  Through superposition, a quantum computer is capable of representing an
exponentially large set of states, according to the number of qubits available.
Quantum machine learning is a subfield of quantum computing that explores the
potential of quantum computing to enhance machine learning algorithms. An
approach of quantum machine learning named quantum ensembles of quantum
classifiers consists of using superposition to build an exponentially large
ensemble of classifiers to be trained with an optimization-free learning
algorithm. In this work, we investigate how the quantum ensemble works with the
addition of an optimization method. Experiments using benchmark datasets show
the improvements obtained with the addition of the optimization step.
