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Quantum Implementation of Dynamic Topology Optimization

Jincheng Zhang

Published Sep 6, 2026• 0 citations
Publisher

Abstract

Dynamic Topology Optimization (DTO) is a powerful optimization technique used in various fields, including materials science, biology, and engineering. It involves iteratively refining a topology by adding or removing elements based on a cost function. Traditional DTO methods often rely on complex algorithms and require significant human intervention. This paper presents a novel quantum implementation of DTO, leveraging the unique properties of quantum mechanics, specifically quantum entanglement and quantum gates, to significantly enhance the optimization process. We explore the design of a quantum circuit that efficiently transforms the problem into a quantum representation, utilizing entanglement for enhanced exploration and quantum gates for precise adjustments to the topology. The proposed algorithm offers improved efficiency, better predictability, and the potential for solving complex topology problems more effectively than traditional methods. The core mechanism involves utilizing quantum entanglement to facilitate the exploration of multiple possible topology configurations simultaneously, leading to a more focused search and faster convergence. The research focuses on building a quantum-enhanced DTO framework, demonstrating its potential to revolutionize the field.

DOI
10.5281/zenodo.22454067

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This paper introduces a novel dynamic topology-based quantum topology optimization algorithm. Traditional optimization methods rely on classical calculations, while this approach leverages quantum spin states to dynamically optimize topology parameters, offering enhanced efficiency and potentially new insights into quantum topology. We propose a framework wherein the quantum spin state serves as a dynamic feedback mechanism, guiding the optimization process towards optimal topology configurations. The algorithm utilizes a quantum circuit to map the current topology parameters to a new set of parameters, effectively exploring the topology space with a quantum twist. The core mechanism involves a carefully designed quantum circuit that iteratively refines the topology based on feedback from the spin state, leading to improved optimization results compared to existing techniques. This research explores the potential of harnessing quantum properties to accelerate and refine the process of topology optimization.
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