Feature selection on quantum computers

In machine learning, fewer features reduce model complexity. Carefully assessing the influence of each input feature on quality is therefore a crucial preprocessing step. We propose novel selection algorithm based quadratic unconstrained binary optimization (QUBO) problem, which allows to select specified number their importance and redundancy. contrast iterative or greedy methods, our direct approach yields higherquality solutions. QUBO problems are particularly interesting because they can be solved quantum hardware. To evaluate proposed algorithm, we conduct series numerical experiments using classical computer, gate computer annealer. Our evaluation compares method range standard methods various benchmark datasets. observe competitive performance.