Quantum-Classical Reservoir Computing to Predict Influenza H3N2 Antigenic Distance

Authors

Khalaj, M., Jin, L., Rayan, S.

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. On two datasets spanning 1963-2002 (271 strains, 73,441 pairs) and 2003-2025 (888 strains, 788,544 pairs), FluQRC is competitive with four established baselines on the historical set and outperforms all of them on the larger, more challenging 2003-2025 set, where it attains MAE = 0.369, RMSE = 0.635, and R2 = 0.900 - a 20.3% reduction in MAE and 14.3% reduction in RMSE over the strongest baseline, raising R2 from 0.862 to 0.900. These results demonstrate the scalability and effectiveness of FluQRC for large-scale antigenic distance prediction.

Preprint server: bioRxiv
The authors list and abstract were imported from bioRxiv on 27 Jul 2026.

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