Quantum Graph Convolutional Networks: Implementation and Trainability Analysis

Abstract: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.

Quantum Physics (quant-ph); Machine Learning (cs.LG)

arXiv:2609.19983 [quant-ph]

(or arXiv:2609.19983v1 [quant-ph] for this version)

https://doi.org/10.48550/arXiv.2609.19983 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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