Quantum Entanglement-Driven Adaptive Geometry Optimization

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

Quantum entanglement, a cornerstone of quantum mechanics, offers a potentially revolutionary approach to optimization. This paper investigates the application of quantum entanglement to design an adaptive geometry optimization algorithm, capable of navigating complex geometric structures and optimizing problems towards desired outcomes. We explore the core mechanism by leveraging entanglement's unique properties to create a dynamically adjusted state space, thereby facilitating novel solutions compared to classical optimization methods. The paper details the theoretical framework, initial implementation, and preliminary results demonstrating the algorithm's efficacy in a specific geometric optimization scenario. The core claim is that this approach yields superior performance in complex geometries, offering a departure from conventional optimization techniques.

DOI: 10.5281/zenodo.22190056

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