(2019-12-02) Araujo, Ismael Cesar da Silva; Nascimento, André Câmara Alves do; Silva, Adenilton José da; http://lattes.cnpq.br/0314035098884256; http://lattes.cnpq.br/0622594061462533; http://lattes.cnpq.br/7125338940009959
Quantum machine learning is a subarea of quantum computing that studies, among other things, the creation of equivalent classical classifiers. An ensemble of classifiers is a classification model in which the output is a combined result of the outputs of the classifiers contained in it. With the premiss that when using a sufficiently large ensemble with average classifiers, a good performance can still be obtained. This work investigates the differences in the performance of a quantum equivalent of an ensemble of classifiers, using trained and untrained classifiers. Where the simulation was mane, which the performance was measured through the calculation of the amplitude probabilities of the system. And the machine learning models of the ensemble were executed over benchmark datasets made available by scikitlearn library.
