Hybrid Classical–Quantum Surrogate Modeling for Nonlinear Reactor Dynamics: A Comparative Study of GRU, LSTM, Transformer, and Variational Quantum Circuits

This preprint presents a comparative study of classical recurrent neural networks and hybrid quantum-classical models for surrogate modeling of nonlinear dynamical systems. A synthetic reactor dataset exhibiting coupled multi-frequency behavior is used to evaluate GRU, LSTM, Transformer, and Variational Quantum Circuit (VQC) architectures under identical training conditions. Model performance is assessed using RMSE, MAE, and R² metrics. Results show that recurrent architectures outperform both Transformer and VQC approaches on this task, highlighting the effectiveness of memory-based models for nonlinear time-series prediction and the current limitations of near-term quantum machine learning methods in complex regression settings. The study provides insights into the applicability of hybrid quantum-classical approaches for scientific surrogate modeling and time-series forecasting.