A Survey of Machine Learning Assisted Continuous-Variable Quantum Key Distribution
Abstrak
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.
Topik & Kata Kunci
Penulis (3)
Nathan K. Long
Robert Malaney
Kenneth J. Grant
Akses Cepat
- Tahun Terbit
- 2023
- Sumber Database
- DOAJ
- DOI
- 10.3390/info14100553
- Akses
- Open Access ✓