The Quantum Mechanics Propagator as the Machine Learning Performance in Space-Time Displacements

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The role of evolution operator is to provide the time displacement of wave function through the Hamiltonian of the system. The usage of coordinates representation gives the well-known propagator that is the Green’s function. In this paper it is emphasized that once the propagator is projected onto a...

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Detalles Bibliográficos
Autor: Nieto-Chaupis, Huber
Formato: artículo
Fecha de Publicación:2022
Institución:Universidad Autónoma del Perú
Repositorio:AUTONOMA-Institucional
Lenguaje:inglés
OAI Identifier:oai:repositorio.autonoma.edu.pe:20.500.13067/1807
Enlace del recurso:https://hdl.handle.net/20.500.13067/1807
https://doi.org/10.1109/AIKE52691.2021.00029
Nivel de acceso:acceso restringido
Materia:Knowledge engineering
Conferences
Quantum mechanics
Morphology
Machine learning
Wave functions
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spelling Nieto-Chaupis, Huber2022-04-29T14:38:01Z2022-04-29T14:38:01Z2022-03-03Nieto-Chaupis, H. (2021). The Quantum Mechanics Propagator as the Machine Learning Performance in Space-Time Displacements. In 2021 IEEE Fourth International Conference on Artificial Intelligence and Knowledge Engineering (AIKE) (pp. 135-136). IEEE.978-1-6654-3736-3https://hdl.handle.net/20.500.13067/18072021 IEEE Fourth International Conference on Artificial Intelligence and Knowledge Engineering (AIKE)https://doi.org/10.1109/AIKE52691.2021.00029The role of evolution operator is to provide the time displacement of wave function through the Hamiltonian of the system. The usage of coordinates representation gives the well-known propagator that is the Green’s function. In this paper it is emphasized that once the propagator is projected onto a scenario of machine learning it would acquire the role of performance in according to the criteria of Tom Mitchell. In this manner from the resulting wave function the probability is simulated presenting noteworthy morphologies in the which the system displays high values of probability for the measurement of distances.application/pdfengInstitute of Electrical and Electronics EngineersPEinfo:eu-repo/semantics/restrictedAccesshttps://creativecommons.org/licenses/by-nc-nd/4.0/AUTONOMA135136reponame:AUTONOMA-Institucionalinstname:Universidad Autónoma del Perúinstacron:AUTONOMAKnowledge engineeringConferencesQuantum mechanicsMorphologyMachine learningWave functionshttps://purl.org/pe-repo/ocde/ford#2.02.04The Quantum Mechanics Propagator as the Machine Learning Performance in Space-Time Displacementsinfo:eu-repo/semantics/articlehttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85127625788&doi=10.1109%2fAIKE52691.2021.00029&partnerID=40&md5TEXTThe Quantum Mechanics Propagator as the Machine Learning Performance in Space-Time Displacements.pdf.txtThe Quantum Mechanics Propagator as the Machine Learning Performance in Space-Time Displacements.pdf.txtExtracted texttext/plain602http://repositorio.autonoma.edu.pe/bitstream/20.500.13067/1807/4/The%20Quantum%20Mechanics%20Propagator%20as%20the%20Machine%20Learning%20Performance%20in%20Space-Time%20Displacements.pdf.txte3dd34123e4359fbaec0b7f2eb988493MD54THUMBNAILThe Quantum Mechanics Propagator as the Machine Learning Performance in Space-Time Displacements.pdf.jpgThe Quantum Mechanics Propagator as the Machine Learning Performance in Space-Time Displacements.pdf.jpgGenerated Thumbnailimage/jpeg5869http://repositorio.autonoma.edu.pe/bitstream/20.500.13067/1807/5/The%20Quantum%20Mechanics%20Propagator%20as%20the%20Machine%20Learning%20Performance%20in%20Space-Time%20Displacements.pdf.jpgd9832e5b03223bd7b9ea188040a0c57cMD55LICENSElicense.txtlicense.txttext/plain; charset=utf-885http://repositorio.autonoma.edu.pe/bitstream/20.500.13067/1807/2/license.txt9243398ff393db1861c890baeaeee5f9MD52ORIGINALThe Quantum Mechanics Propagator as the Machine Learning Performance in Space-Time Displacements.pdfThe Quantum Mechanics Propagator as the Machine Learning Performance in Space-Time Displacements.pdfVer fuenteapplication/pdf98746http://repositorio.autonoma.edu.pe/bitstream/20.500.13067/1807/3/The%20Quantum%20Mechanics%20Propagator%20as%20the%20Machine%20Learning%20Performance%20in%20Space-Time%20Displacements.pdf679c30a4c34ef5a33fd679aea9b799cbMD5320.500.13067/1807oai:repositorio.autonoma.edu.pe:20.500.13067/18072022-04-30 03:00:21.033Repositorio de la Universidad Autonoma del Perúrepositorio@autonoma.pe
dc.title.es_PE.fl_str_mv The Quantum Mechanics Propagator as the Machine Learning Performance in Space-Time Displacements
title The Quantum Mechanics Propagator as the Machine Learning Performance in Space-Time Displacements
spellingShingle The Quantum Mechanics Propagator as the Machine Learning Performance in Space-Time Displacements
Nieto-Chaupis, Huber
Knowledge engineering
Conferences
Quantum mechanics
Morphology
Machine learning
Wave functions
https://purl.org/pe-repo/ocde/ford#2.02.04
title_short The Quantum Mechanics Propagator as the Machine Learning Performance in Space-Time Displacements
title_full The Quantum Mechanics Propagator as the Machine Learning Performance in Space-Time Displacements
title_fullStr The Quantum Mechanics Propagator as the Machine Learning Performance in Space-Time Displacements
title_full_unstemmed The Quantum Mechanics Propagator as the Machine Learning Performance in Space-Time Displacements
title_sort The Quantum Mechanics Propagator as the Machine Learning Performance in Space-Time Displacements
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 Knowledge engineering
Conferences
Quantum mechanics
Morphology
Machine learning
Wave functions
topic Knowledge engineering
Conferences
Quantum mechanics
Morphology
Machine learning
Wave functions
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 The role of evolution operator is to provide the time displacement of wave function through the Hamiltonian of the system. The usage of coordinates representation gives the well-known propagator that is the Green’s function. In this paper it is emphasized that once the propagator is projected onto a scenario of machine learning it would acquire the role of performance in according to the criteria of Tom Mitchell. In this manner from the resulting wave function the probability is simulated presenting noteworthy morphologies in the which the system displays high values of probability for the measurement of distances.
publishDate 2022
dc.date.accessioned.none.fl_str_mv 2022-04-29T14:38:01Z
dc.date.available.none.fl_str_mv 2022-04-29T14:38:01Z
dc.date.issued.fl_str_mv 2022-03-03
dc.type.es_PE.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.citation.es_PE.fl_str_mv Nieto-Chaupis, H. (2021). The Quantum Mechanics Propagator as the Machine Learning Performance in Space-Time Displacements. In 2021 IEEE Fourth International Conference on Artificial Intelligence and Knowledge Engineering (AIKE) (pp. 135-136). IEEE.
dc.identifier.isbn.none.fl_str_mv 978-1-6654-3736-3
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/20.500.13067/1807
dc.identifier.journal.es_PE.fl_str_mv 2021 IEEE Fourth International Conference on Artificial Intelligence and Knowledge Engineering (AIKE)
dc.identifier.doi.none.fl_str_mv https://doi.org/10.1109/AIKE52691.2021.00029
identifier_str_mv Nieto-Chaupis, H. (2021). The Quantum Mechanics Propagator as the Machine Learning Performance in Space-Time Displacements. In 2021 IEEE Fourth International Conference on Artificial Intelligence and Knowledge Engineering (AIKE) (pp. 135-136). IEEE.
978-1-6654-3736-3
2021 IEEE Fourth International Conference on Artificial Intelligence and Knowledge Engineering (AIKE)
url https://hdl.handle.net/20.500.13067/1807
https://doi.org/10.1109/AIKE52691.2021.00029
dc.language.iso.es_PE.fl_str_mv eng
language eng
dc.relation.url.es_PE.fl_str_mv https://www.scopus.com/inward/record.uri?eid=2-s2.0-85127625788&doi=10.1109%2fAIKE52691.2021.00029&partnerID=40&md5
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rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.format.es_PE.fl_str_mv application/pdf
dc.publisher.es_PE.fl_str_mv Institute of Electrical and Electronics Engineers
dc.publisher.country.es_PE.fl_str_mv PE
dc.source.es_PE.fl_str_mv AUTONOMA
dc.source.none.fl_str_mv reponame:AUTONOMA-Institucional
instname:Universidad Autónoma del Perú
instacron:AUTONOMA
instname_str Universidad Autónoma del Perú
instacron_str AUTONOMA
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The Quantum Mechanics Propagator as the Machine Learning Performance in Space-Time Displacements
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