Machine Learning-Assisted Measurement Device-Independent Quantum Key Distribution on Reference Frame Calibration
In most of the realistic measurement device-independent quantum key distribution (MDI-QKD) systems, efficient, real-time feedback controls are required to maintain system stability when facing disturbance from either external environment or imperfect internal components. Traditionally, people either...
| Main Authors: | , , , , , |
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| Format: | Article |
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MDPI AG
2021-09-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/23/10/1242 |
| _version_ | 1797514659193946112 |
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| author | Sihao Zhang Jingyang Liu Guigen Zeng Chunhui Zhang Xingyu Zhou Qin Wang |
| author_facet | Sihao Zhang Jingyang Liu Guigen Zeng Chunhui Zhang Xingyu Zhou Qin Wang |
| author_sort | Sihao Zhang |
| collection | DOAJ |
| description | In most of the realistic measurement device-independent quantum key distribution (MDI-QKD) systems, efficient, real-time feedback controls are required to maintain system stability when facing disturbance from either external environment or imperfect internal components. Traditionally, people either use a “scanning-and-transmitting” program or insert an extra device to make a phase reference frame calibration for a stable high-visibility interference, resulting in higher system complexity and lower transmission efficiency. In this work, we build a machine learning-assisted MDI-QKD system, where a machine learning model—the long short-term memory (LSTM) network—is for the first time to apply onto the MDI-QKD system for reference frame calibrations. In this machine learning-assisted MDI-QKD system, one can predict out the phase drift between the two users in advance, and actively perform real-time phase compensations, dramatically increasing the key transmission efficiency. Furthermore, we carry out corresponding experimental demonstration over 100 km and 250 km commercial standard single-mode fibers, verifying the effectiveness of the approach. |
| first_indexed | 2024-03-10T06:34:44Z |
| format | Article |
| id | doaj.art-7729797ed61a4a1284da6f36f22dea52 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-10T06:34:44Z |
| publishDate | 2021-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-7729797ed61a4a1284da6f36f22dea522023-11-22T18:09:58ZengMDPI AGEntropy1099-43002021-09-012310124210.3390/e23101242Machine Learning-Assisted Measurement Device-Independent Quantum Key Distribution on Reference Frame CalibrationSihao Zhang0Jingyang Liu1Guigen Zeng2Chunhui Zhang3Xingyu Zhou4Qin Wang5Institute of Quantum Information and Technology, Nanjing University of Posts and Telecommunications, Nanjing 210003, ChinaInstitute of Quantum Information and Technology, Nanjing University of Posts and Telecommunications, Nanjing 210003, ChinaInstitute of Quantum Information and Technology, Nanjing University of Posts and Telecommunications, Nanjing 210003, ChinaInstitute of Quantum Information and Technology, Nanjing University of Posts and Telecommunications, Nanjing 210003, ChinaInstitute of Quantum Information and Technology, Nanjing University of Posts and Telecommunications, Nanjing 210003, ChinaInstitute of Quantum Information and Technology, Nanjing University of Posts and Telecommunications, Nanjing 210003, ChinaIn most of the realistic measurement device-independent quantum key distribution (MDI-QKD) systems, efficient, real-time feedback controls are required to maintain system stability when facing disturbance from either external environment or imperfect internal components. Traditionally, people either use a “scanning-and-transmitting” program or insert an extra device to make a phase reference frame calibration for a stable high-visibility interference, resulting in higher system complexity and lower transmission efficiency. In this work, we build a machine learning-assisted MDI-QKD system, where a machine learning model—the long short-term memory (LSTM) network—is for the first time to apply onto the MDI-QKD system for reference frame calibrations. In this machine learning-assisted MDI-QKD system, one can predict out the phase drift between the two users in advance, and actively perform real-time phase compensations, dramatically increasing the key transmission efficiency. Furthermore, we carry out corresponding experimental demonstration over 100 km and 250 km commercial standard single-mode fibers, verifying the effectiveness of the approach.https://www.mdpi.com/1099-4300/23/10/1242measurement-device-independent quantum key distributionreference frame calibrationmachine learningtransmission efficiencybiased basis choice |
| spellingShingle | Sihao Zhang Jingyang Liu Guigen Zeng Chunhui Zhang Xingyu Zhou Qin Wang Machine Learning-Assisted Measurement Device-Independent Quantum Key Distribution on Reference Frame Calibration Entropy measurement-device-independent quantum key distribution reference frame calibration machine learning transmission efficiency biased basis choice |
| title | Machine Learning-Assisted Measurement Device-Independent Quantum Key Distribution on Reference Frame Calibration |
| title_full | Machine Learning-Assisted Measurement Device-Independent Quantum Key Distribution on Reference Frame Calibration |
| title_fullStr | Machine Learning-Assisted Measurement Device-Independent Quantum Key Distribution on Reference Frame Calibration |
| title_full_unstemmed | Machine Learning-Assisted Measurement Device-Independent Quantum Key Distribution on Reference Frame Calibration |
| title_short | Machine Learning-Assisted Measurement Device-Independent Quantum Key Distribution on Reference Frame Calibration |
| title_sort | machine learning assisted measurement device independent quantum key distribution on reference frame calibration |
| topic | measurement-device-independent quantum key distribution reference frame calibration machine learning transmission efficiency biased basis choice |
| url | https://www.mdpi.com/1099-4300/23/10/1242 |
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