Quantum-Driven Topological Data Analysis (Q-TDA)

Jincheng Zhang

Published Sep 7, 2026• 0 citations

Abstract

Quantum-Driven Topological Data Analysis (Q-TDA) represents a paradigm shift in data analysis, leveraging the unique properties of quantum mechanics to uncover hidden patterns and correlations within complex data sets. This paper proposes a novel quantum algorithm designed to effectively explore and interpret topological data, offering a fundamentally different approach compared to traditional methods. We delve into the core mechanism of utilizing quantum superposition and entanglement to achieve a deeper understanding of the underlying topological structure of the data, aiming to unveil patterns that are obscured by classical analysis. This research explores the potential of Q-TDA to revolutionize data discovery and analysis across various domains, including but not limited to medical imaging, materials science, and network security. The paper outlines the core concepts, proposed algorithm, and initial results demonstrating the potential of this approach.

DOI: 10.5281/zenodo.22574550

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