基于量子机器学习的拓扑语言学习

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

This paper introduces a novel quantum machine learning approach to learn complex topological structures from limited topological languages. Traditional topological language learning methods often struggle with the complexity of real-world datasets. We propose a quantum algorithm that leverages quantum entanglement and superposition to efficiently explore the latent spaces of topological languages, enabling the discovery of intricate patterns and relationships. The core mechanism involves quantum-enhanced feature extraction and representation learning, allowing for the identification of subtle structural cues that are difficult to capture with classical methods. We demonstrate the effectiveness of this method through the successful learning of a complex topological language with applications in graph analysis and pattern recognition. The results highlight the potential of quantum machine learning to overcome the limitations of classical approaches, particularly in the domain of topological data analysis.

DOI: 10.5281/zenodo.22293408

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