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Variational quantum metrology for multiparameter estimation under dephasing noise

We present a hybrid quantum-classical variational scheme to enhance precision in quantum metrology. In the scheme, both the initial state and the measurement basis in the quantum part are parameterized and optimized via the classical part. It enables the maximization of information gained about the...

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Autores principales: Le, Trung Kien, Nguyen, Hung Q., Ho, Le Bin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10584960/
https://www.ncbi.nlm.nih.gov/pubmed/37853037
http://dx.doi.org/10.1038/s41598-023-44786-0
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author Le, Trung Kien
Nguyen, Hung Q.
Ho, Le Bin
author_facet Le, Trung Kien
Nguyen, Hung Q.
Ho, Le Bin
author_sort Le, Trung Kien
collection PubMed
description We present a hybrid quantum-classical variational scheme to enhance precision in quantum metrology. In the scheme, both the initial state and the measurement basis in the quantum part are parameterized and optimized via the classical part. It enables the maximization of information gained about the measured quantity. We discuss specific applications to 3D magnetic field sensing under several dephasing noise models. Indeed, we demonstrate its ability to simultaneously estimate all parameters and surpass the standard quantum limit, making it a powerful tool for metrological applications.
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spelling pubmed-105849602023-10-20 Variational quantum metrology for multiparameter estimation under dephasing noise Le, Trung Kien Nguyen, Hung Q. Ho, Le Bin Sci Rep Article We present a hybrid quantum-classical variational scheme to enhance precision in quantum metrology. In the scheme, both the initial state and the measurement basis in the quantum part are parameterized and optimized via the classical part. It enables the maximization of information gained about the measured quantity. We discuss specific applications to 3D magnetic field sensing under several dephasing noise models. Indeed, we demonstrate its ability to simultaneously estimate all parameters and surpass the standard quantum limit, making it a powerful tool for metrological applications. Nature Publishing Group UK 2023-10-18 /pmc/articles/PMC10584960/ /pubmed/37853037 http://dx.doi.org/10.1038/s41598-023-44786-0 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Le, Trung Kien
Nguyen, Hung Q.
Ho, Le Bin
Variational quantum metrology for multiparameter estimation under dephasing noise
title Variational quantum metrology for multiparameter estimation under dephasing noise
title_full Variational quantum metrology for multiparameter estimation under dephasing noise
title_fullStr Variational quantum metrology for multiparameter estimation under dephasing noise
title_full_unstemmed Variational quantum metrology for multiparameter estimation under dephasing noise
title_short Variational quantum metrology for multiparameter estimation under dephasing noise
title_sort variational quantum metrology for multiparameter estimation under dephasing noise
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10584960/
https://www.ncbi.nlm.nih.gov/pubmed/37853037
http://dx.doi.org/10.1038/s41598-023-44786-0
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