Geometry Topology and Quantum Machine Learning

Jincheng Zhang

Published Sep 5, 2026• 0 citations

Abstract

This paper explores the synergistic potential of geometry topology and quantum machine learning, investigating the possibility of leveraging quantum machine learning to enhance the efficiency and precision of machine learning algorithms. The core claim posits that integrating geometric topology, a field of mathematics concerned with shapes and spaces, with quantum machine learning offers a novel approach to improve computational efficiency and algorithmic accuracy. This research will examine the connection between these two disciplines, focusing on how quantum mechanics can be utilized to optimize data processing and model training. We will delve into the potential benefits of this combination, analyzing specific algorithms and datasets to demonstrate a pathway toward more robust and effective machine learning systems. The paper concludes with a discussion of future research directions and potential applications.

DOI: 10.5281/zenodo.22326915

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