Implementação quântica de um algoritmo genético

Autores

DOI:

https://doi.org/10.36561/ING.27.14

Palavras-chave:

Algoritmo Genético Quântico, Computação Quântica, Algoritmo Genético Quântico Híbrido

Resumo

Este trabalho fornece uma visão generalizada do estado atual dos algoritmos genéticos quânticos (QGAs), mostrando os avanços feitos neste campo de pesquisa nos últimos 24 anos. Os QGAs combinam conceitos da computação quântica e algoritmos genéticos clássicos (CGAs), permitindo que eles abordem problemas complexos de busca e otimização de forma eficiente. As principais descobertas e contribuições desses algoritmos quânticos são apresentadas, destacando as tendências e abordagens mais promissoras, bem como os desafios e limitações que precisam ser superados. Novas abordagens e técnicas de implementação para QGAs são apresentadas, incluindo operadores genéticos quânticos e esquemas de codificação eficientes que contribuem para melhorar o desempenho e a convergência dos algoritmos. QGAs e outras abordagens semelhantes, como CGAs e algoritmos quânticos puros, são comparados, destacando as vantagens e desvantagens relativas dos QGAs em comparação com suas versões clássicas. Uma implementação de QGA usando a biblioteca Qiskit também é mostrada. São apresentadas a seleção dos métodos utilizados para a geração da população inicial, o cruzamento e a mutação das diferentes populações dos circuitos quânticos simulados nos experimentos realizados, exemplificando as vantagens significativas que estes podem trazer em comparação com abordagens clássicas.

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Publicado

2024-12-13

Como Citar

[1]
M. Solar, V. Figueroa, F. Manriquez, F. Pizarro, e L. Dombrovskaia, “Implementação quântica de um algoritmo genético”, Memoria investig. ing. (Facultad Ing., Univ. Montev.), nº 27, p. 220–237, dez. 2024.

Edição

Seção

Trabalhos apresentados no 1º Workshop Latino-Americano de Computação Quântica (TLISC 2024)