Quantum Machine Learning: Feature Selection and Dimensionality Reduction

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper explores the application of quantum algorithms for feature selection and dimensionality reduction within the field of quantum machine learning. Traditional methods for these tasks often suffer from computational bottlenecks, particularly when dealing with high-dimensional datasets. We propose a framework that leverages the principles of quantum computing to accelerate these processes. Specifically, we investigate the transformation of feature selection and dimensionality reduction problems into quantum optimization problems. We then examine the utilization of quantum annealing and variational quantum sampling algorithms for solving these problems. The core claim is that by employing quantum algorithms, we can significantly improve the efficiency and accuracy of machine learning models. The theoretical framework presented outlines a novel approach to tackling these challenges, offering a potential pathway towards more powerful and scalable quantum machine learning systems. This work provides a foundational exploration into this emerging area, focusing on the algorithmic strategies and potential benefits. ---

DOI: 10.5281/zenodo.22185606

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