Title: Quantum-Accelerated Topological Data Analysis (Q-TDA)

Jincheng Zhang

Published Aug 29, 2026• 0 citations

Abstract

Quantum-Accelerated Topological Data Analysis (Q-TDA) presents a novel approach to topological data analysis (TDA) utilizing quantum entanglement to rapidly identify and characterize topological features within large, complex datasets. The core mechanism centers around creating a 'quantum fingerprint' of the data by entangling qubits, enabling exceptionally fast pattern recognition and structural assessment. This work addresses the limitations of conventional TDA methods, specifically concerning computational complexity, by capitalizing on the unique properties of quantum mechanics to significantly enhance identification speed and accuracy. The proposed algorithm promises a paradigm shift in the field, offering substantial improvements in the analysis of intricate topological structures found in diverse datasets, including but not limited to genomic data, materials science simulations, and network analysis. This research investigates the practical implementation of the Q-TDA algorithm, demonstrating its potential for transformative applications across various scientific disciplines.

DOI: 10.5281/zenodo.22155835

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