Feature selection on quantum computers

Sascha Mücke

doi:10.1007/s42484-023-00099-z

Feature selection on quantum computers

2023-02-20

18

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AI

DOI：10.1007/s42484-023-00099-z

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Abstract

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In machine learning, fewer features reduce model complexity. Carefully assessing the influence of each input feature on the model quality is therefore a crucial preprocessing step. We propose a novel feature selection algorithm based on a quadratic unconstrained binary optimization (QUBO) problem, which allows to select a specified number of features based on their importance and redundancy. In contrast to iterative or greedy methods, our direct approach yields higher-quality solutions. QUBO problems are particularly interesting because they can be solved on quantum hardware. To evaluate our proposed algorithm, we conduct a series of numerical experiments using a classical computer, a quantum gate computer, and a quantum annealer. Our evaluation compares our method to a range of standard methods on various benchmark data sets. We observe competitive performance.

Keywords:

Feature selection

VQE

Quantum annealer

QUBO

Feature selection on quantum computers

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