Multi-label learning for improving discretely-modulated continuous-variable quantum key distribution
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 s...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IOP Publishing
2020-01-01
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| Series: | New Journal of Physics |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/1367-2630/abab3c |
| _version_ | 1827873102054293504 |
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| author | Qin Liao Gang Xiao Hai Zhong Ying Guo |
| author_facet | Qin Liao Gang Xiao Hai Zhong Ying Guo |
| author_sort | Qin Liao |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-12T16:32:46Z |
| format | Article |
| id | doaj.art-bb0e9e64d8d841cb8f3475074e8e0773 |
| institution | Directory Open Access Journal |
| issn | 1367-2630 |
| language | English |
| last_indexed | 2024-03-12T16:32:46Z |
| publishDate | 2020-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | New Journal of Physics |
| spelling | doaj.art-bb0e9e64d8d841cb8f3475074e8e07732023-08-08T15:26:28ZengIOP PublishingNew Journal of Physics1367-26302020-01-0122808308610.1088/1367-2630/abab3cMulti-label learning for improving discretely-modulated continuous-variable quantum key distributionQin Liao0https://orcid.org/0000-0001-7692-7476Gang Xiao1Hai Zhong2Ying Guo3https://orcid.org/0000-0002-9306-2061College of Computer Science and Electronic Engineering, Hunan University , Changsha 410082, People’s Republic of China; Institute of Advanced Photoelectric Detection and Quantum System, Central South University , Changsha 410075, People’s Republic of ChinaCollege of Computer Science and Electronic Engineering, Hunan University , Changsha 410082, People’s Republic of ChinaInstitute of Advanced Photoelectric Detection and Quantum System, Central South University , Changsha 410075, People’s Republic of China; School of Computer Science and Engineering, Central South University , Changsha 410083, People’s Republic of ChinaInstitute of Advanced Photoelectric Detection and Quantum System, Central South University , Changsha 410075, People’s Republic of China; School of Automation, Central South University , Changsha 410083, People’s Republic of ChinaWe 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.https://doi.org/10.1088/1367-2630/abab3cquantum key distributionmachine learningquantum cryptography |
| spellingShingle | Qin Liao Gang Xiao Hai Zhong Ying Guo Multi-label learning for improving discretely-modulated continuous-variable quantum key distribution New Journal of Physics quantum key distribution machine learning quantum cryptography |
| title | Multi-label learning for improving discretely-modulated continuous-variable quantum key distribution |
| title_full | Multi-label learning for improving discretely-modulated continuous-variable quantum key distribution |
| title_fullStr | Multi-label learning for improving discretely-modulated continuous-variable quantum key distribution |
| title_full_unstemmed | Multi-label learning for improving discretely-modulated continuous-variable quantum key distribution |
| title_short | Multi-label learning for improving discretely-modulated continuous-variable quantum key distribution |
| title_sort | multi label learning for improving discretely modulated continuous variable quantum key distribution |
| topic | quantum key distribution machine learning quantum cryptography |
| url | https://doi.org/10.1088/1367-2630/abab3c |
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