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Fitting magnetic field gradient with Heisenberg-scaling accuracy

The linear function is possibly the simplest and the most used relation appearing in various areas of our world. A linear relation can be generally determined by the least square linear fitting (LSLF) method using several measured quantities depending on variables. This happens for such as detecting...

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Autores principales: Zhang, Yong-Liang, Wang, Huan, Jing, Li, Mu, Liang-Zhu, Fan, Heng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4260217/
https://www.ncbi.nlm.nih.gov/pubmed/25487218
http://dx.doi.org/10.1038/srep07390
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author Zhang, Yong-Liang
Wang, Huan
Jing, Li
Mu, Liang-Zhu
Fan, Heng
author_facet Zhang, Yong-Liang
Wang, Huan
Jing, Li
Mu, Liang-Zhu
Fan, Heng
author_sort Zhang, Yong-Liang
collection PubMed
description The linear function is possibly the simplest and the most used relation appearing in various areas of our world. A linear relation can be generally determined by the least square linear fitting (LSLF) method using several measured quantities depending on variables. This happens for such as detecting the gradient of a magnetic field. Here, we propose a quantum fitting scheme to estimate the magnetic field gradient with N-atom spins preparing in W state. Our scheme combines the quantum multi-parameter estimation and the least square linear fitting method to achieve the quantum Cramér-Rao bound (QCRB). We show that the estimated quantity achieves the Heisenberg-scaling accuracy. Our scheme of quantum metrology combined with data fitting provides a new method in fast high precision measurements.
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spelling pubmed-42602172014-12-15 Fitting magnetic field gradient with Heisenberg-scaling accuracy Zhang, Yong-Liang Wang, Huan Jing, Li Mu, Liang-Zhu Fan, Heng Sci Rep Article The linear function is possibly the simplest and the most used relation appearing in various areas of our world. A linear relation can be generally determined by the least square linear fitting (LSLF) method using several measured quantities depending on variables. This happens for such as detecting the gradient of a magnetic field. Here, we propose a quantum fitting scheme to estimate the magnetic field gradient with N-atom spins preparing in W state. Our scheme combines the quantum multi-parameter estimation and the least square linear fitting method to achieve the quantum Cramér-Rao bound (QCRB). We show that the estimated quantity achieves the Heisenberg-scaling accuracy. Our scheme of quantum metrology combined with data fitting provides a new method in fast high precision measurements. Nature Publishing Group 2014-12-09 /pmc/articles/PMC4260217/ /pubmed/25487218 http://dx.doi.org/10.1038/srep07390 Text en Copyright © 2014, Macmillan Publishers Limited. All rights reserved http://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder in order to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
spellingShingle Article
Zhang, Yong-Liang
Wang, Huan
Jing, Li
Mu, Liang-Zhu
Fan, Heng
Fitting magnetic field gradient with Heisenberg-scaling accuracy
title Fitting magnetic field gradient with Heisenberg-scaling accuracy
title_full Fitting magnetic field gradient with Heisenberg-scaling accuracy
title_fullStr Fitting magnetic field gradient with Heisenberg-scaling accuracy
title_full_unstemmed Fitting magnetic field gradient with Heisenberg-scaling accuracy
title_short Fitting magnetic field gradient with Heisenberg-scaling accuracy
title_sort fitting magnetic field gradient with heisenberg-scaling accuracy
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4260217/
https://www.ncbi.nlm.nih.gov/pubmed/25487218
http://dx.doi.org/10.1038/srep07390
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