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Quantum Machine Learning

Quantum ensemble of trained classifiers

arXiv

Authors: Ismael C. S. Araujo, Adenilton J. da Silva

Year

2020

Paper ID

22144

Status

Preprint

Abstract Read

~2 min

Abstract Words

107

Citations

N/A

Abstract

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.

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This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
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Through superposition, a quantum computer is capable of representing an exponentially large set of states, according to the number of qubits available.

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References & Citation Signals

[1] DOI https://doi.org/arXiv:2007.09293 [2] arXiv https://arxiv.org/abs/2007.09293 [3] Publisher https://arxiv.org/abs/2007.09293

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