Representational efficiency and noise robustness in hybrid quantum-classical graph neural networks

A central question in hybrid quantum–classical learning is whether variational quantum circuits (VQCs) preserve task-relevant information more efficiently than classical nonlinearities of equal output dimension. We study this question for drug–target interaction prediction in Alzheimer’s disease drug-repurposing workflows using a Hybrid Quantum–Classical Graph Neural Network (HQGNN). The model compresses 512-dimensional GNN embeddings into a four-qubit VQC (128 $$\times $$ compression) before a classical prediction head. On the DAVIS kinase benchmark, HQGNN achieves concordance index (CI) $$=0.527$$ and root mean squared error (RMSE) $$=0.850$$ , outperforming a dimension-matched classical bottleneck baseline (CI $$=0.500$$ ; random ranking). Under the same compression, the quantum layer recovers 51% of the ranking signal lost by compression relative to the full classical model and yields a 5.4% relative CI gain over the classical bottleneck. This effect is obtained with 24 trainable quantum parameters (representational yield $$\Delta $$ CI/parameter $$\approx 1.13\times 10^{-3}$$ ). Under depolarizing noise, RMSE degradation remains below 3.3% at realistic noisy intermediate-scale quantum (NISQ) levels ( $$p \le 0.01$$ ). Because the present benchmark is evaluated on a single transductive split, these results are interpreted as controlled proof-of-concept evidence of representational efficiency rather than definitive deployment-level generalization.