Quantum-Classical Reservoir Computing to Predict Influenza A/H3N2 Antigenic Distance

Accurate prediction of antigenic distance between influenza A/H3N2 strains is essential for timely vaccine strain selection, yet traditional hemagglutination inhibition (HI) assays are labour-intensive and limited in throughput. We present FluQRC, a hybrid Quantum-Classical Reservoir Computing framework for sequence-based antigenic distance prediction. FluQRC integrates three novel components: (1) a differentiable gated property ranking network for data-driven property selection, (2) a dimensionality reduction network that compresses the feature representation into a form suitable for quantum processing, and (3) a hybrid quantum-classical reservoir computing architecture for antigenic distance prediction. Experiments on two datasets covering 1963–2002 (271 strains, 73,441 pairs) and 2003–2025 (888 strains, 788,544 pairs) show that FluQRC outperforms four established baselines across all three evaluation metrics (MAE, RMSE, R ² ). On the larger and more challenging 2003–2025 dataset, FluQRC achieves MAE = 0.369, RMSE = 0.635, and R ² = 0.900, corresponding to a 20.3% reduction in MAE and a 14.3% reduction in RMSE relative to the strongest baseline, while raising R ² from 0.862 to 0.900. These results demonstrate the scalability and effectiveness of FluQRC for large-scale antigenic distance prediction.