Title: Quantum-Enhanced Topological Data Analysis – Speeding Up Pattern Recognition

Jincheng Zhang

Published Sep 2, 2026• 0 citations

Abstract

This paper introduces a novel quantum algorithm for topological data analysis (TDA) designed to significantly accelerate the identification of complex patterns within large datasets. Traditional TDA methods often struggle with the inherent computational complexity of exploring high-dimensional data, particularly when dealing with subtle correlations. This work proposes a paradigm shift utilizing quantum superposition and entanglement to overcome these limitations. Quantum mechanics offers the potential for fundamentally altering the landscape of pattern recognition, allowing for the detection of previously imperceptible correlations. This paper details the core mechanism of the proposed algorithm, its potential benefits, and preliminary experimental results demonstrating its efficacy. The focus is on achieving substantial speedups compared to existing classical methods, offering a promising path towards more efficient and insightful pattern discovery.

DOI: 10.5281/zenodo.22250726

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