基于量子纠缠的自适应图神经网络

Jincheng Zhang

Published Sep 1, 2026• 0 citations

Abstract

This paper introduces a novel neural network architecture, termed a Quantum Entanglement-Aware Graph Neural Network (QEA-GNN), which leverages the principles of quantum entanglement to enhance the training process of graph neural networks. The QEA-GNN utilizes entanglement to dynamically adjust the network's parameters, leading to improved model performance and faster convergence. We explore the potential of this approach by demonstrating its effectiveness in a series of benchmark datasets. The core mechanism focuses on establishing entangled representations between nodes, allowing the network to learn more robust and informative features. The paper presents a detailed analysis of the algorithm's design, implementation, and performance evaluation. We conclude that the QEA-GNN offers a promising avenue for advancing graph neural network training through the incorporation of quantum entanglement.

DOI: 10.5281/zenodo.22225469

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