Quantum Machine Learning with Quantum Feature Selection

Jincheng Zhang

Published Aug 28, 2026• 0 citations

Abstract

This paper investigates the potential of combining quantum machine learning with quantum feature selection to enhance the performance of machine learning models. The core idea is to frame the machine learning problem as a quantum feature selection problem, leveraging quantum phenomena such as entanglement and superposition to effectively identify the most informative features. Traditional feature selection methods often struggle with high-dimensional datasets and may not fully exploit the inherent correlations between features. Quantum feature selection, utilizing quantum computers, offers a fundamentally different approach, potentially capable of identifying subtle relationships and patterns that classical methods miss. This paper explores the theoretical framework, outlining the conversion process and the utilization of quantum mechanics to optimize feature selection. The proposed method aims to achieve superior classification and regression results compared to conventional machine learning algorithms. We present a detailed mathematical description of the process, emphasizing the key quantum concepts involved. The ultimate goal is to demonstrate a viable pathway for advancing machine learning capabilities through the integration of quantum computing principles.

DOI: 10.5281/zenodo.22140128

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