机器学习辅助连续变量量子密钥分发研究进展

IF 2.4 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS
Nathan K. Long, Robert Malaney, Kenneth J. Grant
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引用次数: 0

摘要

连续变量量子密钥分发(CV-QKD)显示了信息理论安全全球通信网络快速发展的潜力;然而,CV-QKD实施的复杂性仍然是一个限制性因素。机器学习(ML)最近在缓解这些复杂性方面显示出了希望。ML几乎应用于CV-QKD协议的每个阶段,包括ML辅助的相位误差估计、过量噪声估计、状态识别、参数估计和优化、密钥筛选、信息协调和密钥率估计。本研究对ml辅助CV-QKD的现有文献进行了全面分析。此外,该调查还比较了辅助CV-QKD的ML算法与它们旨在增强的传统算法,并为ML辅助CV-QKD研究的未来方向提供了建议。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Survey of Machine Learning Assisted Continuous-Variable Quantum Key Distribution
Continuous-variable quantum key distribution (CV-QKD) shows potential for the rapid development of an information-theoretic secure global communication network; however, the complexities of CV-QKD implementation remain a restrictive factor. Machine learning (ML) has recently shown promise in alleviating these complexities. ML has been applied to almost every stage of CV-QKD protocols, including ML-assisted phase error estimation, excess noise estimation, state discrimination, parameter estimation and optimization, key sifting, information reconciliation, and key rate estimation. This survey provides a comprehensive analysis of the current literature on ML-assisted CV-QKD. In addition, the survey compares the ML algorithms assisting CV-QKD with the traditional algorithms they aim to augment, as well as providing recommendations for future directions for ML-assisted CV-QKD research.
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来源期刊
Information (Switzerland)
Information (Switzerland) Computer Science-Information Systems
CiteScore
6.90
自引率
0.00%
发文量
515
审稿时长
11 weeks
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