Year
Month
(Preprint) Recursive Multi-Tensor Contraction for XEB Verification of Quantum Circuits
Gleb Kalachev ¹ ², Pavel Panteleev ¹ ², Man-Hong Yung 翁文康 ¹ ³
¹ Huawei 2012 Lab
华为2012实验室
² Lomonosov Moscow State University
³ Institute for Quantum Science and Engineering, and Department of Physics, Southern University of Science and Technology, Shenzhen, 518055, China
中国 深圳 南方科技大学量子科学与工程研究院及物理系
arXiv, 2021-08-12
Abstract

The computational advantage of noisy quantum computers have been demonstrated by sampling the bitstrings of quantum random circuits. An important issue is how the performance of quantum devices could be quantified in the so-called “supremacy regime”. The standard approach is through the linear cross entropy (XEB), where the theoretical value of the probability is required for each bitstring.

However, the computational cost of XEB grows exponentially. So far, random circuits of the 53-qubit Sycamore chip was verified up to 10 cycles of gates only; the XEB fidelities of deeper circuits were approximated with simplified circuits instead. Here we present a multitensor contraction algorithm for speeding up the calculations of XEB of quantum circuits, where the computational cost can be significantly reduced through a recursive manner with some form of memoization.

As a demonstration, we analyzed the experimental data of the 53-qubit Sycamore
chip and obtained the exact values of the corresponding XEB fidelities up to 16 cycles using only moderate computing resources (few GPUs). If the algorithm was implemented on the Summit supercomputer, we estimate that for the 20-cycles supremacy circuits, it would only cost 7.5 days, which is several orders of magnitudes lower than previously estimated in the literature.
Recursive Multi-Tensor Contraction for XEB Verification of Quantum Circuits_1
Recursive Multi-Tensor Contraction for XEB Verification of Quantum Circuits_2
Recursive Multi-Tensor Contraction for XEB Verification of Quantum Circuits_3
Recursive Multi-Tensor Contraction for XEB Verification of Quantum Circuits_4
  • Tunable compound eyes with coaxial lens-on-lens ommatidia for cooperative bi-focal imaging
  • Zhi-Juan Sun, Wei-Jian Zhong, Qing Cai, Yi-Fan Lu, Chang-Xu Li, Dong-Dong Han, Yong-Lai Zhang
  • Opto-Electronic Advances
  • 2026-02-09
  • High-efficiency infrared upconversion imaging with nonlinear silicon metasurfaces empowered by quasi-bound states in the continuum
  • Tingting Liu, Jumin Qiu, Meibao Qin, Xu Tu Huifu Qiu, Feng Wu, Tianbao Yu, Qiegen Liu, Shuyuan Xiao
  • Opto-Electronic Advances
  • 2026-01-29
  • Timeshare surface-enhanced Raman scattering platform with sensitive and quantitative mode
  • Qianqian Ding, Xueyan Chen, Yunlu Jia, Hong Liu, Xiaochen Zhang, Ningtao Cheng, Shikuan Yang
  • Opto-Electronic Advances
  • 2026-01-27
  • Electric-field-induced second-harmonic generation
  • Hangkai Fan, Alexey Proskurin, Mingzhao Song, Andrey Bogdanov
  • Opto-Electronic Advances
  • 2026-01-27
  • Fiber-optic microstructured sensors based on abrupt field patterns: theory, fabrication, and applications
  • Yuxuan Yi, Wanlai Zhu, Zao Yi, Zigang Zhou, Shubo Cheng, Majid Niaz Akhtar, Sohail Ahmad
  • Opto-Electronic Science
  • 2026-01-23
  • Integrated metasurface-freeform system enabled multi-focal planes augmented reality display
  • Shifei Zhang, Lina Gao, Yidan Zhao, Yongdong Wang, Bo Wang, Junjie Li, Jiaxi Duan, Dewen Cheng, Cheng-Wei Qiu, Yongtian Wang, Tong Yang, Lingling Huang
  • Opto-Electronic Science
  • 2026-01-23
  • Decoding subject-invariant emotional information from cardiac signals detected by photonic sensing system
  • Yukun Long, Rui Min Kun Xiao, Zhuo Wang, Lanfang Liu, Yifan Sun, Xiaoli Li, Zhaohui Li, Zeev Zalevsky
  • Opto-Electronic Technology
  • 2025-12-25
  • Integrated photonic synapses, neurons, memristors, and neural networks for photonic neuromorphic computing
  • Shufei Han, Weihong Shen, Min Gu, Qiming Zhang
  • Opto-Electronic Technology
  • 2025-12-25
  • Photoacoustic spectroscopy and light-induced thermoelastic spectroscopy based on inverted-triangular lithium niobate tuning fork
  • Junjie Mu, Guowei Han, Runqiu Wang, Shunda Qiao, Ying He Yufei Ma
  • Opto-Electronic Science
  • 2025-12-25
  • Thin-film lithium niobate-based detector: recent advances and perspectives
  • Xiaoli Sun, Yuechen Jia, Feng Chen
  • Opto-Electronic Science
  • 2025-12-25
  • In-situ and ex-situ twisted bilayer liquid crystal computing platform for reconfigurable image processing
  • Kang Zeng, Yougang Ke, Zhangming Hong, Linzhou Zeng, Xinxing Zhou
  • Opto-Electronic Advances
  • 2025-12-25
  • Highly textured single-crystal-like perovskite films for large-area, high-performance photodiodes
  • Runkai Liu, Feng Li, Rongkun Zheng
  • Opto-Electronic Advances
  • 2025-12-25



  • Auto-Split: A General Framework of Collaborative Edge-Cloud AI                                Modeling Relevance Ranking under the Pre-training and Fine-tuning Paradigm
    About
    |
    Contact
    |
    Copyright © PubCard