Quantum computer based Feature Selection in Machine Learning

TL;DR

This paper explores using quantum computing to perform feature selection in machine learning, comparing its effectiveness to classical methods on small and larger datasets, highlighting current limitations due to error rates.

Contribution

It formulates feature selection as a QUBO problem suitable for quantum computing and compares quantum and classical methods in different dataset sizes.

Findings

QUBO methods' performance varies depending on the dataset.

Classical stochastic methods currently outperform quantum approaches due to error rates.

Quantum methods show potential but need improvements for larger datasets.

Abstract

The problem of selecting an appropriate number of features in supervised learning problems is investigated in this paper. Starting with common methods in machine learning, we treat the feature selection task as a quadratic unconstrained optimization problem (QUBO), which can be tackled with classical numerical methods as well as within a quantum computing framework. We compare the different results in small-sized problem setups. According to the results of our study, whether the QUBO method outperforms other feature selection methods depends on the data set. In an extension to a larger data set with 27 features, we compare the convergence behavior of the QUBO methods via quantum computing with classical stochastic optimization methods. Due to persisting error rates, the classical stochastic optimization methods are still superior.