Analog Quantum Feature Selection with Neutral-Atom Quantum Processors

We present a quantum-native approach to quantum feature selection (QFS) based on analog quantum simulation with neutral atom arrays, adaptable to a variety of academic and industrial applications. In our method, feature relevance-measured via mutual information with the target-is encoded as local detuning amplitudes, while feature redundancy is embedded through distance-dependent van der Waals interactions, constrained by the Rydberg blockade radius. The system is evolved adiabatically toward low-energy configurations, and the resulting measurement bitstrings are used to extract physically consistent subsets of features. The protocol is evaluated through simulations on three benchmark binary classification datasets: Adult Income, Bank Marketing, and Telco Churn. Compared to classical methods such as mutual information ranking and Boruta, combined with XGBoost and Random Forest classifiers, our quantum-computing approach achieves competitive or superior performance. In particular, for compact subsets of 2-5 features, analog QFS improves mean AUC scores by 1.5-2.3% while reducing the number of features by 75-84%, offering interpretable, low-redundancy solutions. These results demonstrate that programmable Rydberg arrays offer a viable platform for intelligent feature selection with practical relevance in machine learning pipelines, capable of transforming computational quantum advantage into industrial quantum usefulness.