Feature Fusion‐Based Hybrid Quantum‐Classical Graph Residual Neural Network

ABSTRACT In the node classification task on graphs, mainstream graph neural networks often adopt the same neighborhood aggregation strategy for all nodes. This approach results in biased learning toward features from the majority nodes while devaluing those from the minority nodes under class imbalance. In particular, for marginal nodes, limited neighboring information can severely impact classification performance. To address this challenge, this paper proposes a feature fusion‐based hybrid quantum‐classical graph residual neural network (QGRNN). Leveraging the nonlinear expressive capacity of qubits in modeling complex feature interactions, the model innovatively integrates a structure‐driven node selection mechanism with a quantum feature enhancement module, while also dynamically fusing classical features and Hamiltonian expectation values through a gated residual fusion mechanism to compensate for representational deficiencies of marginal nodes overlooked by classical methods. Experimental results show that QGRNN consistently outperforms baselines across a range of node classification tasks. In binary and ternary classification settings, it exhibits strong discriminative capability and robustness, especially maintaining high accuracy under severe class imbalance. In addition, QGRNN also demonstrates strong generality in other tasks, in recommendation scenarios, achieving an average improvement of 36.3% on NDCG@5, 44.3% on Recall@5, and 21.9% on Recall@10 compared to the baseline.