<ul><li>A versatile Quantum Graph Neural Network (QGNN) framework is proposed Integrating established techniques for inductive representation learning on graphs with parametrized quantum convolutional and pooling layers Benchmarked on a node regression task with the QM9 dataset, achieving performance comparable to classical Graph Neural Networks (GNNs) The quantum approach exhibits robust generalization across molecules with varying numbers of atoms, outperforming classical GNNs The QGNN framework demonstrates scalability without barren plateaus as the number of qubits increases</li></ul>

Inductive Graph Representation Learning with Quantum Graph Neural Networks

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