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From Rényi Entropy Power to Information Scan of Quantum States

In this paper, we generalize the notion of Shannon’s entropy power to the Rényi-entropy setting. With this, we propose generalizations of the de Bruijn identity, isoperimetric inequality, or Stam inequality. This framework not only allows for finding new estimation inequalities, but it also provides...

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Detalles Bibliográficos
Autores principales: Jizba, Petr, Dunningham, Jacob, Prokš, Martin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8001603/
https://www.ncbi.nlm.nih.gov/pubmed/33809011
http://dx.doi.org/10.3390/e23030334
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author Jizba, Petr
Dunningham, Jacob
Prokš, Martin
author_facet Jizba, Petr
Dunningham, Jacob
Prokš, Martin
author_sort Jizba, Petr
collection PubMed
description In this paper, we generalize the notion of Shannon’s entropy power to the Rényi-entropy setting. With this, we propose generalizations of the de Bruijn identity, isoperimetric inequality, or Stam inequality. This framework not only allows for finding new estimation inequalities, but it also provides a convenient technical framework for the derivation of a one-parameter family of Rényi-entropy-power-based quantum-mechanical uncertainty relations. To illustrate the usefulness of the Rényi entropy power obtained, we show how the information probability distribution associated with a quantum state can be reconstructed in a process that is akin to quantum-state tomography. We illustrate the inner workings of this with the so-called “cat states”, which are of fundamental interest and practical use in schemes such as quantum metrology. Salient issues, including the extension of the notion of entropy power to Tsallis entropy and ensuing implications in estimation theory, are also briefly discussed.
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spelling pubmed-80016032021-03-28 From Rényi Entropy Power to Information Scan of Quantum States Jizba, Petr Dunningham, Jacob Prokš, Martin Entropy (Basel) Article In this paper, we generalize the notion of Shannon’s entropy power to the Rényi-entropy setting. With this, we propose generalizations of the de Bruijn identity, isoperimetric inequality, or Stam inequality. This framework not only allows for finding new estimation inequalities, but it also provides a convenient technical framework for the derivation of a one-parameter family of Rényi-entropy-power-based quantum-mechanical uncertainty relations. To illustrate the usefulness of the Rényi entropy power obtained, we show how the information probability distribution associated with a quantum state can be reconstructed in a process that is akin to quantum-state tomography. We illustrate the inner workings of this with the so-called “cat states”, which are of fundamental interest and practical use in schemes such as quantum metrology. Salient issues, including the extension of the notion of entropy power to Tsallis entropy and ensuing implications in estimation theory, are also briefly discussed. MDPI 2021-03-12 /pmc/articles/PMC8001603/ /pubmed/33809011 http://dx.doi.org/10.3390/e23030334 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ).
spellingShingle Article
Jizba, Petr
Dunningham, Jacob
Prokš, Martin
From Rényi Entropy Power to Information Scan of Quantum States
title From Rényi Entropy Power to Information Scan of Quantum States
title_full From Rényi Entropy Power to Information Scan of Quantum States
title_fullStr From Rényi Entropy Power to Information Scan of Quantum States
title_full_unstemmed From Rényi Entropy Power to Information Scan of Quantum States
title_short From Rényi Entropy Power to Information Scan of Quantum States
title_sort from rényi entropy power to information scan of quantum states
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8001603/
https://www.ncbi.nlm.nih.gov/pubmed/33809011
http://dx.doi.org/10.3390/e23030334
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