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Quantum measurement optimization by decomposition of measurements into extremals

Using the convex structure of positive operator value measurements and several quantities used in quantum metrology, such as quantum Fisher information or the quantum Van Trees information, we present an efficient numerical method to find the best strategy allowed by quantum mechanics to estimate a...

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Autores principales: Martínez-Vargas, Esteban, Pineda, Carlos, Barberis-Blostein, Pablo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7286896/
https://www.ncbi.nlm.nih.gov/pubmed/32523002
http://dx.doi.org/10.1038/s41598-020-65934-w
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author Martínez-Vargas, Esteban
Pineda, Carlos
Barberis-Blostein, Pablo
author_facet Martínez-Vargas, Esteban
Pineda, Carlos
Barberis-Blostein, Pablo
author_sort Martínez-Vargas, Esteban
collection PubMed
description Using the convex structure of positive operator value measurements and several quantities used in quantum metrology, such as quantum Fisher information or the quantum Van Trees information, we present an efficient numerical method to find the best strategy allowed by quantum mechanics to estimate a parameter. This method explores extremal measurements thus providing a significant advantage over previously used methods. We exemplify the method for different cost functions in a qubit and in a harmonic oscillator and find a strong numerical advantage when the desired target error is sufficiently small.
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spelling pubmed-72868962020-06-15 Quantum measurement optimization by decomposition of measurements into extremals Martínez-Vargas, Esteban Pineda, Carlos Barberis-Blostein, Pablo Sci Rep Article Using the convex structure of positive operator value measurements and several quantities used in quantum metrology, such as quantum Fisher information or the quantum Van Trees information, we present an efficient numerical method to find the best strategy allowed by quantum mechanics to estimate a parameter. This method explores extremal measurements thus providing a significant advantage over previously used methods. We exemplify the method for different cost functions in a qubit and in a harmonic oscillator and find a strong numerical advantage when the desired target error is sufficiently small. Nature Publishing Group UK 2020-06-10 /pmc/articles/PMC7286896/ /pubmed/32523002 http://dx.doi.org/10.1038/s41598-020-65934-w Text en © The Author(s) 2020 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 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, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Martínez-Vargas, Esteban
Pineda, Carlos
Barberis-Blostein, Pablo
Quantum measurement optimization by decomposition of measurements into extremals
title Quantum measurement optimization by decomposition of measurements into extremals
title_full Quantum measurement optimization by decomposition of measurements into extremals
title_fullStr Quantum measurement optimization by decomposition of measurements into extremals
title_full_unstemmed Quantum measurement optimization by decomposition of measurements into extremals
title_short Quantum measurement optimization by decomposition of measurements into extremals
title_sort quantum measurement optimization by decomposition of measurements into extremals
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7286896/
https://www.ncbi.nlm.nih.gov/pubmed/32523002
http://dx.doi.org/10.1038/s41598-020-65934-w
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