基于量子机器学习的图神经网络

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper investigates the application of quantum machine learning to graph neural networks (GNNs), specifically focusing on the design and evaluation of a novel quantum-enhanced GNN architecture. We propose a quantum-inspired neural network architecture that leverages the inherent quantum properties of entangled states and superposition to improve the representation capacity and computational efficiency of GNNs. The core mechanism centers around simulating graph structures using quantum algorithms and incorporating quantum machine learning techniques to enhance the network's ability to capture complex relationships within the data. This work introduces a framework for leveraging quantum mechanics to boost the performance of GNNs, potentially offering a significant advancement over traditional approaches. The paper presents initial results demonstrating the efficacy of this quantum-enhanced GNN architecture in a benchmark dataset, highlighting the potential for improved representation and faster training times.

DOI: 10.5281/zenodo.22195473

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