Title: Quantum Entanglement-Based Adaptive Geometry Modeling

Jincheng Zhang

Published Sep 2, 2026• 0 citations

Abstract

This paper introduces a novel approach to geometry modeling based on quantum entanglement. Traditional geometric modeling methods often rely on static, fixed topologies, while this research explores the utilization of entanglement to optimize geometric structures and enhance algorithm efficiency. The core mechanism involves exploiting the correlated properties of entangled particles to dynamically adjust and refine geometric parameters, leading to improved computational performance. This work investigates the potential of entanglement to achieve higher precision and faster convergence in geometric optimization tasks. The theoretical framework and preliminary results demonstrate the effectiveness of this approach in solving specific geometric modeling problems.

DOI: 10.5281/zenodo.22244191

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