Quantum-Guided Optimization for Topological Data Analysis

Jincheng Zhang

Published Sep 5, 2026• 0 citations

Abstract

This paper explores the utilization of quantum annealing to enhance optimization within complex topological data analysis (TDA) algorithms. TDA algorithms, particularly those involving intricate network structures and parameter tuning, often suffer from computationally intensive optimization challenges. Quantum annealing offers a potential paradigm shift by leveraging quantum mechanics to accelerate the search for optimal solutions, leading to faster analysis and improved results. We detail a novel approach that integrates quantum annealing with existing TDA methodologies, demonstrating a significant improvement in the speed and accuracy of key analysis steps. The core claim is that applying quantum annealing to guide optimization within complex topological data analysis algorithms will accelerate the process.

DOI: 10.5281/zenodo.22321894

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