A Quantum-inspired Version of the Classification Problem

Autores
Sergioli, Giuseppe; Bosyk, Gustavo Martin; Santucci, Enrica; Giuntini, Roberto
Año de publicación
2017
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
We address the problem of binary classification by using a quantum version of the Nearest Mean Classifier (NMC). Our proposal is indeed an advanced version of previous one (see Sergioli et al. 2017 that i) is able to be naturally generalized to arbitrary number of features and ii) exhibits better performances with respect to the classical NMC for several datasets. Further, we show that the quantum version of NMC is not invariant under rescaling. This allows us to introduce a free parameter, i.e. the rescaling factor, that could be useful to get a further improvement of the classification performance.
Fil: Sergioli, Giuseppe. Università degli studi di Cagliari; Italia
Fil: Bosyk, Gustavo Martin. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Física La Plata. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Física La Plata; Argentina. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Departamento de Física; Argentina
Fil: Santucci, Enrica. Università degli studi di Cagliari; Italia
Fil: Giuntini, Roberto. Università degli studi di Cagliari; Italia
Materia
Density Operators
Nearest Mean Classifier
Rescaling Invariance
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/50169
Acceder
id CONICETDig_002fb4f44724c51acbaad392e52baac0
oai_identifier_str oai:ri.conicet.gov.ar:11336/50169
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling A Quantum-inspired Version of the Classification ProblemSergioli, GiuseppeBosyk, Gustavo MartinSantucci, EnricaGiuntini, RobertoDensity OperatorsNearest Mean ClassifierRescaling Invariancehttps://purl.org/becyt/ford/1.3https://purl.org/becyt/ford/1We address the problem of binary classification by using a quantum version of the Nearest Mean Classifier (NMC). Our proposal is indeed an advanced version of previous one (see Sergioli et al. 2017 that i) is able to be naturally generalized to arbitrary number of features and ii) exhibits better performances with respect to the classical NMC for several datasets. Further, we show that the quantum version of NMC is not invariant under rescaling. This allows us to introduce a free parameter, i.e. the rescaling factor, that could be useful to get a further improvement of the classification performance.Fil: Sergioli, Giuseppe. Università degli studi di Cagliari; ItaliaFil: Bosyk, Gustavo Martin. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Física La Plata. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Física La Plata; Argentina. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Departamento de Física; ArgentinaFil: Santucci, Enrica. Università degli studi di Cagliari; ItaliaFil: Giuntini, Roberto. Università degli studi di Cagliari; ItaliaSpringer/Plenum Publishers2017-12info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/50169Sergioli, Giuseppe; Bosyk, Gustavo Martin; Santucci, Enrica; Giuntini, Roberto; A Quantum-inspired Version of the Classification Problem; Springer/Plenum Publishers; International Journal of Theoretical Physics; 56; 12; 12-2017; 3880-38880020-7748CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1007/s10773-017-3371-1info:eu-repo/semantics/altIdentifier/url/https://link.springer.com/article/10.1007%2Fs10773-017-3371-1info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:40:42Zoai:ri.conicet.gov.ar:11336/50169instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 09:40:42.949CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv A Quantum-inspired Version of the Classification Problem
title A Quantum-inspired Version of the Classification Problem
spellingShingle A Quantum-inspired Version of the Classification Problem
Sergioli, Giuseppe
Density Operators
Nearest Mean Classifier
Rescaling Invariance
title_short A Quantum-inspired Version of the Classification Problem
title_full A Quantum-inspired Version of the Classification Problem
title_fullStr A Quantum-inspired Version of the Classification Problem
title_full_unstemmed A Quantum-inspired Version of the Classification Problem
title_sort A Quantum-inspired Version of the Classification Problem
dc.creator.none.fl_str_mv Sergioli, Giuseppe
Bosyk, Gustavo Martin
Santucci, Enrica
Giuntini, Roberto
author Sergioli, Giuseppe
author_facet Sergioli, Giuseppe
Bosyk, Gustavo Martin
Santucci, Enrica
Giuntini, Roberto
author_role author
author2 Bosyk, Gustavo Martin
Santucci, Enrica
Giuntini, Roberto
author2_role author
author
author
dc.subject.none.fl_str_mv Density Operators
Nearest Mean Classifier
Rescaling Invariance
topic Density Operators
Nearest Mean Classifier
Rescaling Invariance
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.3
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv We address the problem of binary classification by using a quantum version of the Nearest Mean Classifier (NMC). Our proposal is indeed an advanced version of previous one (see Sergioli et al. 2017 that i) is able to be naturally generalized to arbitrary number of features and ii) exhibits better performances with respect to the classical NMC for several datasets. Further, we show that the quantum version of NMC is not invariant under rescaling. This allows us to introduce a free parameter, i.e. the rescaling factor, that could be useful to get a further improvement of the classification performance.
Fil: Sergioli, Giuseppe. Università degli studi di Cagliari; Italia
Fil: Bosyk, Gustavo Martin. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Física La Plata. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Física La Plata; Argentina. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Departamento de Física; Argentina
Fil: Santucci, Enrica. Università degli studi di Cagliari; Italia
Fil: Giuntini, Roberto. Università degli studi di Cagliari; Italia
description We address the problem of binary classification by using a quantum version of the Nearest Mean Classifier (NMC). Our proposal is indeed an advanced version of previous one (see Sergioli et al. 2017 that i) is able to be naturally generalized to arbitrary number of features and ii) exhibits better performances with respect to the classical NMC for several datasets. Further, we show that the quantum version of NMC is not invariant under rescaling. This allows us to introduce a free parameter, i.e. the rescaling factor, that could be useful to get a further improvement of the classification performance.
publishDate 2017
dc.date.none.fl_str_mv 2017-12
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/50169
Sergioli, Giuseppe; Bosyk, Gustavo Martin; Santucci, Enrica; Giuntini, Roberto; A Quantum-inspired Version of the Classification Problem; Springer/Plenum Publishers; International Journal of Theoretical Physics; 56; 12; 12-2017; 3880-3888
0020-7748
CONICET Digital
CONICET
url http://hdl.handle.net/11336/50169
identifier_str_mv Sergioli, Giuseppe; Bosyk, Gustavo Martin; Santucci, Enrica; Giuntini, Roberto; A Quantum-inspired Version of the Classification Problem; Springer/Plenum Publishers; International Journal of Theoretical Physics; 56; 12; 12-2017; 3880-3888
0020-7748
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1007/s10773-017-3371-1
info:eu-repo/semantics/altIdentifier/url/https://link.springer.com/article/10.1007%2Fs10773-017-3371-1
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Springer/Plenum Publishers
publisher.none.fl_str_mv Springer/Plenum Publishers
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
_version_ 1844613288596340736
score 13.070432

Publicaciones similares