Quantum-Inspired Entropy Maximization for Feature Selection

Jincheng Zhang

Published Sep 7, 2026• 0 citations

Abstract

This paper presents a novel approach to feature selection leveraging quantum-inspired optimization, specifically quantum annealing, to maximize entropy within a candidate feature subset. Traditional feature selection techniques often face challenges in high-dimensional datasets, struggling to pinpoint the most impactful features. Our method addresses this by framing feature selection as an entropy maximization problem. The core idea is to employ a quantum annealing algorithm to explore the feature space, iteratively refining the subset until the entropy reaches a maximum value. This approach aims to identify features that contribute significantly to the information content related to the target variable. We demonstrate the potential of this approach compared to conventional methods, highlighting its ability to effectively handle complex datasets and improve model performance. The theoretical framework is presented, along with a detailed explanation of the algorithm and its underlying principles. The results, while not based on empirical experimentation due to the nature of the theoretical exploration, provide a strong argument for the viability and potential advantages of quantum-inspired techniques in feature selection.

DOI: 10.5281/zenodo.22635208

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