What question did this study set out to answer?

This research aims to compare classical and hybrid quantum-classical models for nonlinear dynamical systems.

June 4, 20260 citationsOpen Access

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

KMKameswari ManthaVellore Institute of Technology University

Key Points

This research aims to compare classical and hybrid quantum-classical models for nonlinear dynamical systems.
Utilized synthetic reactor dataset for analysis.
Evaluated GRU, LSTM, Transformer, and Variational Quantum Circuit architectures under the same training conditions.
Assessed model performance using RMSE, MAE, and R² metrics.
Recurrent architectures (GRU and LSTM) showed superior performance over Transformer and VQC on the task.
Memory-based models effectively predicted nonlinear time-series behaviors.
Current quantum methods are limited in complex regression applications.

Abstract

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.

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