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EPJ Quantum Technology

Quantum hybrid feature selector

Vadim Lopatkin, Asel Sagingalieva, Luca Lusnig, Tatjana Protasevich, Bernadette Behnke, Alexey A. Melnikov

Published Mar 18, 2026• 2 citations
Publisher

Abstract

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.

DOI
10.1140/epjqt/s40507-026-00491-1

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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<0.001p < 0.001p<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.
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Feature selection was a critical process in machine learning that enhances model performance by identifying the most relevant features from high-dimensional datasets. This book chapter delves into various feature selection methodologies, emphasizing the significance of hybrid approaches that leverage the strengths of different techniques, including filter, wrapper, and embedded methods. It critically examines the advantages and limitations of each method, providing insights into their applicability across diverse domains. The chapter also explores future directions in hybrid feature selection, including the integration of advanced algorithms, adaptation to varying data types, and the incorporation of domain knowledge. Emphasizing computational efficiency and real-time application potential, this work serves as a comprehensive guide for researchers and practitioners aiming to enhance machine learning models through effective feature selection. The findings and discussions presented herein contribute to the ongoing discourse in the field and provide a roadmap for future research initiatives.
This paper explores the application of quantum computing to the critical tasks of feature selection and dimensionality reduction within machine learning. Traditional methods often struggle with high-dimensional data, leading to computational bottlenecks and potentially reduced model accuracy. We propose a novel approach leveraging the principles of quantum error correction and quantum entanglement to construct an algorithm capable of efficiently identifying and reducing the most relevant features. The core mechanism utilizes quantum machine learning techniques, specifically quantum feature mapping and feature extraction, to transform data into a quantum state that inherently captures feature relationships. This allows for a more effective selection process based on quantum interference and the exploration of complex correlations. We demonstrate, theoretically, the potential advantages of this approach over classical methods, highlighting improvements in both efficiency and predictive accuracy. The algorithm's design incorporates error correction to mitigate the effects of noise inherent in quantum systems, further enhancing its robustness. The research contributes to the growing field of quantum machine learning, offering a new paradigm for tackling complex data challenges.
Quantum-enhanced feature selection is a promising approach to address the challenges posed by high-dimensional data in machine learning. Traditional feature selection methods often struggle to effectively identify the most informative features, leading to suboptimal model performance and increased computational cost. This paper proposes a novel quantum algorithm designed to accelerate and enhance feature selection, offering a significant improvement over existing techniques. We explore the potential of quantum algorithms, specifically focusing on Grover's search algorithm and quantum-enhanced k-means clustering, to achieve efficient identification of relevant features. The core mechanism involves exploiting quantum phenomena like superposition and entanglement to enhance the search space and improve the selection process. We demonstrate the effectiveness of our approach through theoretical analysis and preliminary experiments on benchmark datasets, showcasing its potential to reduce computational cost and improve accuracy in high-dimensional feature selection scenarios.
Feature selection in high-dimensional data spaces continues to be an intrinsic challenge in machine learning algorithms, especially when the number of features regularly exceeds thousands. Traditional techniques including mutual information filters, LASSO regularization, and recursive feature elimination are limited by the computational inefficiency of brute force searching as well as poor scalability in the ultra-high-dimensional setting. In this paper, a novel technique, the Hybrid Quantum-Classical Variational Algorithm (HQCVA), that combines quantum computing and classical optimization methods in the Variational Quantum Eigensolver (VQE) approach is introduced. Specifically, the quantum part is responsible for representing the correlation structure of the input features via the Ising model. To reduce composite loss, the classical sub-system uses the Adam stochastic optimizer to update variational parameters iteratively while also balancing density between predictive accuracies, densifications and depth of quantum circuits. Using five benchmark datasets (22,000 gene data; 12,000 pixels of hyperspectral imagery; 8,500 data points of financial time series), HQCVA produces a 12-18% higher F1-score than leading, state-of-the-art classical systems while decreasing the number of selected features by 40-65%. All simulations of circuits are performed on IBM Qiskit Aer with noise added, to simulate Eagle r3 consoles. Furthermore, the results indicate that the quantum advantage scales with increasing feature dimension through this process to demonstrate that near-term quantum devices have practical applications in real-world feature selection applications.
This work investigates a hybrid quantum-classical machine learning methodology that combines deep learning with quantum computing to improve predictive analytics. The method starts by changing basic input features into the best mixed forms using classical weight matrices and quantum state encoding. A unitary operator refines quantum states to enhance feature extraction and representation. Gradient-based alterations repeatedly modify feature weights during optimization to improve accuracy. Quantum circuits not only change data but also enhance system separability and resilience. Normalizing and enhancing extracted properties using quantum and classical transformations ensures stability and consistency. Performance trials demonstrate that the hybrid technique improves accuracy, precision, recall, computational complexity, execution time, and energy economy. The hybrid model always outperforms machine learning, deep learning, and quantum mechanics-based approaches. The technique is now more scalable, making enormous data sets easier to analyze. Faster convergence rates and noise tolerance improve model performance in real-world applications. The findings suggest that quantum advances and classical learning approaches improve model interpretability and decision-making reliability. The research found hybrid quantum-classical machine learning to be a powerful, scalable, and efficient predictive modeling solution. This method improves precision while conserving computing power. By using both paradigms, the technique provides deeper insights and better predictions. As a major facilitator for future AI-driven applications, the hybrid model provides a possible solution for difficult machine learning challenges. These developments make hybrid models available.