Adaptive Topology Optimization via Quantum Computation

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

This paper explores the application of quantum computation to enhance topology optimization, specifically addressing the challenges of dynamically adapting parameters based on real-time feedback. Traditional topology optimization methods often rely on fixed parameters, limiting their ability to efficiently explore complex design spaces. We propose a novel framework that utilizes quantum computation to dynamically adjust optimization parameters, allowing for more responsive and effective design iterations. This work introduces a quantum-enhanced optimization algorithm that adapts to feedback signals, resulting in improved design quality and reduced computational cost. The core mechanism involves quantum-enhanced parameter adjustments driven by real-time data, offering a significant leap forward in the field of topology optimization. The paper details the theoretical foundation, implementation of the algorithm, and preliminary results demonstrating the benefits of this approach.

DOI: 10.5281/zenodo.22455463

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