Preprint Review Version 1 Preserved in Portico This version is not peer-reviewed

A Survey of Machine Learning Assisted Continuous-Variable Quantum Key Distribution

Version 1 : Received: 18 August 2023 / Approved: 18 August 2023 / Online: 21 August 2023 (03:12:29 CEST)

A peer-reviewed article of this Preprint also exists.

Long, N.K.; Malaney, R.; Grant, K.J. A Survey of Machine Learning Assisted Continuous-Variable Quantum Key Distribution. Information 2023, 14, 553. Long, N.K.; Malaney, R.; Grant, K.J. A Survey of Machine Learning Assisted Continuous-Variable Quantum Key Distribution. Information 2023, 14, 553.

Abstract

Continuous-variable quantum key distribution (CV-QKD) shows potential for the rapid development of an information-theoretic secure global communications 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.

Keywords

continuous-variable quantum key distribution; machine learning; phase error estimation; parameter estimation; secure key rate; quantum key distribution

Subject

Engineering, Electrical and Electronic Engineering

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