Title: Topological Geometry Quantum Machine Learning Model

Jincheng Zhang

Published Sep 5, 2026• 0 citations

Abstract

This paper explores the integration of topological geometry into quantum machine learning, proposing a novel model leveraging quantum entanglement for enhanced accuracy and computational efficiency. Traditional machine learning relies heavily on classical computation, presenting limitations in complex data analysis and high-dimensional spaces. This research investigates the potential of quantum mechanics to address these limitations through the application of topological geometry. We detail a quantum learning algorithm predicated on the inherent properties of topological spaces, employing quantum entanglement as a key mechanism to accelerate computation and improve model representation. The core claim centers on the development of a quantum model that utilizes the unique characteristics of topological geometry to achieve superior performance compared to existing classical machine learning algorithms in specific scenarios. The paper outlines the proposed mechanism, discusses potential applications across diverse domains, and presents preliminary results demonstrating the model's efficacy. Finally, we address the challenges and future directions for this research.

DOI: 10.5281/zenodo.22322648

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