Feature selection on quantum computers

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dc.contributor.authorMücke, Sascha
dc.contributor.authorHeese, Raoul
dc.contributor.authorMüller, Sabine
dc.contributor.authorWolter, Moritz
dc.contributor.authorPiatkowski, Nico
dc.date.accessioned2025-02-26T10:30:28Z
dc.date.available2025-02-26T10:30:28Z
dc.date.issued2023-02-20
dc.description.abstractIn 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.en
dc.identifier.urihttp://hdl.handle.net/2003/43505
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-25338
dc.language.isoen
dc.relation.ispartofseriesQuantum machine intelligence; 5(1)
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectFeature selectionen
dc.subjectVQEen
dc.subjectQuantum annealeren
dc.subjectQUBOen
dc.subject.ddc004
dc.subject.rswkMerkmalde
dc.subject.rswkQuadratische binäre Optimierung ohne Nebenbedingungende
dc.titleFeature selection on quantum computersen
dc.typeText
dc.type.publicationtypeResearchArticle
dcterms.accessRightsopen access
eldorado.secondarypublicationtrue
eldorado.secondarypublication.primarycitationMücke, S. et al. (2023) ‘Feature selection on quantum computers’, Quantum machine intelligence, 5(1). Available at: https://doi.org/10.1007/s42484-023-00099-z
eldorado.secondarypublication.primaryidentifierhttps://doi.org/10.1007/s42484-023-00099-z

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