基于量子自旋的拓扑信息学习

Jincheng Zhang

Published Sep 1, 2026• 0 citations

Abstract

This paper introduces a novel approach to topological information learning based on quantum spin dynamics. Traditional methods often rely on classical algorithms, while this work leverages the unique quantum properties of spin to learn hidden patterns within topological structures, enabling more effective topological data analysis. We propose a method that utilizes a quantum spin model to iteratively refine a topological map, guided by feedback loops and a carefully designed reward function. The core mechanism involves a continuous quantum evolution of the spin state, which is then used to update the topological map through a series of transformations. The proposed method demonstrates improved performance in identifying and analyzing complex topological structures compared to existing techniques. The study focuses on the design of a reward function to guide the quantum spin evolution, ensuring a stable and effective learning process.

DOI: 10.5281/zenodo.22222240

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