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<0.001 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.
We investigate two modified Quantum Evolutionary methods for solving real value problems. The Quantum Inspired Evolutionary Algorithms (QIEA) were originally used for solving binary encoded problems and their signature features follow superposition of multiple states on a quantum bit and a rotation gate. In order to apply this paradigm to real value problems, we propose two quantum methods Half Significant Bit (HSB) and Stepwise Real QEA (SRQEA), developed using binary and real encoding respectively, while keeping close to the original quantum computing metaphor. We evaluate our approaches against sets of multimodal mathematical test functions and real world problems, using five performance metrics and include comparisons to published results. We report the issues encountered while implementing some of the published real QIEA techniques. Our methods focus on introducing and implementing new rotation gate operators used for evolution, including a novel mechanism for preventing premature convergence in the binary algorithm. The applied performance metrics show superior results for our quantum methods on most of the test problems (especially for the more complex and challenging ones), demonstrating faster convergence and accuracy.
Quantum entanglement is a fundamental phenomenon in quantum mechanics, exhibiting correlations that defy classical intuition. Harnessing this correlation, we propose a novel algorithm, Quantum Entanglement Field Simulation (QEFS), designed to reconstruct complex quantum states directly from entangled fields. This algorithm leverages a '纠缠场映射' technique to translate the state's description into a distribution of entangled fields, enabling precise calculation of quantum state properties using high-level quantum computation. This approach offers a significant advancement over traditional methods, minimizing reliance on classical measurements and providing a pathway toward more accurate and efficient quantum state analysis. This research explores the potential of QEFS for reconstructing intricate quantum states, particularly those exhibiting multiple entangled states, and introduces a methodology for detailed calculations.