Quantum Graph Convolutional Networks: Implementation and Trainability Analysis
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 performa...
| Authors | Paul San Sebastian Sein, Theodor Iosif, Tilen G. Limbäck-Stokin et al. |
|---|---|
| Primary category | quant-ph |
| Published | 17 September 2026 |
| Last updated at source | 17 September 2026 |
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