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    Verifying Quantum Advantage Experiments with Multiple Amplitude Tensor Network Contraction

    Yong Liu1,*, Yaojian Chen2,*, Chu Guo3,†, Jiawei Song4, Xinmin Shi5, Lin Gan2,4,‡, Wenzhao Wu4, Wei Wu4, Haohuan Fu2,4,§ et al.

    Xin Liu1,4,∥, Dexun Chen4, Zhifeng Zhao1, Guangwen Yang1,2,4, and Jiangang Gao6

    • 1Zhejiang Lab, Hangzhou, 311121, China
    • 2Tsinghua University, Beijing, 100084, China
    • 3Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control of Ministry of Education, Department of Physics and Synergetic Innovation Center for Quantum Effects and Applications, Hunan Normal University, Changsha, 410081, China
    • 4National Supercomputing Center in Wuxi, Wuxi, 214000, China
    • 5Information Engineering University, Zhengzhou, 450001, China
    • 6National Research Center of Parallel Computer Engineering and Technology, Beijing, 100190, China

    • *These authors contributed equally to this work.
    • †guochu604b@gmail.com
    • ‡lingan@tsinghua.edu.cn
    • §haohuan@tsinghua.edu.cn
    • ∥lucyliu_zj@163.com

    Phys. Rev. Lett. 132, 030601 – Published 16 January, 2024

    DOI: https://doi.org/10.1103/PhysRevLett.132.030601

    Abstract

    The quantum supremacy experiment, such as Google Sycamore [F. Arute et al., Nature (London) 574, 505 (2019).], poses a great challenge for classical verification due to the exponentially increasing compute cost. Using a new-generation Sunway supercomputer within 8.5 d, we provide a direct verification by computing 3×106 exact amplitudes for the experimentally generated bitstrings, obtaining a cross-entropy benchmarking fidelity of 0.191% (the estimated value is 0.224%). The leap of simulation capability is built on a multiple-amplitude tensor network contraction algorithm which systematically exploits the “classical advantage” (the inherent “store-and-compute” operation mode of von Neumann machines) of current supercomputers, and a fused tensor network contraction algorithm which drastically increases the compute efficiency on heterogeneous architectures. Our method has a far-reaching impact in solving quantum many-body problems, statistical problems, as well as combinatorial optimization problems.

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