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Multi-label learning for improving discretely-modulated continuous-variable quantum key distribution

New journal of physics, 2020-08, Vol.22 (8), p.83086 [Peer Reviewed Journal]

2020 The Author(s). Published by IOP Publishing Ltd on behalf of the Institute of Physics and Deutsche Physikalische Gesellschaft ;2020. This work is published under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1367-2630 ;EISSN: 1367-2630 ;DOI: 10.1088/1367-2630/abab3c ;CODEN: NJOPFM

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  • Title:
    Multi-label learning for improving discretely-modulated continuous-variable quantum key distribution
  • Author: Liao, Qin ; Xiao, Gang ; Zhong, Hai ; Guo, Ying
  • Subjects: Algorithms ; Classifiers ; Continuity (mathematics) ; Feature extraction ; Machine learning ; Protocol ; Quantum cryptography ; quantum key distribution ; Quantum theory
  • Is Part Of: New journal of physics, 2020-08, Vol.22 (8), p.83086
  • Description: 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.
  • Publisher: Bristol: IOP Publishing
  • Language: English
  • Identifier: ISSN: 1367-2630
    EISSN: 1367-2630
    DOI: 10.1088/1367-2630/abab3c
    CODEN: NJOPFM
  • Source: Freely Accessible Journals
    Open Access: IOP Publishing Free Content
    IOPscience (Open Access)
    ROAD: Directory of Open Access Scholarly Resources
    ProQuest Central
    DOAJ Directory of Open Access Journals
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