基于图的量子机器学习的拓扑结构建模

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper introduces a novel quantum machine learning framework based on topological structure modeling. Traditional machine learning methods often rely on human-designed algorithms, limiting their adaptability and efficiency. We propose a new approach that leverages graph theory and topology to automatically discover and optimize quantum machine learning algorithms. This framework allows us to effectively model complex system topological structures and predict their behavior. The core mechanism focuses on identifying and exploiting the inherent structural properties of quantum systems through graph-based representations, fostering enhanced computational performance and predictive capabilities. This work represents a significant advancement in the field of quantum machine learning, offering a robust and automated methodology for topological analysis and quantum algorithm design.

DOI: 10.5281/zenodo.22199250

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