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.
Instituto de Física La Plata
Materia
Física
Quantum-inspired algorithms
Multi-class classification
Pretty Good Measurement
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by/4.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/159869
Acceder
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oai_identifier_str oai:sedici.unlp.edu.ar:10915/159869
network_acronym_str SEDICI
repository_id_str 1329
network_name_str SEDICI (UNLP)
spelling Multi-class classification based on quantum state discriminationGiuntini, RobertoGranda Arango, Andrés CamiloFreytes, HectorHolik, Federico HernánSergioli, GiuseppeFísicaQuantum-inspired algorithmsMulti-class classificationPretty Good MeasurementWe 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.Instituto de Física La Plata2023info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArticulohttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttp://sedici.unlp.edu.ar/handle/10915/159869enginfo:eu-repo/semantics/altIdentifier/issn/0165-0114info:eu-repo/semantics/altIdentifier/doi/10.1016/j.fss.2023.03.012info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International (CC BY 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-10-22T17:22:43Zoai:sedici.unlp.edu.ar:10915/159869Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-10-22 17:22:44.194SEDICI (UNLP) - Universidad Nacional de La Platafalse
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
Física
Quantum-inspired algorithms
Multi-class classification
Pretty Good Measurement
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 Física
Quantum-inspired algorithms
Multi-class classification
Pretty Good Measurement
topic Física
Quantum-inspired algorithms
Multi-class classification
Pretty Good Measurement
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.
Instituto de Física La Plata
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
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Articulo
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://sedici.unlp.edu.ar/handle/10915/159869
url http://sedici.unlp.edu.ar/handle/10915/159869
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/issn/0165-0114
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.fss.2023.03.012
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by/4.0/
Creative Commons Attribution 4.0 International (CC BY 4.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by/4.0/
Creative Commons Attribution 4.0 International (CC BY 4.0)
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
instname:Universidad Nacional de La Plata
instacron:UNLP
reponame_str SEDICI (UNLP)
collection SEDICI (UNLP)
instname_str Universidad Nacional de La Plata
instacron_str UNLP
institution UNLP
repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
repository.mail.fl_str_mv alira@sedici.unlp.edu.ar
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score 12.982451

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