Deep reinforcement learning for quantum multiparameter estimation
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Valeri, Mauro
Polino, Emanuele
Piacentini, Simone
Ceccarelli, Francesco
Corrielli, Giacomo
Spagnolo, Nicolo
Osellame, Roberto
Sciarrino, Fabio
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Abstract
Estimation of physical quantities is at the core of most scientific research, and the use of quantum devices promises to enhance its performances. In real scenarios, it is fundamental to consider that resources are limited, and Bayesian adaptive estimation represents a powerful approach to efficiently allocate, during the estimation process, all the available resources. However, this framework relies on the precise knowledge of the system model, retrieved with a fine calibration, with results that are often computationally and experimentally demanding. We introduce a model-free and deep-learning-based approach to efficiently implement realistic Bayesian quantum metrology tasks accomplishing all the relevant challenges, without relying on any a priori knowledge of the system. To overcome this need, a neural network is trained directly on experimental data to learn the multiparameter Bayesian update. Then the system is set at its optimal working point through feedback provided by a reinforcement learning algorithm trained to reconstruct and enhance experiment heuristics of the investigated quantum sensor. Notably, we prove experimentally the achievement of higher estimation performances than standard methods, demonstrating the strength of the combination of these two black-box algorithms on an integrated photonic circuit. Our work represents an important step toward fully artificial intelligence-based quantum metrology.
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Advanced Photonics
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5
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1
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© The Authors. Published by SPIE and CLP under a Creative Commons Attribution 4.0 International License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.
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Atomic, molecular and optical physics
Science & Technology
Physical Sciences
Optics
quantum sensing
integrated photonics
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Cimini, V; Valeri, M; Polino, E; Piacentini, S; Ceccarelli, F; Corrielli, G; Spagnolo, N; Osellame, R; Sciarrino, F, Deep reinforcement learning for quantum multiparameter estimation, Advanced Photonics, 2023, 5 (1), pp. 016005