Quantum computational advantage via 60-qubit 24-cycle random circuit sampling

Qingling Zhu; Sirui Cao; Fusheng Chen; Ming-Cheng Chen; Xiawei Chen; Tung-Hsun Chung; Hui Deng; Yajie Du; Daojin Fan; Ming Gong; Cheng Guo; Chu Guo; Shaojun Guo; Lianchen Han; Linyin Hong; He-Liang Huang; Yong-Heng Huo; Liping Li; Na Li; Shaowei Li; Yuan Li; Futian Liang; Chun Lin; Jin Lin; Haoran Qian; Dan Qiao; Hao Rong; Hong Su; Lihua Sun; Liangyuan Wang; Shiyu Wang; Dachao Wu; Yulin Wu; Yu Xu; Kai Yan; Weifeng Yang; Yang Yang; Yangsen Ye; Jianghan Yin; Chong Ying; Jiale Yu; Chen Zha; Cha Zhang; Haibin Zhang; Kaili Zhang; Yiming Zhang; Han Zhao; Youwei Zhao; Liang Zhou; Chao-Yang Lu; Cheng-Zhi Peng; Xiaobo Zhu; Jian-Wei Pan

doi:10.1016/j.scib.2021.10.017

Quantum computational advantage via 60-qubit 24-cycle random circuit sampling

Sci Bull (Beijing). 2022 Feb 15;67(3):240-245. doi: 10.1016/j.scib.2021.10.017. Epub 2021 Oct 25.

Authors

Qingling Zhu¹, Sirui Cao¹, Fusheng Chen¹, Ming-Cheng Chen¹, Xiawei Chen², Tung-Hsun Chung¹, Hui Deng¹, Yajie Du², Daojin Fan¹, Ming Gong¹, Cheng Guo¹, Chu Guo¹, Shaojun Guo¹, Lianchen Han¹, Linyin Hong³, He-Liang Huang⁴, Yong-Heng Huo¹, Liping Li², Na Li¹, Shaowei Li¹, Yuan Li¹, Futian Liang¹, Chun Lin⁵, Jin Lin¹, Haoran Qian¹, Dan Qiao², Hao Rong¹, Hong Su¹, Lihua Sun¹, Liangyuan Wang², Shiyu Wang¹, Dachao Wu¹, Yulin Wu¹, Yu Xu¹, Kai Yan², Weifeng Yang³, Yang Yang², Yangsen Ye¹, Jianghan Yin², Chong Ying¹, Jiale Yu¹, Chen Zha¹, Cha Zhang¹, Haibin Zhang², Kaili Zhang¹, Yiming Zhang¹, Han Zhao², Youwei Zhao¹, Liang Zhou³, Chao-Yang Lu¹, Cheng-Zhi Peng¹, Xiaobo Zhu⁶, Jian-Wei Pan¹

Affiliations

¹ Hefei National Laboratory for Physical Sciences at the Microscale and Department of Modern Physics, University of Science and Technology of China, Hefei 230026, China; Shanghai Branch, CAS Center for Excellence in Quantum Information and Quantum Physics, University of Science and Technology of China, Shanghai 201315, China; Shanghai Research Center for Quantum Sciences, Shanghai 201315, China.
² Shanghai Branch, CAS Center for Excellence in Quantum Information and Quantum Physics, University of Science and Technology of China, Shanghai 201315, China.
³ QuantumCTek Co., Ltd., Hefei 230026, China.
⁴ Hefei National Laboratory for Physical Sciences at the Microscale and Department of Modern Physics, University of Science and Technology of China, Hefei 230026, China; Shanghai Branch, CAS Center for Excellence in Quantum Information and Quantum Physics, University of Science and Technology of China, Shanghai 201315, China; Shanghai Research Center for Quantum Sciences, Shanghai 201315, China; Henan Key Laboratory of Quantum Information and Cryptography, Zhengzhou 450000, China.
⁵ Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China.
⁶ Hefei National Laboratory for Physical Sciences at the Microscale and Department of Modern Physics, University of Science and Technology of China, Hefei 230026, China; Shanghai Branch, CAS Center for Excellence in Quantum Information and Quantum Physics, University of Science and Technology of China, Shanghai 201315, China; Shanghai Research Center for Quantum Sciences, Shanghai 201315, China. Electronic address: xbzhu16@ustc.edu.cn.

PMID: 36546072
DOI: 10.1016/j.scib.2021.10.017

Abstract

To ensure a long-term quantum computational advantage, the quantum hardware should be upgraded to withstand the competition of continuously improved classical algorithms and hardwares. Here, we demonstrate a superconducting quantum computing systems Zuchongzhi 2.1, which has 66 qubits in a two-dimensional array in a tunable coupler architecture. The readout fidelity of Zuchongzhi 2.1 is considerably improved to an average of 97.74%. The more powerful quantum processor enables us to achieve larger-scale random quantum circuit sampling, with a system scale of up to 60 qubits and 24 cycles, and fidelity of F_XEB=(3.66±0.345)×10^-4. The achieved sampling task is about 6 orders of magnitude more difficult than that of Sycamore [Nature 574, 505 (2019)] in the classic simulation, and 3 orders of magnitude more difficult than the sampling task on Zuchongzhi 2.0 [arXiv:2106.14734 (2021)]. The time consumption of classically simulating random circuit sampling experiment using state-of-the-art classical algorithm and supercomputer is extended to tens of thousands of years (about 4.8×10⁴ years), while Zuchongzhi 2.1 only takes about 4.2 h, thereby significantly enhancing the quantum computational advantage.

Keywords: Quantum computation; Quantum information; Quantum physics; Superconducting quantum computing; Superconducting qubit.