Nonlinear Geometric Structure Prediction Algorithm

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

This paper introduces a novel algorithmic framework for predicting the stability of complex geometric structures based on a quantum entanglement-driven model. Traditional geometric prediction methods often struggle with high-dimensional complexity and require computationally intensive numerical simulations. Our approach leverages the unique properties of quantum entanglement to transform geometric prediction into a probabilistic quantum computation, significantly enhancing prediction accuracy and efficiency. The core mechanism involves introducing a probability model representing the geometric structure, utilizing quantum state propagation to refine the prediction, and employing optimization techniques to achieve precise results. This algorithm represents a significant advancement over existing methods, offering a fundamentally different approach to geometric analysis.

DOI: 10.5281/zenodo.22291148

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