Advanced Photonics, Volume. 5, Issue 1, 016005(2023)

Deep reinforcement learning for quantum multiparameter estimation

Valeria Cimini1, Mauro Valeri1, Emanuele Polino1, Simone Piacentini2, Francesco Ceccarelli2, Giacomo Corrielli2, Nicolò Spagnolo1, Roberto Osellame2, and Fabio Sciarrino1、*
Author Affiliations
  • 1Sapienza Università di Roma, Dipartimento di Fisica, Roma, Italy
  • 2Istituto di Fotonica e Nanotecnologie, Consiglio Nazionale delle Ricerche, Milano, Italy
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    [17] M. Valeri et al. Experimental multiparameter quantum metrology in adaptive regime(2022).

    [26] V. Gebhart et al. Learning quantum systems(2022).

    [27] A. Dawid et al. Modern applications of machine learning in quantum sciences(2022).

    [41] E. Alpaydin. Introduction to Machine Learning(2020).

    [42] R. S. Sutton, A. G. Barto. Reinforcement Learning: An Introduction(2018).

    [63] R. Osellame, G. Cerullo, R. Ramponi. Femtosecond Laser Micromachining: Photonic and Microfluidic Devices in Transparent Materials, 123(2012).

    [72] D. P. Kingma, J. Ba. Adam: a method for stochastic optimization(2014).

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    Valeria Cimini, Mauro Valeri, Emanuele Polino, Simone Piacentini, Francesco Ceccarelli, Giacomo Corrielli, Nicolò Spagnolo, Roberto Osellame, Fabio Sciarrino, "Deep reinforcement learning for quantum multiparameter estimation," Adv. Photon. 5, 016005 (2023)

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    Paper Information

    Category: Research Articles

    Received: Sep. 27, 2022

    Accepted: Dec. 27, 2022

    Posted: Jan. 4, 2023

    Published Online: Feb. 7, 2023

    The Author Email: Sciarrino Fabio (fabio.sciarrino@uniroma1.it)

    DOI:10.1117/1.AP.5.1.016005

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