Abstract
We propose a novel scheme for discretely-modulated continuous-variable quantum key distribution (CVQKD) using machine learning technologies, which called multi-label learning-based CVQKD (ML-CVQKD). In particular, the proposed scheme divides the whole quantum system into state learning process and state prediction process. The former is used for training and estimating classifier, and the latter is used for generating final secret key. Meanwhile, a multi-label classification algorithm (MLCA) 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 based on the linear bosonic channel assumption shows that MLCA-embedded ML-CVQKD outperforms other existing discretely-modulated CVQKD protocols, such as four-state protocol and eight-state protocol, as well as the original Gaussian-modulated CVQKD protocol, and it will be further enhanced with the increase of modulation variance.
Highlights
Continuous-variable quantum key distribution (CVQKD) [1, 2] has been a hotspot in quantum communication and quantum cryptography
We show that the proposed scheme waives the necessity of small modulation variance in discretely-modulated continuous-variable quantum key distribution (CVQKD), so that quantum multi-label classification (QMLC) can exploit larger and reasonable variance to more precisely predict unknown coherent state, thereby enhancing the performance of discretelymodulated CVQKD system
We have demonstrated the performance of QMLC-embedded ML-CVQKD system in terms of machine learning-based metrics and have interpreted its security through an example
Summary
Continuous-variable quantum key distribution (CVQKD) [1, 2] has been a hotspot in quantum communication and quantum cryptography. Alice usually encodes key bits in the quadratures (pand q) of optical field [14], while Bob can restore the secret key bits through high-speed and high-efficiency coherent detection techniques This strategy usually has a repetition rate higher than that of single-photon detections so that Gaussian-modulated CVQKD could potentially achieve higher secret key rate. We further propose a novel scheme, called MLCVQKD, for discretely-modulated CVQKD using multilabel learning technology This scheme is quite different from traditional discretely-modulated CVQKD, it 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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have