Quantum Machine Learning: A Topological Mapping Approach

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

Quantum Machine Learning (QML) is a rapidly evolving field that combines the principles of quantum mechanics with machine learning algorithms. This paper explores the potential of QML to revolutionize data processing and feature extraction, particularly in the context of complex high-dimensional data. We propose a novel approach centered around the development of a quantum-enhanced topological mapping technique. This technique leverages the unique capabilities of quantum machine learning, specifically quantum algorithms, to automatically map data and extract meaningful features. Quantum mechanics offers potential advantages in representing and manipulating information in a fundamentally different way than classical systems, potentially leading to more efficient and insightful data processing. The core focus of this work is to demonstrate the feasibility and effectiveness of this approach through a detailed theoretical analysis and preliminary experimental validation. We will investigate the potential benefits of this method in various applications, including data analysis and pattern recognition. This paper will also address the challenges and future directions for this rapidly developing area.

DOI: 10.5281/zenodo.22292624

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