Mücke, SaschaHeese, RaoulMüller, SabineWolter, MoritzPiatkowski, Nico2025-02-262025-02-262023-02-20http://hdl.handle.net/2003/4350510.17877/DE290R-25338In 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.enQuantum machine intelligence; 5(1)https://creativecommons.org/licenses/by/4.0/Feature selectionVQEQuantum annealerQUBO004ResearchArticleMerkmalQuadratische binäre Optimierung ohne Nebenbedingungen