| Parameter estimation of continuous variable quantum key distribution system via artificial neural networks |
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| 作者姓名： | 罗浩 王一军 叶炜 钟海 毛宜钰 郭迎 |
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| 作者单位： | 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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| 摘 要： | 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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| 关 键 词： | quantum key distribution artificial neural networks secret key rate parameter estimation |
| 收稿时间： | 2021-07-26 |
Parameter estimation of continuous variable quantum key distribution system via artificial neural networks |
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| Hao Luo,Yi-Jun Wang,Wei Ye,Hai Zhong,Yi-Yu Mao,Ying Guo.Parameter estimation of continuous variable quantum key distribution system via artificial neural networks[J].Chinese Physics B,2022,31(2):20306-020306. |
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| Authors: | Hao Luo Yi-Jun Wang Wei Ye Hai Zhong Yi-Yu Mao Ying Guo |
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| Affiliation: | 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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| Keywords: | quantum key distribution artificial neural networks secret key rate parameter estimation |
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