomena, 1998, 120(1–2): 43–47.
    [42] Cross A. The IBM Q experience and QISKit open-source quantum computing software. Proceedings of the 2018 APS March Meeting. 2018. Abstract ID: BAPS.2018.MAR.L58.3
    [43] Omole V, Tyagi A, Hanus A, et al. Cirq: A Python framework for creating, editing, and invoking quantum circuits. https://sdmay20-08.sd.ece.iastate.edu/docs/Design-Document-v2.pdf. [2023-11-17].
    [44] Svore K, Geller A, Troyer M, et al. Q#: Enabling scalable quantum computing and development with a high-level DSL. Proceedings of the 2018 Real World Domain Specific Languages Workshop. Vienna: ACM, 2018. 7.
    [45] Broughton M, Verdon G, McCourt T, et al. TensorFlow quantum: A software framework for quantum machine learning. arXiv:2003.02989, 2020.
    [46] Jones T, Brown A, Bush I, et al. QuEST and high performance simulation of quantum computers. Scientific Reports, 2019, 9(1): 10736.
    [47] Guerreschi GG, Hogaboam J, Baruffa F, et al. Intel quantum simulator: A cloud-ready high-performance simulator of quantum circuits. Quantum Science and Technology, 2020, 5(3): 034007.
    [48] Kelly A. Simulating quantum computers using OpenCL. arXiv:1805.00988, 2018.
    [49] Efthymiou S, Ramos-Calderer S, Bravo-Prieto C, et al. Qibo: A framework for quantum simulation with hardware acceleration. Quantum Science and Technology, 2022, 7(1): 015018.
    [50] Luo XZ, Liu JG, Zhang P, et al. Yao.jl: Extensible, efficient framework for quantum algorithm design. Quantum, 2020, 4: 341.
    [51] Suzuki Y, Kawase Y, Masumura Y, et al. Qulacs: A fast and versatile quantum circuit simulator for research purpose. Quantum, 2021, 5: 559.
    [52] Khammassi N, Ashraf I, Fu X, et al. QX: A high-performance quantum computer simulation platform. Proceedings of the 2017 Design, Automation & Test in Europe Conference & Exhibition (DATE). Lausanne: IEEE, 2017. 464–469.
    [53] Steiger DS, H?ner T, Troyer M. ProjectQ: An open source software framework for quantum computing. Quantum, 2018, 2: 49.
    [54] Wecker D, Svore KM. LIQUi|>: A software design architecture and domain-specific language for quantum computing. arXiv:1402.4467, 2014.
    [55] Gheorghiu V. Quantum++: A modern C++ quantum computing library. PLoS One, 2018, 13(12): e0208073.
    [56] Khammassi N, Ashraf I, Someren JV, et al. OpenQL: A portable quantum programming framework for quantum accelerators. ACM Journal on Emerging Technologies in Computing Systems, 2022, 18(1): 13.
    [57] Zhang C, Song ZY, Wang HJ, et al. HyQuas: Hybrid partitioner based quantum circuit simulation system on GPU. Proceedings of the 2021 ACM International Conference on Supercomputing. ACM, 2021. 443–454.
    [58] Pednault E, Gunnels JA, Nannicini G, et al. Breaking the 49-qubit barrier in the simulation of quantum circuits. arXiv:1710.05867, 2017.
    [59] Bian HD, Huang JQ, Tang JH, et al. PAS: A new powerful and simple quantum computing simulator. Software: Practice and Experience, 2023, 53(1): 142–159.
    [60] Cypher R, Sanz JLC. SIMD architectures and algorithms for image processing and computer vision. IEEE Transactions on Acoustics, Speech, and Signal Processing, 1989, 37(12): 2158–2174.
    [61] Lomont C. Introduction to Intel? advanced vector extensions. Intel White Paper, 2011. 23. http://www.lomont.org/papers/2011/Intro_to_Intel_AVX-Final.pdf. [2023-11-18].
    [62] Fatima A, Markov IL. Faster Schr?dinger-style simulation of quantum circuits. Proceedings of the 2021 IEEE International Symposium on High-performance Computer Architecture (HPCA). Seoul: IEEE, 2021. IEEE. 194–207.
    [63] Lam MD, Rothberg EE, Wolf ME. The cache performance and optimizations of blocked algorithms. ACM SIGPLAN Notices, 1991, 26(4): 63–74.
    [64] Wu XC, Di S, Dasgupta EM, et al. Full-state quantum circuit simulation by using data compression. Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis. Denver: ACM, 2019. 80.
    [65] Zulehner A, Wille R. Advanced simulation of quantum computations. IEEE Transactions on Computer-aided Design of Integrated Circuits and Systems, 2019, 38(5): 848–859.
    [66] Zhao YL, Guo YN, Yao Y, et al. Q-GPU: A recipe of optimizations for quantum circuit simulation using GPUs. Proceedings of the 2022 IEEE International Symposium on High-performance Computer Architecture (HPCA). Seoul: IEEE, 2022. 726–740.
    [67] Niemann P, Wille R, Miller DM, et al. QMDDs: Efficient quantum function representation and manipulation. IEEE Transactions on Computer-aided Design of Integrated Circuits and Systems, 2016, 35(1): 86–99.
    [68] Burgholzer L, Ploier A, Wille R. Exploiting arbitrary paths for the simulation of quantum circuits with decision diagrams. Proceedings of the 2022 Design, Automation & Test in Europe Conference & Exhibition (DATE). Antwerp: IEEE, 2022. 64–67.
    [69] Shen JC, Long LB, Okita M, et al. A reorder trick for decision diagram based quantum circuit simulation. arXiv:2211.07110, 2022.
    [70] De Raedt H, Jin FP, Willsch D, et al. Massively parallel quantum computer simulator, eleven years later. Computer Physics Communications, 2019, 237: 47–61.
    [71] De Raedt K, Michielsen K, De Raedt H, et al. Massively parallel quantum computer simulator. Computer Physics Communications, 2007, 176(2): 121–136.
    [72] Avila A, Maron A, Reiser R, et al. GPU-aware distributed quantum simulation. Proceedings of the 29th Annual ACM Symposium on Applied Computing. Gyeongju: ACM, 2014. 860–865.
    [73] Avila A, Reiser RHS, Pilla ML, et al. Optimizing D-GM quantum computing by exploring parallel and distributed quantum simulations under GPUs arquitecture. Proceedings of the 2016 IEEE Congress on Evolutionary Computation (CEC). Vancouver: IEEE, 2016. 5146–5153.
    [74] Amariutei A, Caraiman S. Parallel quantum computer simulation on the GPU. Proceedings of the 15th International Conference on System Theory, Control and Computing. Sinaia: IEEE, 2011. 1–6.
    [75] Li Z, Yuan JB. Quantum computer simulation on GPU cluster incorporating data locality. Proceedings of the 3rd International Conference on Cloud Computing and Security. Nanjing: Springer, 2017. 85–97.
    [76] Zhang P, Yuan JB, Lu XW. Quantum computer simulation on multi-GPU incorporating data locality. Proceedings of the 15th International Conference on Algorithms and Architectures for Parallel Processing. Zhangjiajie: Springer, 2015. 241–256.
    [77] Gutierrez E, Romero S, Trenas MA, et al. Simulation of quantum gates on a novel GPU architecture. Proceedings of the 7th WSEAS International Conference on Systems Theory and Scientific Computation. Vouliagmeni: WSEAS, 2007. 121–126.
    [78] Doi J, Takahashi H, Raymond R, et al. Quantum computing simulator on a heterogenous HPC system. Proceedings of the 16th ACM International Conference on Computing Frontiers. Alghero: ACM, 2019. 85–93.
    [79] Gutierrez E, Romero S, Trenas MA, et al. Parallel quantum computer simulation on the CUDA architecture. Proceedings of the 8th International Conference on Computational Science (ICCS 2008). Kraków: Springer. 700–709.
    [80] Gutiérrez E, Romero S, Trenas MA, et al. Quantum computer simulation using the CUDA programming model. Computer Physics Communications, 2010, 181(2): 283–300.
    [81] Feng TY, Chen SY, You X, et al. dgQuEST: Accelerating large scale quantum circuit simulation through hybrid CPU-GPU memory hierarchies. Proceedings of the 18th Network and Parallel Computing (IFIP WG 10.3) International Conference. Paris: Springer, 2021. 16–27.
    [82] Khalid AU, Zilic Z, Radecka K. FPGA emulation of quantum circuits. Proceedings of the 2004 IEEE International Conference on Computer Design: VLSI in Computers and Processors. San Jose: IEEE, 2004. 310–315.
    [83] Fujishima M. FPGA-based high-speed emulator of quantum computing. Proceedings of the 2003 IEEE International Conference on Field-programmable Technology (FPT)(IEEE Cat. No. 03EX798). Tokyo: IEEE, 2003. 21–26.
    [84] Lee YH, Khalil-Hani M, Marsono MN. FPGA-based quantum circuit emulation: A case study on quantum fourier transform. Proceedings of the 2014 International Symposium on Integrated Circuits (ISIC). Singapore: IEEE, 2014. 512–515.
    [85] Silva A, Zabaleta OG. FPGA quantum computing emulator using high level design tools. Proceedings of the 2017 Argentine Symposium and Conference on Embedded Systems (CASE). Buenos Aires: IEEE, 2017. 1–6.
    [86] Qian Y, Wang MY, Chen JL, et al. Efficient FPGA emulation of quantum Fourier transform. Proceedings of the 2019 China Semiconductor Technology International Conference (CSTIC). Shanghai: IEEE, 2019. 1–3.
    [87] Pednault E, Gunnels JA, Nannicini G, et al. Leveraging secondary storage to simulate deep 54-qubit sycamore circuits. arXiv:1910.09534, 2019.
    [88] Park D, Kim H, Kim J, et al. SnuQS: Scaling quantum circuit simulation using storage devices. Proceedings of the 36th ACM International Conference on Supercomputing. ACM, 2022. 6.
    [89] Wang M, Huang R, Tannu S, et al. TQSim: A case for reuse-focused tree-based quantum circuit simulation. arXiv:2203.13892, 2022.
    [90] Li GS, Ding YF, Xie Y. Eliminating redundant computation in noisy quantum computing simulation. Proceedings of the 57th ACM/IEEE Design Automation Conference (DAC). San Francisco: IEEE, 2020. 1–6.
    [91] Markov IL, Shi YY. Simulating quantum computation by contracting tensor networks. SIAM Journal on Computing, 2008, 38(3): 963–981.
    [92] Chen ZY, Zhou Q, Xue C, et al. 64-qubit quantum circuit simulation. Science Bulletin, 2018, 63(15): 964–971.
    [93] Villalonga B, Boixo S, Nelson B, et al. A flexible high-performance simulator for verifying and benchmarking quantum circuits implemented on real hardware. npj Quantum Information, 2019, 5(1): 86.
    [94] Li F, Liu X, Liu Y, et al. SW_Qsim: A minimize-memory quantum simulator with high-performance on a new Sunway supercomputer. Proceedings of the 2021 International Conference for High Performance Computing, Networking, Storage and Analysis. St. Louis: IEEE, 2021. 1–13.
    [95] Gray J, Kourtis S. Hyper-optimized tensor network contraction. Quantum, 2021, 5: 410.
    [96] Huang C, Zhang F, Newman M, et al. Efficient parallelization of tensor network contraction for simulating quantum computation. Nature Computational Science, 2021, 1(9): 578–587.
    [97] Chen YJ, Liu Y, Shi XM, et al. Lifetime-based optimization for simulating quantum circuits on a new Sunway supercomputer. Proceedings of the 28th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming. Montreal: ACM, 2023. 148–159.
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彭世昕,张为华.量子模拟器优化综述.计算机系统应用,2024,33(6):16-27

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  • 收稿日期:2023-12-14
  • 最后修改日期:2024-01-17
  • 在线发布日期: 2024-04-30
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