Analog quantum feature selection with neutral-atom quantum processors

We present a quantum-native approach to feature selection (QFS) based on analog quantum simulation with neutral-atom arrays. Feature relevance, measured through mutual information with the target variable, is encoded in site-dependent local detunings, while pairwise feature redundancy is mapped to distance-dependent van der Waals interactions through a two-dimensional atomic layout. The analog dynamics bias the system toward low-energy configurations that balance relevance and redundancy, and the resulting measurement bitstrings are post-processed to extract feature subsets of prescribed cardinality. The protocol is evaluated in simulation on three binary classification datasets: Adult Income, Bank Marketing, and Telco Churn. We quantify the main approximation and robustness properties of the pipeline, including multidimensional-scaling reconstruction error, subset stability across embedding seeds, sensitivity to post-processing parameters, and predictive performance across repeated train/test splits. QFS achieves competitive, dataset-dependent performance relative to mutual-information ranking, tree-based feature importance, L1-logistic selection, and classical optimizers of the same relevance-redundancy objective. The results establish a physically interpretable neutral-atom implementation of a feature-selection objective and identify the embedding, noise-modelling, and scalability requirements that must be addressed in larger experimental realizations.