Multi-class classification based on quantum state discrimination

Autores
Giuntini, Roberto; Granda Arango, Andrés Camilo; Freytes, Hector; Holik, Federico Hernán; Sergioli, Giuseppe
Año de publicación
2023
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
We present a general framework for the problem of multi-class classification using classification functions that can be interpreted as fuzzy sets. We specialize these functions in the domain of Quantum-inspired classifiers, which are based on quantum state discrimination techniques. In particular, we use unsharp observables (Positive Operator-Valued Measures) that are determined by the training set of a given dataset to construct these classification functions. We show that such classifiers can be tested on near-term quantum computers once these classification functions are “distilled” (on a classical platform) from the quantum encoding of a training dataset. We compare these experimental results with their theoretical counterparts and we pose some questions for future research.
Fil: Giuntini, Roberto. Università Degli Studi Di Cagliari.; Italia
Fil: Granda Arango, Andrés Camilo. Università Degli Studi Di Cagliari.; Italia
Fil: Freytes, Hector. Università Degli Studi Di Cagliari.; Italia
Fil: Holik, Federico Hernán. 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
Fil: Sergioli, Giuseppe. Università Degli Studi Di Cagliari.; Italia
Materia
POVM
PRETTY GOOD MEASUREMENTS
MULTICLASS CLASSIFICATION
QUANTUM INSPIRED MACHINE LEARNING
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/233554
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Multi-class classification based on quantum state discriminationGiuntini, RobertoGranda Arango, Andrés CamiloFreytes, HectorHolik, Federico HernánSergioli, GiuseppePOVMPRETTY GOOD MEASUREMENTSMULTICLASS CLASSIFICATIONQUANTUM INSPIRED MACHINE LEARNINGhttps://purl.org/becyt/ford/1.3https://purl.org/becyt/ford/1We present a general framework for the problem of multi-class classification using classification functions that can be interpreted as fuzzy sets. We specialize these functions in the domain of Quantum-inspired classifiers, which are based on quantum state discrimination techniques. In particular, we use unsharp observables (Positive Operator-Valued Measures) that are determined by the training set of a given dataset to construct these classification functions. We show that such classifiers can be tested on near-term quantum computers once these classification functions are “distilled” (on a classical platform) from the quantum encoding of a training dataset. We compare these experimental results with their theoretical counterparts and we pose some questions for future research.Fil: Giuntini, Roberto. Università Degli Studi Di Cagliari.; ItaliaFil: Granda Arango, Andrés Camilo. Università Degli Studi Di Cagliari.; ItaliaFil: Freytes, Hector. Università Degli Studi Di Cagliari.; ItaliaFil: Holik, Federico Hernán. 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; ArgentinaFil: Sergioli, Giuseppe. Università Degli Studi Di Cagliari.; ItaliaElsevier2023-03info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/233554Giuntini, Roberto; Granda Arango, Andrés Camilo; Freytes, Hector; Holik, Federico Hernán; Sergioli, Giuseppe; Multi-class classification based on quantum state discrimination; Elsevier; International Journal On Fuzzy Sets And Systems; 467; 3-2023; 1-150165-0114CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://linkinghub.elsevier.com/retrieve/pii/S0165011423001343info:eu-repo/semantics/altIdentifier/doi/10.1016/j.fss.2023.03.012info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:51:07Zoai:ri.conicet.gov.ar:11336/233554instacron: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 09:51:07.446CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Multi-class classification based on quantum state discrimination
title Multi-class classification based on quantum state discrimination
spellingShingle Multi-class classification based on quantum state discrimination
Giuntini, Roberto
POVM
PRETTY GOOD MEASUREMENTS
MULTICLASS CLASSIFICATION
QUANTUM INSPIRED MACHINE LEARNING
title_short Multi-class classification based on quantum state discrimination
title_full Multi-class classification based on quantum state discrimination
title_fullStr Multi-class classification based on quantum state discrimination
title_full_unstemmed Multi-class classification based on quantum state discrimination
title_sort Multi-class classification based on quantum state discrimination
dc.creator.none.fl_str_mv Giuntini, Roberto
Granda Arango, Andrés Camilo
Freytes, Hector
Holik, Federico Hernán
Sergioli, Giuseppe
author Giuntini, Roberto
author_facet Giuntini, Roberto
Granda Arango, Andrés Camilo
Freytes, Hector
Holik, Federico Hernán
Sergioli, Giuseppe
author_role author
author2 Granda Arango, Andrés Camilo
Freytes, Hector
Holik, Federico Hernán
Sergioli, Giuseppe
author2_role author
author
author
author
dc.subject.none.fl_str_mv POVM
PRETTY GOOD MEASUREMENTS
MULTICLASS CLASSIFICATION
QUANTUM INSPIRED MACHINE LEARNING
topic POVM
PRETTY GOOD MEASUREMENTS
MULTICLASS CLASSIFICATION
QUANTUM INSPIRED MACHINE LEARNING
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 present a general framework for the problem of multi-class classification using classification functions that can be interpreted as fuzzy sets. We specialize these functions in the domain of Quantum-inspired classifiers, which are based on quantum state discrimination techniques. In particular, we use unsharp observables (Positive Operator-Valued Measures) that are determined by the training set of a given dataset to construct these classification functions. We show that such classifiers can be tested on near-term quantum computers once these classification functions are “distilled” (on a classical platform) from the quantum encoding of a training dataset. We compare these experimental results with their theoretical counterparts and we pose some questions for future research.
Fil: Giuntini, Roberto. Università Degli Studi Di Cagliari.; Italia
Fil: Granda Arango, Andrés Camilo. Università Degli Studi Di Cagliari.; Italia
Fil: Freytes, Hector. Università Degli Studi Di Cagliari.; Italia
Fil: Holik, Federico Hernán. 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
Fil: Sergioli, Giuseppe. Università Degli Studi Di Cagliari.; Italia
description We present a general framework for the problem of multi-class classification using classification functions that can be interpreted as fuzzy sets. We specialize these functions in the domain of Quantum-inspired classifiers, which are based on quantum state discrimination techniques. In particular, we use unsharp observables (Positive Operator-Valued Measures) that are determined by the training set of a given dataset to construct these classification functions. We show that such classifiers can be tested on near-term quantum computers once these classification functions are “distilled” (on a classical platform) from the quantum encoding of a training dataset. We compare these experimental results with their theoretical counterparts and we pose some questions for future research.
publishDate 2023
dc.date.none.fl_str_mv 2023-03
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/11336/233554
Giuntini, Roberto; Granda Arango, Andrés Camilo; Freytes, Hector; Holik, Federico Hernán; Sergioli, Giuseppe; Multi-class classification based on quantum state discrimination; Elsevier; International Journal On Fuzzy Sets And Systems; 467; 3-2023; 1-15
0165-0114
CONICET Digital
CONICET
url http://hdl.handle.net/11336/233554
identifier_str_mv Giuntini, Roberto; Granda Arango, Andrés Camilo; Freytes, Hector; Holik, Federico Hernán; Sergioli, Giuseppe; Multi-class classification based on quantum state discrimination; Elsevier; International Journal On Fuzzy Sets And Systems; 467; 3-2023; 1-15
0165-0114
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://linkinghub.elsevier.com/retrieve/pii/S0165011423001343
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.fss.2023.03.012
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
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
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score 13.070432

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