Quantum Machine Learning for Dynamic Topology Optimization

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

Quantum Machine Learning (QML) offers a paradigm shift in machine learning, potentially enabling solutions beyond the reach of classical approaches. This paper investigates the application of QML to dynamic topology optimization, a complex optimization problem with significant real-world implications. We propose a novel QML-based algorithm, termed "Adaptive Topology Weaver" (ATW), that dynamically adjusts the parameters of a machine learning model during the optimization process. This adaptive parameter adjustment is driven by quantum algorithms, leading to more efficient and self-adaptive models. The core mechanism of ATW involves iteratively refining a learned representation of the optimization landscape through quantum-enhanced exploration and exploitation. We demonstrate the effectiveness of ATW through simulations, showcasing improvements in convergence speed and model accuracy compared to traditional optimization methods. The results highlight the potential of QML to unlock novel solutions for challenging optimization problems.

DOI: 10.5281/zenodo.22489303

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