Quantum Entanglement-Based Adaptive Geometry Structural Optimization

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

Quantum entanglement offers a fundamentally different approach to optimization compared to classical algorithms, potentially enabling the discovery of novel and efficient solutions. This paper investigates the application of quantum entanglement to adaptive geometry structural optimization, a task where complex geometric shapes are modeled and optimized. We propose a novel algorithm leveraging entangled states to guide the exploration of the solution space, minimizing the need for explicit parameter tuning and promoting the discovery of optimal geometric structures. We demonstrate the effectiveness of this approach through a case study focusing on the design and optimization of a fractal-like shape. The core mechanism centers around the generation and manipulation of entangled quantum states to represent geometric configurations, allowing for efficient exploration and refinement of solutions. We explore the potential of this approach to address challenges in complex geometry modeling and optimization, highlighting its promise for advancing the field of structural design and artificial intelligence.

DOI: 10.5281/zenodo.22195155

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