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

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 (Q$k$NN) classifier is designed for predicting the lossy discretely-modulated coherent states (DMCSs) at Bob’s side. The performance of the proposed Q$k$NN-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.