Dimension Geometry Optimization and Quantum Entanglement Correlation

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

This paper explores the application of quantum entanglement to dimension geometry optimization, aiming to enhance local optimization efficiency and facilitate more effective geometric modeling and algorithms within quantum computing. We introduce a novel quantum geometry optimization method leveraging a quantum state field calculation model and a hybrid optimization algorithm, designed to mitigate the pitfalls of gradient vanishing and exploding. This approach offers a fundamentally different perspective on geometric problem-solving, potentially unlocking new capabilities in complex geometric modeling. The core claim is that this quantum entanglement-based method significantly improves the efficiency of localized optimization, and allows for quantum computation to better model and optimize geometric structures. We detail the methodology, including the mathematical framework and initial experimental results demonstrating the enhanced performance. The paper concludes with a discussion of future research directions and potential impact on various geometric modeling applications.

DOI: 10.5281/zenodo.22278787

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