Please use this identifier to cite or link to this item: https://doi.org/10.1038/s41534-020-00292-z
Title: Probe optimization for quantum metrology via closed-loop learning control
Authors: Yang, X.
Thompson, J. 
Wu, Z.
Gu, M. 
Peng, X.
Du, J.
Issue Date: 2020
Publisher: Nature Research
Citation: 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. ScholarBank@NUS Repository. https://doi.org/10.1038/s41534-020-00292-z
Rights: Attribution 4.0 International
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. © 2020, The Author(s).
Source Title: npj Quantum Information
URI: https://scholarbank.nus.edu.sg/handle/10635/199337
ISSN: 20566387
DOI: 10.1038/s41534-020-00292-z
Rights: Attribution 4.0 International
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