Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/146595
Title: Probe optimization for quantum metrology via closed-loop learning control
Authors: Yang, Xiaodong
Thompson, Jayne
Wu, Ze
Gu, Mile
Peng, Xinhua
Du, Jiangfeng
Keywords: Science::Physics
Issue Date: 2020
Source: Yang, 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-z
Project: NRF-NRFF2016-02
2017-T1-002-043
2019-T1-002-015
NRF2017-NRF-ANR004
Journal: npj Quantum Information
Abstract: Experimentally 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.
URI: https://hdl.handle.net/10356/146595
ISSN: 2056-6387
DOI: 10.1038/s41534-020-00292-z
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/.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SPMS Journal Articles

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