Title: Quantum-Enhanced Topological Data Analysis - Adaptive Feature Extraction for Complex Data Clusters

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

Quantum-enhanced topological data analysis (TDA) offers a promising paradigm shift in data analysis, particularly for complex datasets exhibiting intricate patterns and hierarchical structures. This paper introduces a novel technique, Quantum-Enhanced Topological Data Analysis (QETDA), which leverages quantum algorithms to dynamically extract features for improved identification of complex data clusters. We demonstrate the effectiveness of QETDA through a series of simulations and preliminary experiments, showcasing its ability to surpass traditional methods in identifying subtle and significant clusters within high-dimensional data. The core mechanism centers around quantum-induced feature transformation, allowing for an adaptive and efficient exploration of the data space. This research addresses limitations inherent in existing data analysis approaches, particularly in handling the inherent complexity of data landscapes, and holds significant potential for uncovering previously hidden patterns.

DOI: 10.5281/zenodo.22453267

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