Quantum Reservoir Computing with Physics-Informed Correction for Reduced-Order PDE Forecasting

• URL: http://arxiv.org/abs/2608.23119v1
• Date: Mon, 24 Aug 2026 11:28:49 GMT
• Title: Quantum Reservoir Computing with Physics-Informed Correction for Reduced-Order PDE Forecasting
• Abstract summary: We study a hybrid proposal--correction architecture for reduced-order PDE forecasting in which a pure-state quantum reservoir computer (QRC) predicts latent coefficient dynamics and a PINN-based physics-informed corrector (PIC) refines local rollout windows.<n>Results suggest that QRC proposals with local physics-informed correction are a viable benchmark-dependent reduced-order forecasting strategy.
• Score: 0.030586855806896046
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We study a hybrid proposal--correction architecture for reduced-order PDE forecasting in which a pure-state quantum reservoir computer (QRC) predicts latent coefficient dynamics and a PINN-based physics-informed corrector (PIC) refines local rollout windows. The method is evaluated on Burgers and Kuramoto--Sivashinsky (KS), with KS as the primary chaotic benchmark. On KS, QRC+PIC consistently improves over QRC alone in RMSE, NRMSE, and PDE residual, while Burgers highlights a regime in which simple baselines remain strong. These results suggest that QRC proposals with local physics-informed correction are a viable benchmark-dependent reduced-order forecasting strategy.

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