Open AccessDESEMPENHO DE ALGORITMOS QUÂNTICOS E CLÁSSICOS EM TREINAMENTO DE MACHINE LEARNING SUPERVISIONADO
Author(s) -
Mariana Godoy Vazquez Miano,
Aleccheevina Silva de Oliveira
Publication year - 2021
Publication title -
revista tecnológica da fatec americana
Language(s) - English
Resource type - Journals
ISSN - 2446-7049
DOI - 10.47283/244670492021090281
Subject(s) - computer science , machine learning , quantum , artificial intelligence , support vector machine , theme (computing) , algorithm , quantum algorithm , quantum computer , physics , quantum mechanics , operating system
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.