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Quantum Annealing Algorithm for Probabilistic Topological Optimization

Jincheng Zhang

Published Sep 5, 2026• 0 citations
Publisher

Abstract

This paper investigates the application of quantum annealing algorithms to the field of topological optimization, specifically focusing on a probabilistic approach. Traditional topological optimization relies on iterative refinement of a mesh, often computationally expensive. We propose a novel method leveraging the quantum annealing algorithm's inherent probabilistic search capabilities, combined with topological structural information, to achieve more efficient solutions for complex optimization problems. The core concept involves translating the optimization problem into a search problem within the space of possible topological structures. The quantum annealing algorithm then explores this space probabilistically, guided by the topological constraints, with the goal of identifying the optimal topology. This approach aims to reduce the computational burden associated with conventional iterative methods and potentially unlock solutions that are inaccessible through classical optimization techniques. The presented framework is theoretically grounded and offers a promising avenue for future research in hybrid quantum-classical optimization. The key performance indicator for this approach is the convergence rate and the quality of the solutions obtained compared to traditional methods. The algorithm's success depends heavily on the effective encoding of the problem's constraints into the topology search space.

DOI
10.5281/zenodo.22333851

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