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
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/159869
Ver los metadatos del registro completo
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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:UNLP2025-09-29T11:41:51Zoai: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:13292025-09-29 11:41:51.482SEDICI (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 |
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SEDICI (UNLP) |
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SEDICI (UNLP) |
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Universidad Nacional de La Plata |
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SEDICI (UNLP) - Universidad Nacional de La Plata |
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