Hybrid Quantum Neural Network Model with Catalyst Experimental Validation: Application for the Dry Reforming of Methane

Abstract

Machine learning (ML), which has been increasingly applied to complex problems such as catalyst development, encounters challenges in data collection and structuring. Quantum neural networks (QNNs) outperform classical ML models, such as artificial neural networks (ANNs), in prediction accuracy, even with limited data. However, QNNs have limited available qubits. To address this issue, we introduce a hybrid QNN model, combining a parametrized quantum circuit with an ANN structure. We used the catalyst data sets of the dry reforming of methane reaction from the literature and in-house experimental results to compare the hybrid QNN and the ANN models. The hybrid QNN exhibited superior prediction accuracy and a faster convergence rate, achieving an R 2 of 0.942 at 2478 epochs, whereas the ANN achieved an R 2 of 0.935 at 3175 epochs. For the 224 in-house experimental data points previously unreported in the literature, the hybrid QNN exhibited an enhanced generalization performance. It showed a mean absolute error (MAE) of 13.42, compared with an MAE of 27.40 for the ANN under similar training conditions. This study highlights the potential of the hybrid QNN as a powerful tool for solving complex problems in catalysis and chemistry, demonstrating its advantages over classical ML models.

Machine Learning-Based Adaptive Regression to Identify Nonlinear Dynamics of Biochemical Systems: A Case Study on Bio 2,3-Butanediol Distillation Process

“…Subset regression and sparse identification approaches focusing on this research are effective in capturing the dynamics of the nonlinear system but have the limitation of being indirect. To address this, more state-of-the-art and direct modeling approaches have been proposed, with two major methods being Physics-Informed Neural Networks (PINNs) − and hybrid modeling − in various research areas. PINNs utilize neural networks to incorporate physical laws directly into the model.…”