Quantum-Driven Topological Data Analysis for Sparse Data

Jincheng Zhang

Published Sep 1, 2026• 0 citations

Abstract

Quantum-driven Topological Data Analysis (Q-TDA) offers a fundamentally different approach to analyzing sparse data compared to traditional methods. This paper investigates the potential of leveraging quantum mechanics to identify patterns and relationships within these datasets, with the goal of significantly reducing the computational burden associated with data preprocessing and enhancing the detection of hidden structure. We introduce a novel quantum algorithm designed to efficiently explore topological spaces, which are inherently structured and provide a powerful framework for understanding data. This work explores the theoretical foundations and preliminary implementation of this quantum approach, highlighting its potential to revolutionize the field of topological data analysis. The core claim is that Q-TDA can achieve superior performance and efficiency for sparse data, offering a paradigm shift in the analysis of complex datasets.

DOI: 10.5281/zenodo.22219094

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