A Quantum-inspired Approach to Estimate Optimum-Path Forest Prototypes based on the Traveling Salesman Problem

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Autor(es): dc.contributorUniversidade Estadual de Campinas (UNICAMP)-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorCzestochowa University of Technology-
Autor(es): dc.creatorMiranda, Maria Angélica Krüger-
Autor(es): dc.creatorFanchini, Felipe Fernandes-
Autor(es): dc.creatorPassos, Leandro Aparecido-
Autor(es): dc.creatorRodrigues, Douglas-
Autor(es): dc.creatorCosta, Kelton Augusto Pontara da-
Autor(es): dc.creatorSherer, Rafał-
Autor(es): dc.creatorPapa, João Paulo-
Data de aceite: dc.date.accessioned2025-08-21T20:09:52Z-
Data de disponibilização: dc.date.available2025-08-21T20:09:52Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2024-12-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1007/978-3-031-78183-4_6-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/307732-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/307732-
Descrição: dc.descriptionQuantum mechanics emerge as a promise for the future of computing, broadening the horizons for solutions concerning complex tasks, e.g., NP-hard problems. Alongside quantum computing, machine learning has become indispensable. This paper explores the potential integration of quantum computing principles into the Optimum-Path Forest (OPF), a graph-based framework comprised of solutions for machine learning, optimization, and image processing. We are particularly interested in the supervised OPF approach, which elects the most representative samples for each class, aka prototypes, as the connected samples from different classes in a minimum spanning tree (MST) computed over the training set. By harnessing quantum parallelism and superposition, this paper introduces a new approach to identifying prototypes employing a quantum-based Traveler Salesman Problem (TSP) algorithm, which provides an alternative to computing MSTs and yields a hybrid version of the OPF classifier. The experiments on established datasets demonstrated the promising potential of this approach while also underscoring the necessity for further research in this field.-
Descrição: dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)-
Descrição: dc.descriptionInstitute of Computing Campinas State University - UNICAMP-
Descrição: dc.descriptionSão Paulo State University (UNESP) School of Sciences-
Descrição: dc.descriptionInstitute of Computational Intelligence Czestochowa University of Technology-
Descrição: dc.descriptionSão Paulo State University (UNESP) School of Sciences-
Descrição: dc.descriptionFAPESP: 2013/07375-0-
Descrição: dc.descriptionFAPESP: 2019/07665-4-
Descrição: dc.descriptionFAPESP: 2021/04655-8-
Descrição: dc.descriptionFAPESP: 2023/03726-4-
Descrição: dc.descriptionFAPESP: 2023/10823-6-
Descrição: dc.descriptionFAPESP: 2023/12830-0-
Descrição: dc.descriptionFAPESP: 2023/14427-8-
Formato: dc.format85-98-
Idioma: dc.languageen-
Relação: dc.relationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectMachine Learning-
Palavras-chave: dc.subjectOptimum-Path Forest.-
Palavras-chave: dc.subjectQuantum Computing-
Palavras-chave: dc.subjectQuantum Optimization-
Título: dc.titleA Quantum-inspired Approach to Estimate Optimum-Path Forest Prototypes based on the Traveling Salesman Problem-
Tipo de arquivo: dc.typeaula digital-
Aparece nas coleções:Repositório Institucional - Unesp

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