Parameter estimation of continuous variable quantum key distribution system via artificial neural networks

doi:10.1088/1674-1056/ac2807

中国物理B ›› 2022, Vol. 31 ›› Issue (2): 20306-020306.doi: 10.1088/1674-1056/ac2807

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

收稿日期:2021-07-26 修回日期:2021-09-11 接受日期:2021-09-18 出版日期:2022-01-13 发布日期:2022-01-25
通讯作者: Hai Zhong, Ying Guo E-mail:zhonghai@csu.edu.cn;yingguo@csu.edu.cn

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

Received:2021-07-26 Revised:2021-09-11 Accepted:2021-09-18 Online:2022-01-13 Published:2022-01-25
Contact: Hai Zhong, Ying Guo E-mail:zhonghai@csu.edu.cn;yingguo@csu.edu.cn

摘要/Abstract

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.

关键词: quantum key distribution, artificial neural networks, secret key rate, parameter estimation

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.

Key words: quantum key distribution, artificial neural networks, secret key rate, parameter estimation

中图分类号: (Quantum information)

03.67.-a

87.85.dq (Neural networks)

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[J]. 中国物理B, 2022, 31(2): 20306-020306.

参考文献

[1] Bennett C H and Brassard G 1984 Proceedings of IEEE International Conference on Computers Systems, and Signal Processing, December 10-12, 1984, Bangalore, India, pp. 175-179
[2] Ruppert L, Usenko V C and Filip R 2014 Phys. Rev. A 90 062310
[3] Diamanti E and Leverrier A 2015 Entropy 17 6072
[4] Grosshans F and Grangier P 2002 Phys. Rev. Lett. 88 057902
[5] Lance A M, Symul T, Sharma V, Weedbrook C, Ralph T C and Lam P K 2005 Phys. Rev. Lett. 95 180503
[6] Grosshans F, Van Assche G, Wenger J, Brouri R, Cerf N J and Grangier P 2003 Nature 421 238
[7] Jouguet P, Kunz-Jacques S, Leverrier A, Grangier P and Diamanti E 2013 Nat. Photon. 7 378
[8] Lodewyck J, Bloch M, García-Patrón R, Fossier S, Karpov E, Diamanti E, Debuisschert T, Cerf N J, TualleBrouri R, McLaughlin S W and Grangier P 2007 Phys. Rev. A 76 042305
[9] Mao Y Y, Huang W T, Zhong H, Wang Y J, Qin H, Guo Y and Huang D 2020 New J. Phys. 22 083073
[10] Wu X D, Wang Y J, Guo Y, Zhong H and Huang D 2021 Phys. Rev. A 103 032604
[11] Liao Q, Xiao G, Zhong H and Guo Y 2020 New J. Phys. 22 083086
[12] Su X L, Wang W Z, Wang Y, Jia X J, Xie C D and Peng K C 2009 Europhys. Lett. 87 20005
[13] Su X L 2014 Chin. Sci. Bull. 59 1083
[14] M Navascués, Grosshans F and Acin A 2006 Phys. Rev. Lett. 97 190502
[15] Furrer F, Franz T, Berta M, Leverrier A, Scholz V B, Tomamichel M and Werner R F 2012 Phys. Rev. Lett. 109 100502
[16] García-Patrón R and Cerf N J 2006 Phys. Rev. Lett. 97 190503
[17] Leverrier A 2015 Phys. Rev. Lett. 114 070501
[18] Wang Y J, Mao Y Y, Huang W T, Huang D and Guo Y 2019 Opt. Express 27 25314
[19] Zeng G H 2010 Quantum private communication (Beijing:Higher Education Press and Springer Publishing Company) pp. 293-297
[20] Weedbrook C, Lance A M, Bowen W P, Symul T, Ralph T C and Lam P K 2004 Phys. Rev. Lett. 93 170504
[21] Brunner H H, Comandar L C, Karinou F, Bettelli S, Hillerkuss D, Fung F, Wang D W, Mikroulis S, Yi Q, Kuschnerov M, Poppe A, Xie C S and Peev M 2017 19th International Conference on Transparent Optical Networks (ICTON), July 2-6, 2017, Girona, Spain, pp. 1-4
[22] Guo Y, Xie C L, Huang P, Li J W, Zhang L, Huang D and Zeng G H 2018 Phys. Rev. A 97 052326
[23] Chai G, Cao Z W, Liu W Q, Wang S Y, Huang P and Zeng G H 2019 Phys. Rev. A 99 032326
[24] Guo Y, Zhou Z H, Wang X D, Wu X D, Zhang L and Huang D 2019 Int. J. Theor. Phys. 58 1613
[25] Wang X Y, Zhang Y C, Yu S and Guo H 2019 Quantum Inf. Process. 18 1
[26] Yang J, Xu B J, Peng X and Guo H 2012 Phys. Rev. A 85 052302
[27] Li Y, Huang P, Wang S Y, Wang T, Li D W and Zeng G H 2018 Phys. Lett. A 382 3253
[28] Garcia-Patron Sanchez Raul 2007 Quantum information with optical continuous variables:from bell tests to key distribution, Ph.D. Dissertation (Brussels:Université libre de Bruxelles)
[29] Chai G, Li D W, Cao Z W, Zhang M H, Huang P and Zeng G H 2020 Quantum Engineering 2 e37
[30] Gyongyosi L and Imre S 2018 Appl. Sci. 8 87
[31] Jiang X Q, Yang S Y, Huang P and Zeng G H 2018 IEEE Photon. J. 10 1
[32] Gyongyosi L and Imre S 2014 Advances in Photonics of Quantum Computing, Memory, and Communication VII, February 19, 2014, San Francisco, USA, pp. 28-40
[33] Liu B, Zhao B K, Zou D J, Wu C Q, Yu W R and You I 2013 Information and Communication Technology-EurAsia Conference, March, 2013, Yogyakarta, Indonesia, pp. 453-458
[34] Li D W, Huang P, Zhou Y M, Li Y and Zeng G H 2018 IEEE Photon. J. 10 1
[35] McCulloch W S and Pitts W 1943 The bulletin of mathematical biophysics volume 5 p. 115
[36] Wang W Y and Hoi-Kwong Lo 2019 Phys. Rev. A 100 062334
[37] Zhou M G, Liu Z P, Liu W B, Li C L, Bai J L, Xue Y R, Fu Y, Yin H L and Chen Z B 2021 arXiv:2108.02578v1[quant-ph]
[38] Beale H D, Demuth H B and Hagan M 1997 Neural network design (Boston:PWS Publishing Co.) Chapter 1 pp. 4-7
[39] Leverrier A, Grosshans F and Grangier P 2010 Phys. Rev. A 81 062343
[40] Fossier S, Diamanti E, Debuisschert T, Tuallebrouri R and Grangier P 2009 J. Phys. B:At. Mol. Opt. Phys. 42 114014
[41] Leverrier A and Grangier P 2009 Phys. Rev. Lett. 102 180504
[42] Thearle O, Assad S M and Symul T 2016 Phys. Rev. A 93 042343

Parameter estimation of continuous variable quantum key distribution system via artificial neural networks

Parameter estimation of continuous variable quantum key distribution system via artificial neural networks

RichHTML

PDF (PC)

赞

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

Metrics

本文评价

推荐阅读 0

[1]	Hai-Yuan Hong(洪海源), Xiu-Juan Lu(鲁秀娟), and Sen Kuang(匡森). Feedback control and quantum error correction assisted quantum multi-parameter estimation[J]. 中国物理B, 2023, 32(4): 40603-040603.
[2]	Bao Feng(冯宝), Hai-Dong Huang(黄海东), Yu-Xiang Bian(卞宇翔), Wei Jia(贾玮), Xing-Yu Zhou(周星宇), and Qin Wang(王琴). Security of the traditional quantum key distribution protocolswith finite-key lengths[J]. 中国物理B, 2023, 32(3): 30307-030307.
[3]	Haiqiang Ma(马海强), Yanxin Han(韩雁鑫), Tianqi Dou(窦天琦), and Pengyun Li(李鹏云). Performance of phase-matching quantum key distribution based on wavelength division multiplexing technology[J]. 中国物理B, 2023, 32(2): 20304-020304.
[4]	Dan Wu(吴丹), Xiao Li(李骁), Liang-Liang Wang(王亮亮), Jia-Shun Zhang(张家顺), Wei Chen(陈巍), Yue Wang(王玥), Hong-Jie Wang(王红杰), Jian-Guang Li(李建光), Xiao-Jie Yin(尹小杰), Yuan-Da Wu(吴远大), Jun-Ming An(安俊明), and Ze-Guo Song(宋泽国). Temperature characterizations of silica asymmetric Mach-Zehnder interferometer chip for quantum key distribution[J]. 中国物理B, 2023, 32(1): 10305-010305.
[5]	Lingzhi Kong(孔令志), Weiqi Liu(刘维琪), Fan Jing(荆凡), Zhe-Kun Zhang(张哲坤), Jin Qi(齐锦), and Chen He(贺晨). Improvement of a continuous-variable measurement-device-independent quantum key distribution system via quantum scissors[J]. 中国物理B, 2022, 31(9): 90304-090304.
[6]	Lingzhi Kong(孔令志), Weiqi Liu(刘维琪), Fan Jing(荆凡), and Chen He(贺晨). Practical security analysis of continuous-variable quantum key distribution with an unbalanced heterodyne detector[J]. 中国物理B, 2022, 31(7): 70303-070303.
[7]	Qingquan Peng(彭清泉), Qin Liao(廖骎), Hai Zhong(钟海), Junkai Hu(胡峻凯), and Ying Guo(郭迎). Short-wave infrared continuous-variable quantum key distribution over satellite-to-submarine channels[J]. 中国物理B, 2022, 31(6): 60306-060306.
[8]	Xiao Li(李骁), Liang-Liang Wang(王亮亮), Jia-shun Zhang(张家顺), Wei Chen(陈巍), Yue Wang(王玥), Dan Wu (吴丹), and Jun-Ming An (安俊明). Quantum key distribution transmitter chip based on hybrid-integration of silica and lithium niobates[J]. 中国物理B, 2022, 31(6): 64212-064212.
[9]	Mengmeng Luo(罗萌萌), Wenxiao Liu(刘文晓), Yuetao Chen(陈悦涛), Shangbin Han(韩尚斌), and Shaoyan Gao(高韶燕). Environmental parameter estimation with the two-level atom probes[J]. 中国物理B, 2022, 31(5): 50304-050304.
[10]	Wen-Ting Li(李文婷), Le Wang(王乐), Wei Li(李威), and Sheng-Mei Zhao(赵生妹). Phase-matching quantum key distribution with light source monitoring[J]. 中国物理B, 2022, 31(5): 50310-050310.
[11]	Xiao-Ming Chen(陈小明), Lei Chen(陈雷), and Ya-Long Yan(阎亚龙). Detecting the possibility of a type of photon number splitting attack in decoy-state quantum key distribution[J]. 中国物理B, 2022, 31(12): 120304-120304.
[12]	Jin You(游金), Yue Wang(王玥), and Jun-Ming An(安俊明). Realization of simultaneous balanced multi-outputs for multi-protocols QKD decoding based onsilica-based planar lightwave circuit[J]. 中国物理B, 2021, 30(8): 80302-080302.
[13]	Xian-Ke Li(李咸柯), Xiao-Qian Song(宋小谦), Qi-Wei Guo(郭其伟), Xing-Yu Zhou(周星宇), and Qin Wang(王琴). Practical decoy-state BB84 quantum key distribution with quantum memory[J]. 中国物理B, 2021, 30(6): 60305-060305.
[14]	Xiao-Ting Chen(陈小婷), Lu-Ping Zhang(张露萍), Shou-Kang Chang(常守康), Huan Zhang(张欢), and Li-Yun Hu(胡利云). Continuous-variable quantum key distribution based on photon addition operation[J]. 中国物理B, 2021, 30(6): 60304-060304.
[15]	Ke Wang(王珂), Xiaopeng Yan(闫晓鹏), Ze Li(李泽), Xinhong Hao(郝新红), and Honghai Yu(于洪海). Blind parameter estimation of pseudo-random binary code-linear frequency modulation signal based on Duffing oscillator at low SNR[J]. 中国物理B, 2021, 30(5): 50708-050708.