Quantum-inspired classifiers

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dc.contributor.authorLeporini, Roberto
dc.contributor.authorBertini, Cesarino
dc.contributor.editorKais, Sabre
dc.contributor.editorSkala, Václav
dc.date.accessioned2025-10-08T14:52:18Z
dc.date.available2025-10-08T14:52:18Z
dc.date.issued2025
dc.description.abstract-translatedQuantum-inspired machine learning is a new branch of machine learning based on the application of the mathematical formalism of quantum mechanics to devise novel algorithms for classical computers. We implement some quantum-inspired classification algorithms, based on quantum state discrimination, within a local approach in the feature space by taking into account elements close to the element to be classified. This local approach improves the accuracy in classification and motivates the integration with the classifiers. The quantum-inspired classifiers require the encoding of the feature vectors into density operators and methods for estimating the distinguishability of quantum states like the Helstrom state discrimination and the Pretty-Good measurement. We present a comparison of the performances of the local quantum-inspired classifiers against well-known classical algorithms in order to show that the local approach can be a valuable tool for increasing the performances of this kind of classifiers.en
dc.format6 s.cs
dc.identifier.doihttp://www.doi.org/10.24132/CSRN.2025-A57
dc.identifier.issn2464-4617
dc.identifier.issn2464-4625
dc.identifier.urihttp://hdl.handle.net/11025/62932
dc.language.isoenen
dc.publisherVaclav Skala - UNION Agencyen
dc.rights© Václav Skala - UNION Agencyen
dc.rights.accessopenAccessen
dc.subjectkvantově inspirované klasifikátorycs
dc.subjectstrojové učenícs
dc.subjectlokální přístupcs
dc.subject.translatedquantum-inspired classifiersen
dc.subject.translatedmachine learningen
dc.subject.translatedlocal approachen
dc.titleQuantum-inspired classifiersen
dc.typekonferenční příspěvekcs
dc.typeconferenceObjecten
dc.type.statusPeer revieweden
dc.type.versionpublishedVersionen
local.files.count2*
local.files.size2580781*
local.has.filesyes*

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QC–HORIZON 2025 Proceedings