Quantum-Based Feature Selection Method for Machine Learning

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

This paper proposes a novel feature selection method leveraging the principles of quantum mechanics, specifically quantum entanglement and decoherence, to enhance the performance of machine learning models. Traditional feature selection techniques often struggle with computational complexity and the curse of dimensionality, particularly in high-dimensional datasets. We introduce a quantum-based approach that utilizes quantum entanglement to encode and select features, aiming to overcome these limitations. The core claim is that exploiting quantum entanglement can significantly improve feature selection efficiency and accuracy. The method constructs a quantum model based on entanglement, employing its unique properties to identify and prioritize relevant features. This approach represents a shift from classical algorithms towards harnessing quantum phenomena for improved data analysis and model building. The paper details the theoretical framework, the proposed model architecture, and outlines the potential benefits and future research directions in this emerging field.

DOI: 10.5281/zenodo.22292933

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