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dc.date.accessioned 2023-11-07T15:08:10Z
dc.date.available 2023-11-07T15:08:10Z
dc.date.issued 2023
dc.identifier.uri http://sedici.unlp.edu.ar/handle/10915/159869
dc.description.abstract 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. en
dc.language en es
dc.subject Quantum-inspired algorithms es
dc.subject Multi-class classification es
dc.subject Pretty Good Measurement es
dc.title Multi-class classification based on quantum state discrimination en
dc.type Articulo es
sedici.identifier.other https://doi.org/10.1016/j.fss.2023.03.012 es
sedici.identifier.issn 0165-0114 es
sedici.creator.person Giuntini, Roberto es
sedici.creator.person Granda Arango, Andrés Camilo es
sedici.creator.person Freytes, Hector es
sedici.creator.person Holik, Federico Hernán es
sedici.creator.person Sergioli, Giuseppe es
sedici.subject.materias Física es
sedici.description.fulltext true es
mods.originInfo.place Instituto de Física La Plata es
sedici.subtype Articulo es
sedici.rights.license Creative Commons Attribution 4.0 International (CC BY 4.0)
sedici.rights.uri http://creativecommons.org/licenses/by/4.0/
sedici.description.peerReview peer-review es
sedici.relation.journalTitle Fuzzy Setsand Systems es
sedici.relation.journalVolumeAndIssue vol. 467 es


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Creative Commons Attribution 4.0 International (CC BY 4.0) Except where otherwise noted, this item's license is described as Creative Commons Attribution 4.0 International (CC BY 4.0)