Dynamic Topological Optimization Algorithm Quantum Accelerator

Jincheng Zhang

Published Sep 5, 2026• 0 citations

Abstract

This paper presents a novel dynamic topological optimization algorithm designed to accelerate the optimization process through the utilization of quantum accelerators. Traditional optimization algorithms often rely on manual design, limiting their scalability and efficiency. This work introduces a quantum-enhanced approach, leveraging quantum phenomena to significantly improve computational speed and explore complex solution spaces. The core mechanism of this algorithm involves dynamically adjusting parameters within a topological framework to guide the search process, resulting in enhanced optimization capabilities. This research explores the potential of quantum acceleration in tackling complex optimization problems across diverse fields, including materials science, robotics, and machine learning. The paper details the algorithm's architecture, key operational steps, and preliminary results demonstrating substantial improvements in computational efficiency compared to conventional methods.

DOI: 10.5281/zenodo.22315431

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