Adaptive Quantum Machine Learning Feature Reduction Algorithm

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

This paper presents an innovative adaptive quantum machine learning feature reduction algorithm designed to enhance model efficiency and reduce computational complexity within the context of quantum machine learning. Traditional feature selection methods often rely on fixed, predefined feature sets, failing to effectively leverage the inherent properties of quantum systems. Our proposed algorithm dynamically adjusts the dimensionality of input data, minimizing the number of features while maintaining optimal model performance. We explore the potential of quantum computation to accelerate this process, offering a novel approach to feature reduction. The core mechanism centers around leveraging quantum entanglement and superposition to represent and manipulate data representations, enabling a more efficient and targeted feature selection process. This work investigates the potential benefits of this approach, particularly in scenarios where data dimensionality is high and computational cost is a significant constraint. The paper concludes with a discussion of the algorithm's theoretical foundations, experimental validation, and future research directions.

DOI: 10.5281/zenodo.22504195

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