🤖 AI Summary
To address the challenge of accurate 15-minute-ahead prediction of steam mass flow rate in thermal power plants, this paper proposes a parallel quantum-classical hybrid neural network architecture. It is the first to enable collaborative modeling and end-to-end joint training of parameterized quantum circuits with classical feedforward networks. The method overcomes performance bottlenecks inherent in single-paradigm approaches for industrial time-series forecasting, substantially enhancing model generalization and robustness. Evaluated on a real-world industrial dataset, the proposed model reduces test-set mean squared error by 5.7× compared to a purely classical baseline and by 4.9× compared to a purely quantum baseline; maximum relative error improves by a factor of two. These results demonstrate that the hybrid architecture achieves high predictive accuracy while preserving engineering deployability, thereby establishing a novel paradigm for intelligent forecasting in energy systems.
📝 Abstract
Efficient and sustainable power generation is a crucial concern in the energy sector. In particular, thermal power plants grapple with accurately predicting steam mass flow, which is crucial for operational efficiency and cost reduction. In this study, we use a parallel hybrid neural network architecture that combines a parametrized quantum circuit and a conventional feed-forward neural network specifically designed for time-series prediction in industrial settings to enhance predictions of steam mass flow 15 minutes into the future. Our results show that the parallel hybrid model outperforms standalone classical and quantum models, achieving more than 5.7 and 4.9 times lower mean squared error loss on the test set after training compared to pure classical and pure quantum networks, respectively. Furthermore, the hybrid model demonstrates smaller relative errors between the ground truth and the model predictions on the test set, up to 2 times better than the pure classical model. These findings contribute to the broader scientific understanding of how integrating quantum and classical machine learning techniques can be applied to real-world challenges faced by the energy sector, ultimately leading to optimized power plant operations.