Quantum Machine Learning Adaptive Feature Selection

Jincheng Zhang

Published Sep 1, 2026• 0 citations

Abstract

Quantum Machine Learning (QML) offers a paradigm shift in computational power and algorithmic capabilities, potentially revolutionizing various fields, including machine learning. This research explores the application of quantum algorithms to enhance feature selection, a crucial step in many machine learning models. We introduce a novel quantum-enhanced feature selection algorithm, termed "Quantum Feature Selection," which leverages quantum entanglement and superposition to dynamically adjust feature importance during the training process. The core mechanism centers around exploiting these quantum phenomena to guide the selection of the most relevant features, ultimately leading to improved model accuracy and robustness. The algorithm's design aims to surpass existing static feature selection techniques by offering a dynamically adaptive approach. This work presents a comprehensive analysis of the algorithm's performance, including theoretical justifications and experimental validation utilizing a diverse dataset. This research is motivated by the potential to address challenges in traditional machine learning, specifically concerning the effective handling of high-dimensional data and the optimization of complex models. Quantum Machine Learning has the potential to provide solutions that are far beyond traditional computational methods.

DOI: 10.5281/zenodo.22222579

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