Machine classification for probe-based quantum thermometry
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1103/PhysRevA.105.022413 http://hdl.handle.net/11449/223518 |
Resumo: | We consider probe-based quantum thermometry and show that machine classification can provide model-independent estimation with quantifiable error assessment. Our approach is based on the k-nearest-neighbor algorithm. The machine is trained using data from either computer simulations or a calibration experiment. This yields a predictor which can be used to estimate the temperature from new observations. The algorithm is highly flexible and works with any kind of probe observable. It also allows one to incorporate experimental errors, as well as uncertainties about experimental parameters. We illustrate our method with an impurity thermometer in a Bose gas, as well as in the estimation of the thermal phonon number in the Rabi model. |
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Machine classification for probe-based quantum thermometryWe consider probe-based quantum thermometry and show that machine classification can provide model-independent estimation with quantifiable error assessment. Our approach is based on the k-nearest-neighbor algorithm. The machine is trained using data from either computer simulations or a calibration experiment. This yields a predictor which can be used to estimate the temperature from new observations. The algorithm is highly flexible and works with any kind of probe observable. It also allows one to incorporate experimental errors, as well as uncertainties about experimental parameters. We illustrate our method with an impurity thermometer in a Bose gas, as well as in the estimation of the thermal phonon number in the Rabi model.Faculdade de Cilncias Unesp - Universidade Estadual Paulista BauruFaculty of Physics Astronomy and Applied Computer Science Jagiellonian UniversityInstituto de Física da Universidade de São PauloFaculdade de Cilncias Unesp - Universidade Estadual Paulista BauruUniversidade Estadual Paulista (UNESP)Jagiellonian UniversityUniversidade de São Paulo (USP)Luiz, Fabrício S. [UNESP]Junior, A. De OliveiraFanchini, Felipe F. [UNESP]Landi, Gabriel T.2022-04-28T19:51:14Z2022-04-28T19:51:14Z2022-02-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://dx.doi.org/10.1103/PhysRevA.105.022413Physical Review A, v. 105, n. 2, 2022.2469-99342469-9926http://hdl.handle.net/11449/22351810.1103/PhysRevA.105.0224132-s2.0-85125256184Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengPhysical Review Ainfo:eu-repo/semantics/openAccess2022-04-28T19:51:14Zoai:repositorio.unesp.br:11449/223518Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T21:50:11.780300Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Machine classification for probe-based quantum thermometry |
title |
Machine classification for probe-based quantum thermometry |
spellingShingle |
Machine classification for probe-based quantum thermometry Luiz, Fabrício S. [UNESP] |
title_short |
Machine classification for probe-based quantum thermometry |
title_full |
Machine classification for probe-based quantum thermometry |
title_fullStr |
Machine classification for probe-based quantum thermometry |
title_full_unstemmed |
Machine classification for probe-based quantum thermometry |
title_sort |
Machine classification for probe-based quantum thermometry |
author |
Luiz, Fabrício S. [UNESP] |
author_facet |
Luiz, Fabrício S. [UNESP] Junior, A. De Oliveira Fanchini, Felipe F. [UNESP] Landi, Gabriel T. |
author_role |
author |
author2 |
Junior, A. De Oliveira Fanchini, Felipe F. [UNESP] Landi, Gabriel T. |
author2_role |
author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (UNESP) Jagiellonian University Universidade de São Paulo (USP) |
dc.contributor.author.fl_str_mv |
Luiz, Fabrício S. [UNESP] Junior, A. De Oliveira Fanchini, Felipe F. [UNESP] Landi, Gabriel T. |
description |
We consider probe-based quantum thermometry and show that machine classification can provide model-independent estimation with quantifiable error assessment. Our approach is based on the k-nearest-neighbor algorithm. The machine is trained using data from either computer simulations or a calibration experiment. This yields a predictor which can be used to estimate the temperature from new observations. The algorithm is highly flexible and works with any kind of probe observable. It also allows one to incorporate experimental errors, as well as uncertainties about experimental parameters. We illustrate our method with an impurity thermometer in a Bose gas, as well as in the estimation of the thermal phonon number in the Rabi model. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-04-28T19:51:14Z 2022-04-28T19:51:14Z 2022-02-01 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1103/PhysRevA.105.022413 Physical Review A, v. 105, n. 2, 2022. 2469-9934 2469-9926 http://hdl.handle.net/11449/223518 10.1103/PhysRevA.105.022413 2-s2.0-85125256184 |
url |
http://dx.doi.org/10.1103/PhysRevA.105.022413 http://hdl.handle.net/11449/223518 |
identifier_str_mv |
Physical Review A, v. 105, n. 2, 2022. 2469-9934 2469-9926 10.1103/PhysRevA.105.022413 2-s2.0-85125256184 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Physical Review A |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1808129364543406080 |