中国物理B ›› 2022, Vol. 31 ›› Issue (8): 80304-080304.doi: 10.1088/1674-1056/ac6330

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Purification in entanglement distribution with deep quantum neural network

Jin Xu(徐瑾), Xiaoguang Chen(陈晓光)†, Rong Zhang(张蓉), and Hanwei Xiao(肖晗微)   

  1. Department of Communications Science and Engineering, Fudan University, Shanghai 200433, China
  • 收稿日期:2022-01-14 修回日期:2022-03-21 接受日期:2022-04-01 出版日期:2022-07-18 发布日期:2022-07-27
  • 通讯作者: Xiaoguang Chen E-mail:xiaoguangchen@fudan.edu.cn, xgchen@fudan.ac.cn

Purification in entanglement distribution with deep quantum neural network

Jin Xu(徐瑾), Xiaoguang Chen(陈晓光)†, Rong Zhang(张蓉), and Hanwei Xiao(肖晗微)   

  1. Department of Communications Science and Engineering, Fudan University, Shanghai 200433, China
  • Received:2022-01-14 Revised:2022-03-21 Accepted:2022-04-01 Online:2022-07-18 Published:2022-07-27
  • Contact: Xiaoguang Chen E-mail:xiaoguangchen@fudan.edu.cn, xgchen@fudan.ac.cn

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摘要： Entanglement distribution is important in quantum communication. Since there is no information with value in this process, purification is a good choice to solve channel noise. In this paper, we simulate the purification circuit under true environment on Cirq, which is a noisy intermediate-scale quantum (NISQ) platform. Besides, we apply quantum neural network (QNN) to the state after purification. We find that combining purification and quantum neural network has good robustness towards quantum noise. After general purification, quantum neural network can improve fidelity significantly without consuming extra states. It also helps to obtain the advantage of entangled states with higher dimension under amplitude damping noise. Thus, the combination can bring further benefits to purification in entanglement distribution.

关键词: purification, quantum neural network, entanglement distribution, quantum communication

Abstract: Entanglement distribution is important in quantum communication. Since there is no information with value in this process, purification is a good choice to solve channel noise. In this paper, we simulate the purification circuit under true environment on Cirq, which is a noisy intermediate-scale quantum (NISQ) platform. Besides, we apply quantum neural network (QNN) to the state after purification. We find that combining purification and quantum neural network has good robustness towards quantum noise. After general purification, quantum neural network can improve fidelity significantly without consuming extra states. It also helps to obtain the advantage of entangled states with higher dimension under amplitude damping noise. Thus, the combination can bring further benefits to purification in entanglement distribution.

Key words: purification, quantum neural network, entanglement distribution, quantum communication

中图分类号:  (Quantum communication)

  • 03.67.Hk
03.67.Ac (Quantum algorithms, protocols, and simulations) 03.67.Bg (Entanglement production and manipulation) 42.50.Lc (Quantum fluctuations, quantum noise, and quantum jumps)