Machine learning aided carrier recovery in continuous-variable quantum key distribution

Hou Man Chin*, Nitin Jain, Darko Zibar, Ulrik L. Andersen, Tobias Gehring

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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Abstract

The secret key rate of a continuous-variable quantum key distribution (CV-QKD) system is limited by excess noise. A key issue typical to all modern CV-QKD systems implemented with a reference or pilot signal and an independent local oscillator is controlling the excess noise generated from the frequency and phase noise accrued by the transmitter and receiver. Therefore accurate phase estimation and compensation, so-called carrier recovery, is a critical subsystem of CV-QKD. Here, we explore the implementation of a machine learning framework based on Bayesian inference, namely an unscented Kalman filter (UKF), for estimation of phase noise and compare it to a standard reference method and a previously demonstrated machine learning method. Experimental results obtained over a 20-km fibre-optic link indicate that the UKF can ensure very low excess noise even at low pilot powers. The measurements exhibited low variance and high stability in excess noise over a wide range of pilot signal to noise ratios. This may enable CV-QKD systems with low hardware implementation complexity which can seamlessly work on diverse transmission lines.

Original languageEnglish
Article number20
Journalnpj Quantum Information
Volume7
Issue number1
ISSN2056-6387
DOIs
Publication statusPublished - Dec 2021

Bibliographical note

Funding Information:
The authors gratefully acknowledge support by the European Research Council through the ERC-CoG FRECOM project (grant agreement no. 771878), the Danish National Research Foundation, Center for Macroscopic Quantum States (bigQ, DNRF142) and SPOC Research Center of Excellence and EU project CiViQ (grant agreement no. 820466).

Publisher Copyright:
© 2021, The Author(s).

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