Secure Continuous-Variable Quantum Key Distribution with Machine Learning

Quantum key distribution (QKD) offers information-theoretical security, while real systems are thought not to promise practical security effectively. In the practical continuous-variable (CV) QKD system, the deviations between realistic devices and idealized models might introduce vulnerabilities fo...

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Autores principales:	Duan Huang, Susu Liu, Ling Zhang
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	CVQKD machine learning attack and defense Applied optics. Photonics TA1501-1820
Acceso en línea:	https://doaj.org/article/db007b280c72486a86f3b20ae3583ec3
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spelling	oai:doaj.org-article:db007b280c72486a86f3b20ae3583ec32021-11-25T18:43:38ZSecure Continuous-Variable Quantum Key Distribution with Machine Learning10.3390/photonics81105112304-6732https://doaj.org/article/db007b280c72486a86f3b20ae3583ec32021-11-01T00:00:00Zhttps://www.mdpi.com/2304-6732/8/11/511https://doaj.org/toc/2304-6732Quantum key distribution (QKD) offers information-theoretical security, while real systems are thought not to promise practical security effectively. In the practical continuous-variable (CV) QKD system, the deviations between realistic devices and idealized models might introduce vulnerabilities for eavesdroppers and stressors for two parties. However, the common quantum hacking strategies and countermeasures inevitably increase the complexity of practical CV systems. Machine-learning techniques are utilized to explore how to perceive practical imperfections. Here, we review recent works on secure CVQKD systems with machine learning, where the methods for detections and attacks were studied.Duan HuangSusu LiuLing ZhangMDPI AGarticleCVQKDmachine learningattack and defenseApplied optics. PhotonicsTA1501-1820ENPhotonics, Vol 8, Iss 511, p 511 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	CVQKD machine learning attack and defense Applied optics. Photonics TA1501-1820
spellingShingle	CVQKD machine learning attack and defense Applied optics. Photonics TA1501-1820 Duan Huang Susu Liu Ling Zhang Secure Continuous-Variable Quantum Key Distribution with Machine Learning
description	Quantum key distribution (QKD) offers information-theoretical security, while real systems are thought not to promise practical security effectively. In the practical continuous-variable (CV) QKD system, the deviations between realistic devices and idealized models might introduce vulnerabilities for eavesdroppers and stressors for two parties. However, the common quantum hacking strategies and countermeasures inevitably increase the complexity of practical CV systems. Machine-learning techniques are utilized to explore how to perceive practical imperfections. Here, we review recent works on secure CVQKD systems with machine learning, where the methods for detections and attacks were studied.
format	article
author	Duan Huang Susu Liu Ling Zhang
author_facet	Duan Huang Susu Liu Ling Zhang
author_sort	Duan Huang
title	Secure Continuous-Variable Quantum Key Distribution with Machine Learning
title_short	Secure Continuous-Variable Quantum Key Distribution with Machine Learning
title_full	Secure Continuous-Variable Quantum Key Distribution with Machine Learning
title_fullStr	Secure Continuous-Variable Quantum Key Distribution with Machine Learning
title_full_unstemmed	Secure Continuous-Variable Quantum Key Distribution with Machine Learning
title_sort	secure continuous-variable quantum key distribution with machine learning
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/db007b280c72486a86f3b20ae3583ec3
work_keys_str_mv	AT duanhuang securecontinuousvariablequantumkeydistributionwithmachinelearning AT susuliu securecontinuousvariablequantumkeydistributionwithmachinelearning AT lingzhang securecontinuousvariablequantumkeydistributionwithmachinelearning
_version_	1718410804252901376

Secure Continuous-Variable Quantum Key Distribution with Machine Learning

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