Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/146595
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dc.contributor.authorYang, Xiaodongen_US
dc.contributor.authorThompson, Jayneen_US
dc.contributor.authorWu, Zeen_US
dc.contributor.authorGu, Mileen_US
dc.contributor.authorPeng, Xinhuaen_US
dc.contributor.authorDu, Jiangfengen_US
dc.date.accessioned2021-03-02T06:48:49Z-
dc.date.available2021-03-02T06:48:49Z-
dc.date.issued2020-
dc.identifier.citationYang, X., Thompson, J., Wu, Z., Gu, M., Peng, X., & Du, J. (2020). Probe optimization for quantum metrology via closed-loop learning control. npj Quantum Information, 6(1), 62-. doi:10.1038/s41534-020-00292-zen_US
dc.identifier.issn2056-6387en_US
dc.identifier.other0000-0003-4509-6045-
dc.identifier.other0000-0002-3746-244X-
dc.identifier.other0000-0002-5459-4313-
dc.identifier.other0000-0001-5260-2976-
dc.identifier.other0000-0001-8085-8012-
dc.identifier.urihttps://hdl.handle.net/10356/146595-
dc.description.abstractExperimentally achieving the precision that standard quantum metrology schemes promise is always challenging. Recently, additional controls were applied to design feasible quantum metrology schemes. However, these approaches generally does not consider ease of implementation, raising technological barriers impeding its realization. In this paper, we circumvent this problem by applying closed-loop learning control to propose a practical controlled sequential scheme for quantum metrology. Purity loss of the probe state, which relates to quantum Fisher information, is measured efficiently as the fitness to guide the learning loop. We confirm its feasibility and certain superiorities over standard quantum metrology schemes by numerical analysis and proof-of-principle experiments in a nuclear magnetic resonance system.en_US
dc.description.sponsorshipMinistry of Education (MOE)en_US
dc.description.sponsorshipNational Research Foundation (NRF)en_US
dc.language.isoenen_US
dc.relationNRF-NRFF2016-02en_US
dc.relation2017-T1-002-043en_US
dc.relation2019-T1-002-015en_US
dc.relationNRF2017-NRF-ANR004en_US
dc.relation.ispartofnpj Quantum Informationen_US
dc.rights© 2020 The Author(s). This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing,adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visithttp://creativecommons.org/licenses/by/4.0/.en_US
dc.subjectScience::Physicsen_US
dc.titleProbe optimization for quantum metrology via closed-loop learning controlen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Physical and Mathematical Sciencesen_US
dc.identifier.doi10.1038/s41534-020-00292-z-
dc.description.versionPublished versionen_US
dc.identifier.scopus2-s2.0-85088123665-
dc.identifier.issue1en_US
dc.identifier.volume6en_US
dc.subject.keywordsQuantum Informationen_US
dc.subject.keywordsQuantum Metrologyen_US
dc.description.acknowledgementThis work was supported by National Key Research and Development Program of China (Grant No. 2018YFA0306600), National Natural Science Foundation of China (Grants Nos. 11661161018 and 11927811), Anhui Initiative in Quantum Information Technologies (Grant No. AHY050000), the National Research Foundation (NRF) Singapore, under its NRFF Fellow programme (Award No. NRF-NRFF2016-02), the Singapore Ministry of Education Tier 1 Grant 2017-T1-002-043 and 2019-T1-002-015, the NRF-ANR Grant NRF2017-NRF-ANR004 VanQuTe, and the FQXi large grant: FQXi-RFP-1809 the role of quantum effects in simplifying adaptive agents, and FQXi-RFP-IPW-1903 are quantum agents more energetically efficient at making predictions. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not reflect the views of National Research Foundation, Singapore.en_US
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