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Continuous-variable quantum key distribution with extended Kalman filter assisted by recurrent neural networks for phase estimation.

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Title: Continuous-variable quantum key distribution with extended Kalman filter assisted by recurrent neural networks for phase estimation.
Authors: KASTHURI, P.1 prakashp_mit@annauniv.edu, PRAKASH, P.1, VATHSAN, M. S. SOWMYAA1, SASITHRADEVI, A.2
Source: Optica Applicata. 2024, Vol. 54 Issue 4, p483-495. 13p.
Subjects: Phase estimation (Electronics), Kalman filtering, Telecommunication systems, Recurrent neural networks, Quantum cryptography, Quantum communication
Abstract: Continuous-variable quantum key distribution (CV-QKD) holds promise for enhancing security in communication networks. However, obtaining a higher secure key rate poses challenges, particularly in reliable phase estimation. So, it is very necessary for CV-QKD implementations with independent local oscillator (LO) to employ carrier recovery along with precise phase estimation. Our methodology combines extended Kalman filters (EKF) with recurrent neural networks (RNNs) to enhance the accuracy of phase recovery for locally generated LO signals. Using numerical simulations, we evaluate the achievable secret key rates for different transmission distances and line widths. The proposed method achieves a phase error of approximately 1×10-4, leading to positive secure key rates for distances up to 40 km. This method of phase tracking solves the problem and is effective in real-time deployment of CV-QKD in communication networks. [ABSTRACT FROM AUTHOR]
Copyright of Optica Applicata is the property of Oficyna Wydawnicza Politechniki Wroclawskiej and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: <searchLink fieldCode="JN" term="%22Optica+Applicata%22">Optica Applicata</searchLink>. 2024, Vol. 54 Issue 4, p483-495. 13p.
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  Data: <searchLink fieldCode="DE" term="%22Phase+estimation+%28Electronics%29%22">Phase estimation (Electronics)</searchLink><br /><searchLink fieldCode="DE" term="%22Kalman+filtering%22">Kalman filtering</searchLink><br /><searchLink fieldCode="DE" term="%22Telecommunication+systems%22">Telecommunication systems</searchLink><br /><searchLink fieldCode="DE" term="%22Recurrent+neural+networks%22">Recurrent neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Quantum+cryptography%22">Quantum cryptography</searchLink><br /><searchLink fieldCode="DE" term="%22Quantum+communication%22">Quantum communication</searchLink>
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  Data: Continuous-variable quantum key distribution (CV-QKD) holds promise for enhancing security in communication networks. However, obtaining a higher secure key rate poses challenges, particularly in reliable phase estimation. So, it is very necessary for CV-QKD implementations with independent local oscillator (LO) to employ carrier recovery along with precise phase estimation. Our methodology combines extended Kalman filters (EKF) with recurrent neural networks (RNNs) to enhance the accuracy of phase recovery for locally generated LO signals. Using numerical simulations, we evaluate the achievable secret key rates for different transmission distances and line widths. The proposed method achieves a phase error of approximately 1×10-4, leading to positive secure key rates for distances up to 40 km. This method of phase tracking solves the problem and is effective in real-time deployment of CV-QKD in communication networks. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
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  Data: <i>Copyright of Optica Applicata is the property of Oficyna Wydawnicza Politechniki Wroclawskiej and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.37190/oa/195026
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      – Code: eng
        Text: English
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        PageCount: 13
        StartPage: 483
    Subjects:
      – SubjectFull: Phase estimation (Electronics)
        Type: general
      – SubjectFull: Kalman filtering
        Type: general
      – SubjectFull: Telecommunication systems
        Type: general
      – SubjectFull: Recurrent neural networks
        Type: general
      – SubjectFull: Quantum cryptography
        Type: general
      – SubjectFull: Quantum communication
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      – TitleFull: Continuous-variable quantum key distribution with extended Kalman filter assisted by recurrent neural networks for phase estimation.
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              M: 10
              Text: 2024
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              Y: 2024
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