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Parameter estimation of continuous variable quantum key distribution system via artificial neural networks |
| Hao Luo(罗浩)1,3, Yi-Jun Wang(王一军)1, Wei Ye(叶炜)2, Hai Zhong(钟海)2,†, Yi-Yu Mao(毛宜钰)1, and Ying Guo(郭迎)1,‡ |
1 School of Automation, Central South University, Changsha 410083, China;
2 School of Computer Science and Engineering, Central South University, Changsha 410083, China;
3 College of Applied Science, Jiangxi University of Science and Technology, Ganzhou 341000, China |
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Abstract Continuous-variable quantum key distribution (CVQKD) allows legitimate parties to extract and exchange secret keys. However, the tradeoff between the secret key rate and the accuracy of parameter estimation still around the present CVQKD system. In this paper, we suggest an approach for parameter estimation of the CVQKD system via artificial neural networks (ANN), which can be merged in post-processing with less additional devices. The ANN-based training scheme, enables key prediction without exposing any raw key. Experimental results show that the error between the predicted values and the true ones is in a reasonable range. The CVQKD system can be improved in terms of the secret key rate and the parameter estimation, which involves less additional devices than the traditional CVQKD system.
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Received: 26 July 2021
Revised: 11 September 2021
Accepted manuscript online: 18 September 2021
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PACS:
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03.67.-a
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(Quantum information)
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87.85.dq
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(Neural networks)
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Corresponding Authors:
Hai Zhong, Ying Guo
E-mail: zhonghai@csu.edu.cn;yingguo@csu.edu.cn
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Cite this article:
Hao Luo(罗浩), Yi-Jun Wang(王一军), Wei Ye(叶炜), Hai Zhong(钟海), Yi-Yu Mao(毛宜钰), and Ying Guo(郭迎) Parameter estimation of continuous variable quantum key distribution system via artificial neural networks 2022 Chin. Phys. B 31 020306
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