Learning Feedback Control Strategies for Quantum Metrology

dc.contributorAalto-yliopistofi
dc.contributorAalto Universityen
dc.contributor.authorFallani, Alessioen_US
dc.contributor.authorRossi, Matteo A.C.en_US
dc.contributor.authorTamascelli, Darioen_US
dc.contributor.authorGenoni, Marco G.en_US
dc.contributor.departmentDepartment of Applied Physicsen
dc.contributor.groupauthorQuantum Phenomena and Devicesen
dc.contributor.groupauthorCentre of Excellence in Quantum Technology, QTFen
dc.contributor.organizationUniversity of Milanen_US
dc.date.accessioned2022-05-17T06:50:15Z
dc.date.available2022-05-17T06:50:15Z
dc.date.issued2022-04-14en_US
dc.descriptionFunding 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.
dc.description.abstractWe 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.en
dc.description.versionPeer revieweden
dc.format.extent15
dc.format.extent1-15
dc.format.mimetypeapplication/pdfen_US
dc.identifier.citationFallani, 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.3.020310en
dc.identifier.doi10.1103/PRXQuantum.3.020310en_US
dc.identifier.issn2691-3399
dc.identifier.otherPURE UUID: 1f9a5eb1-da28-42e2-9455-910c865f9227en_US
dc.identifier.otherPURE ITEMURL: https://research.aalto.fi/en/publications/1f9a5eb1-da28-42e2-9455-910c865f9227en_US
dc.identifier.otherPURE LINK: http://www.scopus.com/inward/record.url?scp=85128833343&partnerID=8YFLogxKen_US
dc.identifier.otherPURE FILEURL: https://research.aalto.fi/files/83058851/Learning_Feedback_Control_Strategies_for_Quantum_Metrology.pdfen_US
dc.identifier.urihttps://aaltodoc.aalto.fi/handle/123456789/114359
dc.identifier.urnURN:NBN:fi:aalto-202205173219
dc.language.isoenen
dc.publisherAmerican Physical Society
dc.relation.ispartofseriesPRX Quantumen
dc.relation.ispartofseriesVolume 3, issue 2en
dc.rightsopenAccessen
dc.titleLearning Feedback Control Strategies for Quantum Metrologyen
dc.typeA1 Alkuperäisartikkeli tieteellisessä aikakauslehdessäfi
dc.type.versionpublishedVersion
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