Quantum Machine Learning for Topological Structure Modeling

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

This paper introduces a novel quantum machine learning framework for the automated modeling of complex topological structures. Traditional topological modeling approaches often rely on computationally intensive algorithms, limiting their applicability to relatively simple systems. This work proposes a quantum-enhanced approach that leverages quantum machine learning to learn and represent topological properties more efficiently. The framework's core mechanism involves utilizing quantum algorithms to explore and refine the representation of topological features, ultimately leading to improved model performance across various applications. The study demonstrates the framework's effectiveness in modeling complex systems with intricate topological landscapes, offering a significant advancement over existing techniques.

DOI: 10.5281/zenodo.22489282

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