High-rate discretely-modulated continuous-variable quantum key
  distribution using quantum machine learning
Qin Liao Jieyu Liu Anqi Huang Lei Huang Zhuoying Fei Xiquan Fu
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.