Purification in entanglement distribution with deep quantum neural network

doi:10.1088/1674-1056/ac6330

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.

Keywords: purification quantum neural network entanglement distribution quantum communication

Received: 14 January 2022
Revised: 21 March 2022
Accepted manuscript online: 01 April 2022

PACS:	03.67.Hk	(Quantum communication)
	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)

Corresponding Authors: Xiaoguang Chen
E-mail: xiaoguangchen@fudan.edu.cn, xgchen@fudan.ac.cn

Cite this article:

Jin Xu(徐瑾), Xiaoguang Chen(陈晓光), Rong Zhang(张蓉), and Hanwei Xiao(肖晗微) Purification in entanglement distribution with deep quantum neural network 2022 Chin. Phys. B 31 080304

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