基于量子纠缠的非线性几何拓扑结构分析与预测

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper investigates the application of quantum entanglement to the analysis of non-linear geometric topology, exploring the potential for predictive modeling and optimization through the integration of quantum machine learning. We outline a novel framework centered on the unique properties of quantum entanglement, proposing a method to characterize and manipulate geometric structures with unprecedented precision. The core mechanism involves leveraging entangled states to represent and analyze complex topological landscapes. The paper presents preliminary results demonstrating the effectiveness of this approach in predicting and optimizing geometric configurations, highlighting its potential to advance our understanding of these intricate structures. Furthermore, we discuss the challenges and future directions of this research, emphasizing the crucial role of quantum entanglement in achieving robust and accurate predictions.

DOI: 10.5281/zenodo.22189587

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