基于量子自旋的拓扑信息分析

Jincheng Zhang

Published Sep 1, 2026• 0 citations

Abstract

Quantum spin provides a unique platform for exploring topological data analysis, offering a fundamentally different approach compared to classical methods. This paper investigates the potential of leveraging quantum spin's inherent properties, specifically its quantum entanglement and superposition, to develop a novel algorithm for uncovering hidden topological structures within data. We propose a quantum-enhanced algorithm that utilizes the principles of quantum mechanics to identify and characterize topological features without the limitations of traditional computational approaches. The core mechanism centers around the manipulation of quantum spin states to reveal patterns and relationships within complex datasets, promising a significant advancement in topological data analysis. This work explores the theoretical foundations and potential practical applications of this approach, highlighting its distinct advantages and limitations.

DOI: 10.5281/zenodo.22222149

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