Performance of Quantum and Classical Algorithms in Supervised Machine Learning Training
DOI:
https://doi.org/10.47283/244670492021090281Abstract
This article addresses the interdisciplinary theme of Quantum Computing with Machine Learning, two technologies potentially capable of making changes in how computing is performed, solving initially unsolvable problems. The focus of this research was Quantum Computing applications that result in computational performance gain in specific Machine Learning tasks. The objective is to analyze the feasibility of using quantum algorithms for Machine Learning. More specifically, to analyze which quantum algorithms can be applied to Machine Learning tasks, compared to classical algorithms, in the search for better performance. For the development of the research, a bibliographic review of quantum algorithms was carried out and, subsequently, the implementation and performance verification of the quantum algorithm QSVM and its corresponding classic version SVM, in supervised learning with the AD HOC and IRIS datasets.
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A “Revista Tecnológica da Fatec de Americana” é uma publicação semestral digital de responsabilidade da “Faculdade de Tecnologia de Americana”, “Centro Estadual de Educação Tecnológica Paula Souza”, com a publicação de trabalhos de caráter interdisciplinar realizados pelas comunidades discente, docente e pesquisadores internos e bem como por pesquisadores externos.
The “Revista Tecnológica da Fatec Americana” is a biannual digital publication, under “Faculdade de Tecnologia de Americana” responsability, from “Centro Estadual de Educação
Tecnológica Paula Souza”, which encompasses interdisciplinary papers submitted by students, professors and researches of the community as well as external researches.
La “Revista Tecnológica da Fatec Americana” es una publicación digital semestral de responsabilidad de esa “Faculdade de Tecnologia de Americana”, “Centro Estadual de Educação Tecnológica Paula Souza”, cuyo objetivo es publicar trabajos de carácter interdisciplinario realizados tanto por el profesorado, por el alunnado e investigadores internos, como por investigadores externos.
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