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Neurocomputing

Enhancing graph learning with quantum structural encoding

Yu Lei, Gaoxiang Chen, Hongyang Chen, Jingsong Lv, Linyao Yang

Published Nov 8, 2025• 1 citation
Publisher
DOI
10.1016/j.neucom.2025.132054

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Quantum graph neural networks (QGNNs) have shown promise in leveraging quantum computing to enhance graph-structured data learning, yet they are typically limited to homogeneous graphs. In this paper, we introduce the quantum heterogeneous graph neural network (QHGNN), which integrates heterogeneity as an inductive bias within quantum circuits, enabling effective learning on heterogeneous graphs. By encoding diverse node and edge types directly into the quantum circuit architecture, QHGNN can capture complex relationships intrinsic to heterogeneous molecular data. Our experiments utilize PTC datasets to assess the performance of QHGNN in molecular property prediction tasks, showcasing superior prediction accuracy and efficiency, along with enhanced noise resilience when compared to existing QGNN models.