We propose a high-rate scheme for discretely-modulated continuous-variable quantum key distribution (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 the existing DM CVQKD protocols, and it can be further enhanced with the increase of modulation variance.

翻译：我们提出了一种利用量子机器学习技术的高速率离散调制连续变量量子密钥分发（DM CVQKD）方案。该方案将整个CVQKD系统划分为三个模块：用于训练和评估量子分类器的初始化模块、用于生成高度相关原始密钥的预测模块，以及用于生成Alice与Bob共享最终密钥串的数据后处理模块。为此，我们设计了低复杂度的量子k近邻（QkNN）分类器，用于预测Bob端有损离散调制相干态（DMCSs）。分析了所提基于QkNN的CVQKD方案在机器学习指标及复杂度方面的性能，并通过半定规划（SDP）方法证明了其理论安全性。数值仿真表明，所提方案的密钥率显著优于现有DM CVQKD协议，且随着调制方差的增大可进一步提升。