##基于量子纠缠的几何形状预测

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

This paper introduces a novel approach to geometric shape prediction leveraging the unique properties of quantum entanglement. Traditional methods often rely on computationally intensive simulations, limiting accuracy and scalability. We propose a framework utilizing entangled quantum states to represent and manipulate geometric shapes, allowing for predictive modeling with enhanced stability and precision. The core mechanism centers on exploiting the non-classical correlations inherent in entangled systems to uncover subtle geometric patterns that classical algorithms miss. This investigation demonstrates the potential of quantum entanglement to significantly improve prediction accuracy compared to existing methods, offering a paradigm shift in geometric shape analysis. The research explores the fundamental implications of this approach and outlines future directions for quantum computing and geometric modeling.

DOI: 10.5281/zenodo.22525001

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