Learning Feedback Control Strategies for Quantum Metrology

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Journal Title
Journal ISSN
Volume Title
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Date
2022-04-14
Major/Subject
Mcode
Degree programme
Language
en
Pages
15
1-15
Series
PRX Quantum, Volume 3, issue 2
Abstract
We consider the problem of frequency estimation for a single bosonic field evolving under a squeezing Hamiltonian and continuously monitored via homodyne detection. In particular, we exploit reinforcement learning techniques to devise feedback control strategies achieving increased estimation precision. We show that the feedback control determined by the neural network greatly surpasses in the long-time limit the performances of both the "no-control"strategy and the standard "open-loop control"strategy, which we considered as benchmarks. We indeed observe how the devised strategy is able to optimize the nontrivial estimation problem by preparing a large fraction of trajectories corresponding to more sensitive quantum conditional states.
Description
Funding Information: We thank F. Albarelli and M. Paris for helpful discussions. M.A.C.R. acknowledges financial support from the Academy of Finland via the Centre of Excellence program (Project No. 336810). M.G.G. and D.T. acknowledge support from the Sviluppo UniMi 2018 initiative. The computer resources of the Finnish IT Center for Science (CSC) and the FGCI project (Finland) are acknowledged. Publisher Copyright: © 2022 authors. Published by the American Physical Society.
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Citation
Fallani , A , Rossi , M A C , Tamascelli , D & Genoni , M G 2022 , ' Learning Feedback Control Strategies for Quantum Metrology ' , PRX Quantum , vol. 3 , no. 2 , 020310 , pp. 1-15 . https://doi.org/10.1103/PRXQuantum.3.020310