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Quantum Information Processing

Recurrent neural network approach to quantum signal: coherent state restoration for continuous-variable quantum key distribution

  • 01-05-2018
Published in:
Authors
Weizhao Lu
Chunhui Huang
Kun Hou
Liting Shi
Huihui Zhao
Zhengmei Li
Jianfeng Qiu
Published in
Quantum Information Processing | Issue 5/2018

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Abstract

In continuous-variable quantum key distribution (CV-QKD), weak signal carrying information transmits from Alice to Bob; during this process it is easily influenced by unknown noise which reduces signal-to-noise ratio, and strongly impacts reliability and stability of the communication. Recurrent quantum neural network (RQNN) is an artificial neural network model which can perform stochastic filtering without any prior knowledge of the signal and noise. In this paper, a modified RQNN algorithm with expectation maximization algorithm is proposed to process the signal in CV-QKD, which follows the basic rule of quantum mechanics. After RQNN, noise power decreases about 15 dBm, coherent signal recognition rate of RQNN is 96%, quantum bit error rate (QBER) drops to 4%, which is 6.9% lower than original QBER, and channel capacity is notably enlarged.

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previous article Multi-party quantum key agreement with five-qubit brown states
next article Composable security of unidimensional continuous-variable quantum key distribution
Title
Recurrent neural network approach to quantum signal: coherent state restoration for continuous-variable quantum key distribution
Authors
Weizhao Lu
Chunhui Huang
Kun Hou
Liting Shi
Huihui Zhao
Zhengmei Li
Jianfeng Qiu
Publication date
01-05-2018
Publisher
Springer US
Published in
Quantum Information Processing / Issue 5/2018
Print ISSN: 1570-0755
Electronic ISSN: 1573-1332
DOI
https://doi.org/10.1007/s11128-018-1877-y
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