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.

本文提出了一种使用量子机器学习技术的高速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 系统分为三个部分，分别是用于训练和估计量子分类器的初始化部分，用于生成高度相关原始密钥的预测部分以及生成 Alice 和 Bob 之间最终秘密密钥字符串的数据后处理部分。为此，设计了一种低复杂度的量子 k 最近邻（QkNN）分类器，用于预测 Bob 端的丢失性离散调制相干态（DMCSs）。分析了所提出的基于 QkNN 的 CVQKD 在机器学习指标和复杂性方面的性能，并用半定规划（SDP）方法证明了其理论安全性。数值模拟表明，我们提出的方案的秘密密钥速率明显优于现有的 DM CVQKD 协议，并且可以通过增加调制方差进一步增强。