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

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...

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Autores principales:	Hou-Man Chin, Nitin Jain, Darko Zibar, Ulrik L. Andersen, Tobias Gehring
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Physics QC1-999 Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/507e5e56c9ba425fb77a50d85751d1b9
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spelling	oai:doaj.org-article:507e5e56c9ba425fb77a50d85751d1b92021-12-02T14:06:53ZMachine learning aided carrier recovery in continuous-variable quantum key distribution10.1038/s41534-021-00361-x2056-6387https://doaj.org/article/507e5e56c9ba425fb77a50d85751d1b92021-02-01T00:00:00Zhttps://doi.org/10.1038/s41534-021-00361-xhttps://doaj.org/toc/2056-6387Abstract 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.Hou-Man ChinNitin JainDarko ZibarUlrik L. AndersenTobias GehringNature PortfolioarticlePhysicsQC1-999Electronic computers. Computer scienceQA75.5-76.95ENnpj Quantum Information, Vol 7, Iss 1, Pp 1-6 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Physics QC1-999 Electronic computers. Computer science QA75.5-76.95
spellingShingle	Physics QC1-999 Electronic computers. Computer science QA75.5-76.95 Hou-Man Chin Nitin Jain Darko Zibar Ulrik L. Andersen Tobias Gehring Machine learning aided carrier recovery in continuous-variable quantum key distribution
description	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.
format	article
author	Hou-Man Chin Nitin Jain Darko Zibar Ulrik L. Andersen Tobias Gehring
author_facet	Hou-Man Chin Nitin Jain Darko Zibar Ulrik L. Andersen Tobias Gehring
author_sort	Hou-Man Chin
title	Machine learning aided carrier recovery in continuous-variable quantum key distribution
title_short	Machine learning aided carrier recovery in continuous-variable quantum key distribution
title_full	Machine learning aided carrier recovery in continuous-variable quantum key distribution
title_fullStr	Machine learning aided carrier recovery in continuous-variable quantum key distribution
title_full_unstemmed	Machine learning aided carrier recovery in continuous-variable quantum key distribution
title_sort	machine learning aided carrier recovery in continuous-variable quantum key distribution
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/507e5e56c9ba425fb77a50d85751d1b9
work_keys_str_mv	AT houmanchin machinelearningaidedcarrierrecoveryincontinuousvariablequantumkeydistribution AT nitinjain machinelearningaidedcarrierrecoveryincontinuousvariablequantumkeydistribution AT darkozibar machinelearningaidedcarrierrecoveryincontinuousvariablequantumkeydistribution AT ulriklandersen machinelearningaidedcarrierrecoveryincontinuousvariablequantumkeydistribution AT tobiasgehring machinelearningaidedcarrierrecoveryincontinuousvariablequantumkeydistribution
_version_	1718391961682968576

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

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