Reinforcement-learning calibration of coherent-state receivers on variable-loss optical channels

Authors

Image provided by Matias Bilkis
Matias
Bilkis
Autonomous University of Barcelona
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John
Calsamiglia
Física Teòrica: Informació i Fenòmens Quàntics, Departament de Física, Universitat Autònoma de Barcelona
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Matteo
Rosati
Física Teòrica: Informació i Fenòmens Quàntics, Departament de Física, Universitat Autònoma de Barcelona

Abstract

We study the problem of calibrating a quantum receiver for optical coherent states when transmitted on a quantum optical channel with variable transmissivity, a common model for long-distance optical-fiber and free/deep-space optical communication. We optimize the error probability of legacy receivers, such as Kennedy's and Dolinar's, on average with respect to the channel transmissivity distribution. We then compare our results with the ultimate error probability attainable by a general quantum device, computing the Helstrom bound for mixtures of coherent-state hypotheses, for the first time to our knowledge, and with homodyne measurements. Then, we employ a recently introduced library of shallow reinforcement learning methods, demonstrating that the receiver can be calibrated under practical conditions in real time, based only on training samples of the signals transmitted through the variable channel.

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