Long, N.K.; Malaney, R.; Grant, K.J. A Survey of Machine Learning Assisted Continuous-Variable Quantum Key Distribution. Information2023, 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.
Long, N.K.; Malaney, R.; Grant, K.J. A Survey of Machine Learning Assisted Continuous-Variable Quantum Key Distribution. Information2023, 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.
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
