Quantum hybrid feature selector

Abstract Unsupervised feature selection is essential for high-dimensional machine learning tasks, as it improves model quality and efficiency while providing interpretable insights into datasets. However, existing methods often struggle to simultaneously achieve robustness, reliability, interpretability, and computational efficiency. Furthermore, feature selection in quantum machine learning remains largely unexplored despite the potential of quantum approaches to capture complex feature interactions. In this work, we propose the Quantum Hybrid Feature Selector, a novel unsupervised feature selection framework that combines quantum-enhanced feature extraction with interpretable scoring algorithms. Our main contributions are threefold: we introduce a quantum hybrid autoencoder pipeline that couples feature extraction with feature ranking in the original input space, we define three scoring mechanisms: SHAP-based scoring, correlation-matrix scoring, and weight-based analysis, that relate latent features to original features without requiring external predictive models, and we provide comprehensive empirical evaluation on synthetic and real-world benchmarks with statistical validation. On Madelon-style synthetic datasets, our quantum SCM achieves up to 23. 5% improvement in Mean Informative Rank and 9. 7% improvement in Informative Ratio compared to classical alternatives, with all improvements statistically significant at p p 0. 001. On the Communities and Crime dataset with injected noise features, both quantum and classical SCM variants achieve near-perfect noise elimination, demonstrating effective denoising capability on real-world data. We also show that expert input is essential for evaluating feature selectors, as different methods emphasize distinct aspects of the data despite similar aggregate metrics. Finally, we evaluate QHFS under realistic hardware noise conditions using device-informed simulations based on IBM superconducting quantum processors, demonstrating graceful performance degradation and systematic improvement with increased shot budgets, which supports the practical viability of our approach on near-term quantum devices.