  Discretely-modulated continuous-variable quantum key distribution (CVQKD) is
more suitable for long-distance transmission compared with its
Gaussian-modulated CVQKD counterpart. However, its security can only be
guaranteed when modulation variance is very small, which limits its further
development. To solve this problem, in this work, we propose a novel scheme for
discretely-modulated CVQKD using multi-label learning technology, called
multi-label learning-based CVQKD (ML-CVQKD). In particular, the proposed scheme
divides the whole quantum system into state learning and state prediction. The
former is used for training and estimating quantum classifier, and the latter
is used for generating final secret key. A quantum multi-label classification
(QMLC) algorithm is also designed as an embedded classifier for distinguishing
coherent state. Feature extraction for coherent state and related machine
learning-based metrics for the quantum classifier are successively suggested.
Security analysis shows that QMLC-embedded ML-CVQKD is able to immune
intercept-resend attack so that small modulation variance is no longer
compulsively required, thereby improving the performance of
discretely-modulated CVQKD system.
