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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 | Schools: | School of Physical and Mathematical Sciences | 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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