Quantum ensemble of trained classifiers
This work addresses the challenge of optimizing quantum machine learning models for researchers in quantum computing, but it is incremental as it builds on existing quantum ensemble methods.
The authors tackled the problem of enhancing quantum ensembles of quantum classifiers by adding an optimization method, resulting in improved performance on benchmark datasets.
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.