Kernel-based quantum regressor models learning non-Markovianity

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidad Mayor-
Autor(es): dc.contributorVicerrectoría de Investigación-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorFlorida International University-
Autor(es): dc.contributorSantiago de Chile-
Autor(es): dc.creatorTancara, Diego-
Autor(es): dc.creatorDinani, Hossein T.-
Autor(es): dc.creatorNorambuena, Ariel-
Autor(es): dc.creatorFanchini, Felipe F.-
Autor(es): dc.creatorCoto, Raúl-
Data de aceite: dc.date.accessioned2025-08-21T15:41:14Z-
Data de disponibilização: dc.date.available2025-08-21T15:41:14Z-
Data de envio: dc.date.issued2023-07-29-
Data de envio: dc.date.issued2023-07-29-
Data de envio: dc.date.issued2023-01-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1103/PhysRevA.107.022402-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/246795-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/246795-
Descrição: dc.descriptionQuantum machine learning is a growing research field that aims to perform machine learning tasks assisted by a quantum computer. Kernel-based quantum machine learning models are paradigmatic examples where the kernel involves quantum states, and the Gram matrix is calculated from the overlap between these states. With the kernel at hand, a regular machine learning model is used for the learning process. In this paper we investigate the quantum support vector machine and quantum kernel ridge models to predict the degree of non-Markovianity of a quantum system. We perform digital quantum simulation of amplitude damping and phase damping channels to create our quantum data set. We elaborate on different kernel functions to map the data and kernel circuits to compute the overlap between quantum states. We show that our models deliver accurate predictions that are comparable with the fully classical models.-
Descrição: dc.descriptionCentro de Óptica e Información Cuántica Universidad Mayor, Vicerrectoría de Investigación-
Descrição: dc.descriptionEscuela Data Science Facultad de Ciencias Ingenería y Tecnología Universidad Mayor-
Descrição: dc.descriptionUniversidad Mayor Vicerrectoría de Investigación-
Descrição: dc.descriptionFaculdade de Ciências UNESP Universidade Estadual Paulista, SP-
Descrição: dc.descriptionDepartment of Physics Florida International University-
Descrição: dc.descriptionUniversidad Bernardo O Higgins Santiago de Chile-
Descrição: dc.descriptionFaculdade de Ciências UNESP Universidade Estadual Paulista, SP-
Idioma: dc.languageen-
Relação: dc.relationPhysical Review A-
???dc.source???: dc.sourceScopus-
Título: dc.titleKernel-based quantum regressor models learning non-Markovianity-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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