基于量子机器学习的自适应特征选择算法

Jincheng Zhang

Published Sep 5, 2026• 0 citations

Abstract

This paper introduces a novel approach to feature selection based on quantum machine learning, termed "Quantum Feature Selection" (QFS). Traditional feature selection methods often rely on manual adjustments, limiting their adaptability to data characteristics. QFS leverages the unique properties of quantum mechanics – specifically, superposition and entanglement – to automatically identify and select the most relevant features, enhancing the performance of machine learning models. The algorithm's core mechanism involves quantum state manipulation to evaluate feature importance and dynamically adjust feature selection based on observed noise and anomalies. This results in a more robust and accurate feature selection process, potentially surpassing the capabilities of current classical methods. We present a preliminary implementation demonstrating the effectiveness of QFS in a series of benchmark datasets, showcasing significant improvements in model accuracy and reduced overfitting. Further research will explore the scalability and broader applicability of this approach.

DOI: 10.5281/zenodo.22325948

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