Quantum enhancements for machine learning based on a probabilistic quantum memory

Detalhes bibliográficos
Autor(a) principal: SANTOS, Priscila Gabriele Marques dos
Data de Publicação: 2019
Tipo de documento: Dissertação
Idioma: por
Título da fonte: Biblioteca Digital de Teses e Dissertações da UFRPE
Texto Completo: http://www.tede2.ufrpe.br:8080/tede2/handle/tede2/8538
Resumo: Quantum machine learning arises from the interaction of fields of machine learning and quantum computing. Machine learning is a branch of artificial intelligence relevant in many areas. It provides computers the ability to learn autonomously from experience. Quantum computing, on the other hand, is a different computational paradigm. The processing of information and communication in a quantum computer makes use of the principles and properties of quantum mechanics. With this, it is possible to achieve computational effects that cannot be efficiently reached classically. Quantum computing raises new possibilities through promising approaches that make use of these effects. In fact, proposed quantum algorithms demonstrate their potential in outperforming classical algorithms in some tasks. The present work aims to contribute with the field of quantum machine learning. In order to do so, the use and applications of a quantum probabilistic memory as a tool to propose improved machine learning algorithms is investigated. Here, the quantum memory is used to develop improved procedures for tasks such as cross-validation, and the selection and evaluation of artificial neural network architectures. In addition, a weightless neural network model using the probabilistic quantum memory was evaluated and improved.
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spelling SILVA, Adenilton José daFERREIRA, Tiago Alessandro EspinolaPAULA NETO, Fernando Maciano dehttp://lattes.cnpq.br/2800100503239436SANTOS, Priscila Gabriele Marques dos2021-07-09T20:54:32Z2019-02-28SANTOS, Priscila Gabriele Marques dos. Quantum enhancements for machine learning based on a probabilistic quantum memory. 2019. 55 f. Dissertação (Programa de Pós-Graduação em Informática Aplicada) - Universidade Federal Rural de Pernambuco, Recife.http://www.tede2.ufrpe.br:8080/tede2/handle/tede2/8538Quantum machine learning arises from the interaction of fields of machine learning and quantum computing. Machine learning is a branch of artificial intelligence relevant in many areas. It provides computers the ability to learn autonomously from experience. Quantum computing, on the other hand, is a different computational paradigm. The processing of information and communication in a quantum computer makes use of the principles and properties of quantum mechanics. With this, it is possible to achieve computational effects that cannot be efficiently reached classically. Quantum computing raises new possibilities through promising approaches that make use of these effects. In fact, proposed quantum algorithms demonstrate their potential in outperforming classical algorithms in some tasks. The present work aims to contribute with the field of quantum machine learning. In order to do so, the use and applications of a quantum probabilistic memory as a tool to propose improved machine learning algorithms is investigated. Here, the quantum memory is used to develop improved procedures for tasks such as cross-validation, and the selection and evaluation of artificial neural network architectures. In addition, a weightless neural network model using the probabilistic quantum memory was evaluated and improved.A aprendizagem de máquina quântica surge a partir da interação das áreas de aprendizagem de máquina e computação quântica. Aprendizagem de máquina é um ramo da inteligência artificial de impacto em diversas áreas que provê aos computadores a habilidade de aprender de maneira autônoma a partir de experiências. A computação quântica, por outro lado, é um diferente paradigma computacional. O processamento de informação e comunicação em um computador quântico faz uso de princípios e propriedades da mecânica quântica, obtendo efeitos computacionais que não podem ser realizados eficientemente em computadores clássicos. A computação quântica levanta novas possibilidades a partir de abordagens promissoras que fazem uso desses efeitos. De fato, propostas de algoritmos quânticos demonstram seu potencial em superar a eficiência dos algoritmos clássicos em algumas tarefas. O presente trabalho busca contribuir com o campo de aprendizagem de máquina quântica. Para tanto, foi investigado o uso e as aplicações de uma memória probabilística quântica como ferramenta para propor algoritmos de aprendizagem de máquina melhorados. Aqui, a memória quântica foi utilizada para desenvolver procedimentos melhorados para as tarefas de validação cruzada, seleção e avaliação de arquiteturas de redes neurais artificiais. Além disso, um modelo de rede neural sem peso que utiliza a memória quântica foi avaliado e melhorado.Submitted by Mario BC (mario@bc.ufrpe.br) on 2021-07-09T20:54:31Z No. of bitstreams: 1 Priscila Gabriele Marques dos Santos.pdf: 690095 bytes, checksum: be6ab358832f0ebaa7c3766adc9616db (MD5)Made available in DSpace on 2021-07-09T20:54:32Z (GMT). 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dc.title.por.fl_str_mv Quantum enhancements for machine learning based on a probabilistic quantum memory
title Quantum enhancements for machine learning based on a probabilistic quantum memory
spellingShingle Quantum enhancements for machine learning based on a probabilistic quantum memory
SANTOS, Priscila Gabriele Marques dos
Computação quântica
Memória quântica
Aprendizagem de máquina
CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO
title_short Quantum enhancements for machine learning based on a probabilistic quantum memory
title_full Quantum enhancements for machine learning based on a probabilistic quantum memory
title_fullStr Quantum enhancements for machine learning based on a probabilistic quantum memory
title_full_unstemmed Quantum enhancements for machine learning based on a probabilistic quantum memory
title_sort Quantum enhancements for machine learning based on a probabilistic quantum memory
author SANTOS, Priscila Gabriele Marques dos
author_facet SANTOS, Priscila Gabriele Marques dos
author_role author
dc.contributor.advisor1.fl_str_mv SILVA, Adenilton José da
dc.contributor.referee1.fl_str_mv FERREIRA, Tiago Alessandro Espinola
dc.contributor.referee2.fl_str_mv PAULA NETO, Fernando Maciano de
dc.contributor.authorLattes.fl_str_mv http://lattes.cnpq.br/2800100503239436
dc.contributor.author.fl_str_mv SANTOS, Priscila Gabriele Marques dos
contributor_str_mv SILVA, Adenilton José da
FERREIRA, Tiago Alessandro Espinola
PAULA NETO, Fernando Maciano de
dc.subject.por.fl_str_mv Computação quântica
Memória quântica
Aprendizagem de máquina
topic Computação quântica
Memória quântica
Aprendizagem de máquina
CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO
dc.subject.cnpq.fl_str_mv CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO
description Quantum machine learning arises from the interaction of fields of machine learning and quantum computing. Machine learning is a branch of artificial intelligence relevant in many areas. It provides computers the ability to learn autonomously from experience. Quantum computing, on the other hand, is a different computational paradigm. The processing of information and communication in a quantum computer makes use of the principles and properties of quantum mechanics. With this, it is possible to achieve computational effects that cannot be efficiently reached classically. Quantum computing raises new possibilities through promising approaches that make use of these effects. In fact, proposed quantum algorithms demonstrate their potential in outperforming classical algorithms in some tasks. The present work aims to contribute with the field of quantum machine learning. In order to do so, the use and applications of a quantum probabilistic memory as a tool to propose improved machine learning algorithms is investigated. Here, the quantum memory is used to develop improved procedures for tasks such as cross-validation, and the selection and evaluation of artificial neural network architectures. In addition, a weightless neural network model using the probabilistic quantum memory was evaluated and improved.
publishDate 2019
dc.date.issued.fl_str_mv 2019-02-28
dc.date.accessioned.fl_str_mv 2021-07-09T20:54:32Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
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dc.identifier.citation.fl_str_mv SANTOS, Priscila Gabriele Marques dos. Quantum enhancements for machine learning based on a probabilistic quantum memory. 2019. 55 f. Dissertação (Programa de Pós-Graduação em Informática Aplicada) - Universidade Federal Rural de Pernambuco, Recife.
dc.identifier.uri.fl_str_mv http://www.tede2.ufrpe.br:8080/tede2/handle/tede2/8538
identifier_str_mv SANTOS, Priscila Gabriele Marques dos. Quantum enhancements for machine learning based on a probabilistic quantum memory. 2019. 55 f. Dissertação (Programa de Pós-Graduação em Informática Aplicada) - Universidade Federal Rural de Pernambuco, Recife.
url http://www.tede2.ufrpe.br:8080/tede2/handle/tede2/8538
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language por
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dc.relation.confidence.fl_str_mv 600
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dc.publisher.initials.fl_str_mv UFRPE
dc.publisher.country.fl_str_mv Brasil
dc.publisher.department.fl_str_mv Departamento de Estatística e Informática
publisher.none.fl_str_mv Universidade Federal Rural de Pernambuco
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