CC BY 4.0Mücke, SaschaSaschaMückeHeese, RaoulRaoulHeeseMüller, SabineSabineMüllerWolter, MoritzMoritzWolterPiatkowski, NicoNicoPiatkowski2023-06-192023-06-192023-02-20https://publica.fraunhofer.de/handle/publica/443009https://doi.org/10.24406/publica-150010.1007/s42484-023-00099-z10.24406/publica-1500In 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.enjournal article