Performance of Quantum and Classical Algorithms in Supervised Machine Learning Training

Authors

  • Mariana Godoy Vazquez Miano Faculdade de Tecnologia de Americana - Curso de Tecnologia em Análise e Desenvolvimento de Sistemas

DOI:

https://doi.org/10.47283/244670492021090281

Abstract

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.

Author Biography

  • Mariana Godoy Vazquez Miano, Faculdade de Tecnologia de Americana - Curso de Tecnologia em Análise e Desenvolvimento de Sistemas

    Profa. Dr. Mariana Godoy Vazquez Miano

    Pós-Doutorado em Engenharia de Produção pela Universidade Federal de São Carlos (UFSCAR - 2014). Doutorado (2009) e Mestrado (2004) em Engenharia Mecânica pela Universidade Estadual de Campinas (UNICAMP). Licenciatura Plena em Matemática pela Universidade Estadual Paulista (UNESP - 2001). Tem experiência nas áreas de Matemática, Engenharia e
    Computação, com ênfase em Matemática Aplicada à Engenharia e Simulação de Sistemas e Desempenho de Redes de Internet.

    Contato: vazquez.prof@gmail.com

    Fonte: CNPQ – Curriculo Lattes

Published

2022-02-04