Quantum-Classical Reservoir Computing to Predict Influenza H3N2 Antigenic Distance

Abstract

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, R2). On the larger and more challenging 2003-2025 dataset, FluQRC achieves MAE = 0.369, RMSE = 0.635, and R2 = 0.900, corresponding to a 20.3% reduction in MAE and a 14.3% reduction in RMSE relative to the strongest baseline, while raising R2 from 0.862 to 0.900. These results demonstrate the scalability and effectiveness of FluQRC for large-scale antigenic distance prediction.