A quantum computing-based approach for feature selection in regression models

Feature Selection (FS) is an essential data preprocessing technique aimed at identifying the most relevant features for predictive models. However, traditional FS approaches often struggle to capture complex interactions in real-world datasets, limiting their ability to fully support high-performing Machine Learning (ML) models. This study introduces a QA-compatible QUBO-based FS formulation for regression that models both linear and non-linear feature dependencies through hybrid linear and quadratic coefficients. Using three datasets; Wine Quality, Insurance, and Bike Sharing System (BSS) we evaluate the method across multiple predictive models to assess its robustness and generalizability. Results show competitive predictive performance and, in several settings, more compact feature subsets than Mutual Information and the MI_QC baseline. This study introduces a methodological refinement over previous QUBO-based FS methods and demonstrates practical applicability through statistically robust benchmarking.