Title: Quantum-Enhanced Topological Data Analysis (Q-TDA) for Complex Graphs

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

Quantum-Enhanced Topological Data Analysis (Q-TDA) presents a novel approach to graph analysis leveraging the power of quantum algorithms to uncover hidden patterns and relationships within complex graph networks. This paper explores the potential of combining quantum processing with established topological data analysis (TDA) techniques to achieve enhanced pattern detection capabilities. The core mechanism involves utilizing quantum algorithms for fundamental graph analysis, aiming to identify subtle correlations and structures that are currently inaccessible to classical methods. The proposed Q-TDA framework demonstrates significant improvements in pattern recognition and data visualization across a range of graph types, offering a potentially transformative approach to data exploration and insight generation. This work addresses limitations of current TDA methodologies by introducing a quantum-driven analysis that can reveal previously undetected correlations, ultimately improving the accuracy and efficiency of graph discovery.

DOI: 10.5281/zenodo.22501875

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