Quantum-Enhanced Topological Data Analysis (TDAN)

Jincheng Zhang

Published Sep 7, 2026• 0 citations

Abstract

This paper presents a novel Quantum-Enhanced Topological Data Analysis (TDAN) method designed to identify subtle topological patterns within complex datasets. Traditional TDAN relies on iterative clustering and feature extraction, often struggling with the inherent complexity of topological data. We introduce a quantum entanglement-based approach that exploits quantum correlations to enhance data understanding and uncover hidden topological relationships. This research addresses limitations in existing TDAN methods by leveraging quantum phenomena to improve pattern recognition and data insight. The core mechanism involves establishing entanglement between data points and using this entanglement to identify correlations that would otherwise be obscured. This process significantly improves the accuracy and efficiency of topological pattern detection. We provide a detailed theoretical framework and empirical validation of this approach, demonstrating its potential to revolutionize data analysis and discovery.

DOI: 10.5281/zenodo.22635383

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