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

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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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Acknowledgements

This work was supported by National Natural Science Foundation of China (NSFC) (61177072) and Key Research and Development Program of Shandong Province (CN) (2017GGX201010).

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Authors and Affiliations

  1. Department of Radiology, Taishan Medical University, Tai’an, China

    Weizhao Lu, Kun Hou, Liting Shi, Huihui Zhao, Zhengmei Li & Jianfeng Qiu

  2. College of Physics and Information Engineering, Fuzhou University, Fuzhou, China

    Chunhui Huang

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  1. Weizhao Lu
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  2. Chunhui Huang
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  3. Kun Hou
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  4. Liting Shi
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  5. Huihui Zhao
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  7. Jianfeng Qiu
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Correspondence to Weizhao Lu.

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Lu, W., Huang, C., Hou, K. et al. Recurrent neural network approach to quantum signal: coherent state restoration for continuous-variable quantum key distribution. Quantum Inf Process 17, 109 (2018). https://doi.org/10.1007/s11128-018-1877-y

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  • DOI: https://doi.org/10.1007/s11128-018-1877-y

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