Quantum-Inspired Algorithms for Topological Data Analysis

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

Quantum-inspired algorithms are emerging as a promising approach to analyze complex topological data, offering potential advantages over classical methods. This paper explores the design of such algorithms inspired by quantum mechanics, aiming to enhance robustness and scalability through novel representations and processing techniques. We propose a framework centered on quantum state representations, demonstrating their efficacy in identifying intricate patterns within topological data. The core mechanism hinges on leveraging quantum properties to facilitate enhanced analysis and insight extraction. The research presents a preliminary investigation into this approach, highlighting potential improvements in data comprehension and robustness.

DOI: 10.5281/zenodo.22186499

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