Feature Selection on Quantum Computers

TL;DR

This paper introduces a quantum-compatible feature selection algorithm based on QUBO, aiming to improve feature importance assessment in machine learning by leveraging quantum hardware capabilities.

Contribution

The paper presents a novel QUBO-based feature selection method that outperforms traditional greedy approaches and can be implemented on quantum hardware.

Findings

Higher-quality feature selection solutions compared to greedy methods

Competitive performance on benchmark datasets

Feasibility of implementing the algorithm on quantum hardware

Abstract

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 higherquality 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 datasets. We observe competitive performance.