量子图卷积网络：实现与可训练性分析

中文摘要 AI 辅助

图神经网络（GNNs）在图结构数据上取得了最先进的性能，但在大型图上的训练和推理常常受到内存限制和稀疏线性代数工作负载的瓶颈制约。量子计算提供了一套替代原语，可能提高图学习的可扩展性。基于Liao等人提出的量子图神经网络（QGNN）框架，本工作实现了两种代表性架构——简化图卷积（SGC）和线性图卷积（LGC）模型——并使用量子模拟在开放基准图数据集和半监督学习任务上对它们进行了评估。我们将预测性能和优化行为与经典基线进行比较，表明量子模型以更少的参数实现了有竞争力的性能。最后，我们提出了一种成本梯度分析，以识别所展示模型可训练的任务。随后进行了一项经典可模拟性研究，以找出所提出电路在训练过程中保持鲁棒性的区间。

英文摘要

Graph Neural Networks (GNNs) achieve state-of-the-art performance on graph-structured data, but training and inference on large graphs are often bottlenecked by memory constraints and sparse linear-algebra workloads. Quantum computing offers an alternative set of primitives that may improve scalability for graph learning. Building on the quantum graph neural network (QGNN) framework of Liao \textit{et al.}, this work implements two representative architectures --- the Simplified Graph Convolution (SGC) and Linear Graph Convolution (LGC) models --- and evaluates them on open benchmark graph datasets and semi-supervised learning tasks using quantum simulation. We compare predictive performance and optimization behavior against classical baselines, showing that the quantum models achieve competitive performance with fewer parameters. Finally, we present a cost gradient analysis that identifies the tasks for which the models showcased are trainable. This is followed by a classical simulability study to find regimes in which the proposed circuits remain robust during training.

发表机构

• Ikerlan Technology Research Centre（伊克兰技术研究中心）
• Basque Research and Technology Alliance (BRTA)（巴斯克研究与技术联盟（BRTA））
• University of the Basque Country/Euskal Herriko Unibertsitatea-EHU（巴斯克大学/EHU）
• University College London（伦敦大学学院）
• London Centre for Nanotechnology（伦敦纳米技术中心）
• University of Technology Sydney（悉尼科技大学）
• Sydney Quantum Academy（悉尼量子学院）
• Laboratoire d’Informatique de Paris 6（巴黎第六信息实验室）
• CNRS（法国国家科学研究中心）
• Sorbonne Université（索邦大学）

机构由 AI 辅助整理，请以论文原文为准。