Optical frequency comb noise characterization using machine learning

Brajato, Giovanni; Lundberg, Lars; Zibar, Darko

doi:10.1049/cp.2019.0889

Cited by 3 publications

(1 citation statement)

References 6 publications

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“…The talk was based on a recent survey [1] and a recent tutorial, [2] published by the speaker. Following the short introduction, she presented the results of the group's recent activities in the area of phase noise characterization for lasers and frequency combs [3,4], Raman amplifier inverse design and modelling [5][6][7], and auto-encoders for optical communication systems [8][9][10]. Additionally, to have a comprehensive view of the main applications of machine learning in optical communications and the current state-of-the-art, she discussed the key point of a few relevant surveys on the topic [11][12][13][14].…”

Section: Overviewmentioning

confidence: 99%

Summary:

Gordon¹,

Battou²,

Majurski³

et al. 2020

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Optical communication systems are expected to find use in new applications that require more intelligent and automated functionality. Optical networks are needed to address the high speeds and low latency of 5G wireless networks. The analog nature of optical transmission and the complexity of operation and management remain an impediment to greater use of software controls. The optical community at large has proposed many possible applications and avenues for using and implementing artificial intelligence and machine learning to improve functionality of optical systems for communications. However, broad agreement has yet to be reached due to both technical and non-technical reasons. On August 2 nd , 2019 The National Institute of Standards and Technology (NIST) Communications Technology Laboratory (CTL) hosted a Workshop on Machine Learning for Optical Communication Systems to bring together industry, academia and government in order to discuss the roll of AI and ML in optical communication systems. This document provides an overview and summary of the workshop.

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Section: Overviewmentioning

confidence: 99%

Summary:

Gordon¹,

Battou²,

Majurski³

et al. 2020

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

et al. 2021

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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.

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Photonic quantum metrology

Polino

Valeri

Spagnolo

et al. 2020

AVS Quantum Science

354

251

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Quantum metrology is one of the most promising applications of quantum technologies. The aim of this research field is the estimation of unknown parameters exploiting quantum resources, whose application can lead to enhanced performances with respect to classical strategies. Several physical quantum systems can be employed to develop quantum sensors, and photonic systems represent ideal probes for a large number of metrological tasks. Here, the authors review the basic concepts behind quantum metrology and then focus on the application of photonic technology for this task, with particular attention to phase estimation. The authors describe the current state of the art in the field in terms of platforms and quantum resources. Furthermore, the authors present the research area of multiparameter quantum metrology, where multiple parameters have to be estimated at the same time. The authors conclude by discussing the current experimental and theoretical challenges and the open questions toward implementation of photonic quantum sensors with quantum-enhanced performances in the presence of noise.

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Optical frequency comb noise characterization using machine learning

Cited by 3 publications

References 6 publications

Summary:

Summary:

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

Photonic quantum metrology

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