AI RESEARCH
Quantum Feature Selection with Higher-Order Binary Optimization on Trapped-Ion Hardware
arXiv CS.LG
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ArXi:2604.26834v1 Announce Type: cross We present a quantum feature-selection framework based on a higher-order unconstrained binary optimization (HUBO) formulation that explicitly incorporates multivariate dependencies beyond standard quadratic encodings. In contrast to QUBO-based approaches, the proposed model includes one-, two-, and three-body interaction terms derived from mutual-information measures, enabling the objective function to capture feature relevance, pairwise redundancy, and higher-order statistical structure within a unified energy model.