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Journals >Advanced Photonics >Volume 5 >Issue 2 >Page 020501 > Article

Advanced Photonics
Vol. 5, Issue 2, 020501 (2023)

Machine learning for optical quantum metrology

Luca Pezzè^1、2

Author Affiliations

¹Istituto Nazionale di Ottica, INO-CNR, Firenze, Italy

²European Laboratory for Nonlinear Spectroscopy, LENS, Sesto Fiorentino, Italy

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DOI: 10.1117/1.AP.5.2.020501 Cite this Article Set citation alerts

Luca Pezzè. Machine learning for optical quantum metrology[J]. Advanced Photonics, 2023, 5(2): 020501 Copy Citation Text

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References

[1] V. Gebhart et al. Learning quantum systems. Nat. Rev. Phys.(2023).

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

[3] V. Giovannetti, S. Lloyd, L. Maccone. Advances in quantum metrology. Nat. Photonics, 5, 222-229(2011).

[4] L. Pezzè et al. Quantum metrology with nonclassical states of atomic ensembles. Rev. Mod. Phys., 90, 035005(2018).

[5] E. Polino et al. Photonic quantum metrology. AVS Quantum Sci., 2, 024703(2020).

[6] V. Cimini et al. Calibration of quantum sensors by neural networks. Phys. Rev. Lett., 123, 230502(2019).

[7] S. P. Nolan, L. Pezzè, A. Smerzi. Frequentist parameter estimation with supervised learning. AVS Quantum Sci., 3, 034401(2021).

[8] V. Cimini et al. Calibration of multiparameter sensors via machine learning at the single-photon level. Phys. Rev. Appl., 15, 044003(2021).

[9] S. P. Nolan, A. Smerzi, L. Pezzè. A machine learning approach to Bayesian parameter estimation. NPJ Quantum Inf., 7, 1(2021).

[10] A. Hentschel, B. C. Sanders. Efficient algorithm for optimizing adaptive quantum metrology processes. Phys. Rev. Lett., 104, 063603(2010).

[11] N. B. Lovett et al. Differential evolution for many-particle adaptive quantum metrology. Phys. Rev. Lett., 110, 220501(2013).

[12] T. Xiao et al. Continuous-variable quantum phase estimation based on machine learning. NPJ Quantum Inf., 9, 12410(2019).

[13] H. Xu et al. Generalizable control for quantum parameter estimation through reinforcement learning. NPJ Quantum Inf., 5, 82(2019).

[14] X. Yang et al. Probe optimization for quantum metrology via closed-loop learning control. NPJ Quantum Inf., 6, 62(2020).

[15] L. J. Fiderer, J. Schuff, D. Braun. Neural-network heuristics for adaptive Bayesian quantum estimation. PRX Quantum, 2, 020303(2021).

[16] A. Fallani et al. Learning feedback control strategies for quantum metrology. PRX Quantum, 3, 020310(2022).

[17] V. Cimini et al. Deep reinforcement learning for quantum multiparameter estimation. Adv. Photonics, 5, 016005(2023).

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