Quantum-Enhanced Topological Data Analysis (QTDDA)

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

Abstract: Quantum-Enhanced Topological Data Analysis (QTDDA) explores the potential of quantum computation to revolutionize data representation and pattern discovery within topological data analysis (TDA). This paper introduces a novel algorithm, Quantum-Guided Topological Feature Extraction (QGTE), designed to leverage quantum properties – specifically superposition and entanglement – to enhance the representation of topological features. We demonstrate the efficacy of QGTE through a series of experiments, showcasing improvements in data quality and pattern identification compared to existing TDA techniques. This work provides a foundational basis for future research into utilizing quantum mechanics to enhance the capabilities of TDA.

DOI: 10.5281/zenodo.22290604

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