Theory of machine learning based on nonrelativistic quantum mechanics

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El documento a texto completo no se encuentra disponible en el Repositorio de la Universidad Autónoma del Perú debido a las restricciones de la casa editorial donde se encuentra publicada.
Detalles Bibliográficos
Autor: Nieto-Chaupis, Huber
Formato: artículo
Fecha de Publicación:2021
Institución:Universidad Autónoma del Perú
Repositorio:AUTONOMA-Institucional
Lenguaje:español
OAI Identifier:oai:repositorio.autonoma.edu.pe:20.500.13067/1586
Enlace del recurso:https://hdl.handle.net/20.500.13067/1586
https://doi.org/10.1142/S0219749921410045
Nivel de acceso:acceso restringido
Materia:Dirac brackets
Machine learning
Quantum mechanics
Tom Mitchell
https://purl.org/pe-repo/ocde/ford#2.02.04
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spelling Nieto-Chaupis, Huber2022-01-25T21:35:16Z2022-01-25T21:35:16Z20212197499https://hdl.handle.net/20.500.13067/1586International Journal of Quantum Informationhttps://doi.org/10.1142/S0219749921410045El documento a texto completo no se encuentra disponible en el Repositorio de la Universidad Autónoma del Perú debido a las restricciones de la casa editorial donde se encuentra publicada.The goal of this paper is the presentation of the elementary procedures that normally are done in nonrelativistic Quantum Mechanics in terms of the principles of Machine Learning. In essence, this paper discusses Mitchell's criteria, whose block fundamental dictates that the universal evolution of any system is composed by three fundamental steps: (i) Task, (ii) Performance and (iii) Experience. In this paper, the quantum mechanics formalism reflected on the usage of evolution operator and Green's function are assumed to be part of mechanisms that are inherently engaged to the Machine Learning philosophy. The action for measuring observables through experiments and the intrinsic apparition of statistical or systematic errors are discussed in terms of "quantum learning". © 2021 World Scientific Publishing Company.application/pdfspaWorld ScientificPEinfo:eu-repo/semantics/restrictedAccesshttps://creativecommons.org/licenses/by-nc-nd/4.0/AUTONOMA194reponame:AUTONOMA-Institucionalinstname:Universidad Autónoma del Perúinstacron:AUTONOMADirac bracketsMachine learningQuantum mechanicsTom Mitchellhttps://purl.org/pe-repo/ocde/ford#2.02.04Theory of machine learning based on nonrelativistic quantum mechanicsinfo:eu-repo/semantics/articlehttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85108575681&doi=10.1142%2fS0219749921410045&partnerID=40&md5=ad8550ea02b8341f3f1f8d8f23089616ORIGINALTheory of machine learning based on nonrelativistic quantum mechanics.pdfTheory of machine learning based on nonrelativistic quantum mechanics.pdfVer fuenteapplication/pdf98978http://repositorio.autonoma.edu.pe/bitstream/20.500.13067/1586/3/Theory%20of%20machine%20learning%20based%20on%20nonrelativistic%20quantum%20mechanics.pdfb2b873580978a22effde201a76608c33MD53TEXTTheory of machine learning based on nonrelativistic quantum mechanics.pdf.txtTheory of machine learning based on nonrelativistic quantum mechanics.pdf.txtExtracted texttext/plain486http://repositorio.autonoma.edu.pe/bitstream/20.500.13067/1586/4/Theory%20of%20machine%20learning%20based%20on%20nonrelativistic%20quantum%20mechanics.pdf.txt9a51e13a30604054f255016445c86728MD54THUMBNAILTheory of machine learning based on nonrelativistic quantum mechanics.pdf.jpgTheory of machine learning based on nonrelativistic quantum mechanics.pdf.jpgGenerated Thumbnailimage/jpeg5557http://repositorio.autonoma.edu.pe/bitstream/20.500.13067/1586/5/Theory%20of%20machine%20learning%20based%20on%20nonrelativistic%20quantum%20mechanics.pdf.jpge184c19a95be1fdd7bf15a83cdc02f75MD55LICENSElicense.txtlicense.txttext/plain; charset=utf-885http://repositorio.autonoma.edu.pe/bitstream/20.500.13067/1586/2/license.txt9243398ff393db1861c890baeaeee5f9MD5220.500.13067/1586oai:repositorio.autonoma.edu.pe:20.500.13067/15862022-01-26 03:00:24.805Repositorio de la Universidad Autonoma del Perúrepositorio@autonoma.pe
dc.title.es_PE.fl_str_mv Theory of machine learning based on nonrelativistic quantum mechanics
title Theory of machine learning based on nonrelativistic quantum mechanics
spellingShingle Theory of machine learning based on nonrelativistic quantum mechanics
Nieto-Chaupis, Huber
Dirac brackets
Machine learning
Quantum mechanics
Tom Mitchell
https://purl.org/pe-repo/ocde/ford#2.02.04
title_short Theory of machine learning based on nonrelativistic quantum mechanics
title_full Theory of machine learning based on nonrelativistic quantum mechanics
title_fullStr Theory of machine learning based on nonrelativistic quantum mechanics
title_full_unstemmed Theory of machine learning based on nonrelativistic quantum mechanics
title_sort Theory of machine learning based on nonrelativistic quantum mechanics
author Nieto-Chaupis, Huber
author_facet Nieto-Chaupis, Huber
author_role author
dc.contributor.author.fl_str_mv Nieto-Chaupis, Huber
dc.subject.es_PE.fl_str_mv Dirac brackets
Machine learning
Quantum mechanics
Tom Mitchell
topic Dirac brackets
Machine learning
Quantum mechanics
Tom Mitchell
https://purl.org/pe-repo/ocde/ford#2.02.04
dc.subject.ocde.es_PE.fl_str_mv https://purl.org/pe-repo/ocde/ford#2.02.04
description El documento a texto completo no se encuentra disponible en el Repositorio de la Universidad Autónoma del Perú debido a las restricciones de la casa editorial donde se encuentra publicada.
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dc.date.accessioned.none.fl_str_mv 2022-01-25T21:35:16Z
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dc.identifier.journal.es_PE.fl_str_mv International Journal of Quantum Information
dc.identifier.doi.none.fl_str_mv https://doi.org/10.1142/S0219749921410045
identifier_str_mv 2197499
International Journal of Quantum Information
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dc.publisher.es_PE.fl_str_mv World Scientific
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Theory of machine learning based on nonrelativistic quantum mechanics
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