High-rate discretely-modulated continuous-variable quantum key distribution using quantum machine learning

Qin Liao, Zhuoying Fei, Jieyu Liu, Anqi Huang, Lei Huang and Yijun Wang

Chaos, Solitons & Fractals, 2025, vol. 196, issue C

Abstract: Continuous-variable quantum key distribution (CVQKD) is one of the promising ways to ensure information security. In this paper, we propose a high-rate scheme for discretely-modulated (DM) CVQKD using quantum machine learning technologies, which divides the whole CVQKD system into three parts, i.e., the initialization part that is used for training and estimating quantum classifier, the prediction part that is used for generating highly correlated raw keys, and the data postprocessing part that generates the final secret key string shared by Alice and Bob. To this end, a low-complexity quantum k-nearest neighbor (QkNN) classifier is designed for predicting the lossy discretely-modulated coherent states (DMCSs) at Bob’s side. The performance of the proposed QkNN-based CVQKD especially in terms of machine learning metrics and complexity is analyzed, and its theoretical security is proved by using semi-definite program (SDP) method. Numerical simulation shows that the secret key rate of our proposed scheme is explicitly superior to that of the existing DM CVQKD protocols, and it can be further enhanced with the increase of modulation variance.

Keywords: Quantum machine learning; Quantum key distribution; kNN classification (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0960077925003443
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:196:y:2025:i:c:s0960077925003443

DOI: 10.1016/j.chaos.2025.116331

Access Statistics for this article

Chaos, Solitons & Fractals is currently edited by Stefano Boccaletti and Stelios Bekiros

More articles in Chaos, Solitons & Fractals from Elsevier
Bibliographic data for series maintained by Thayer, Thomas R. ().