  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.
