Hybrid Quantum Recurrent Neural Network For Remaining Useful Life Prediction
This work addresses predictive maintenance for aerospace engineers by proposing a hybrid quantum-classical approach, though it is incremental as it builds on existing quantum and classical neural network techniques.
The paper tackles remaining useful life prediction for jet engines in aerospace predictive maintenance by introducing a Hybrid Quantum Recurrent Neural Network that combines Quantum Long Short-Term Memory layers with classical dense layers, achieving up to a 5% improvement in mean root mean squared error and mean absolute error over a stacked Long Short-Term Memory Recurrent Neural Network and surpassing Random Forest, Convolutional Neural Network, and Multilayer Perceptron baselines by approximately 13.68%, 16.21%, and 7.87%, respectively, with a Root Mean Squared Error of 15.46.
Predictive maintenance in aerospace heavily relies on accurate estimation of the remaining useful life of jet engines. In this paper, we introduce a Hybrid Quantum Recurrent Neural Network framework, combining Quantum Long Short-Term Memory layers with classical dense layers for Remaining Useful Life forecasting on NASA's Commercial Modular Aero-Propulsion System Simulation dataset. Each Quantum Long Short-Term Memory gate replaces conventional linear transformations with Quantum Depth-Infused circuits, allowing the network to learn high-frequency components more effectively. Experimental results demonstrate that, despite having fewer trainable parameters, the Hybrid Quantum Recurrent Neural Network achieves up to a 5% improvement over a Recurrent Neural Network based on stacked Long Short-Term Memory layers in terms of mean root mean squared error and mean absolute error. Moreover, a thorough comparison of our method with established techniques, including Random Forest, Convolutional Neural Network, and Multilayer Perceptron, demonstrates that our approach, which achieves a Root Mean Squared Error of 15.46, surpasses these baselines by approximately 13.68%, 16.21%, and 7.87%, respectively. Nevertheless, it remains outperformed by certain advanced joint architectures. Our findings highlight the potential of hybrid quantum-classical approaches for robust time-series forecasting under limited data conditions, offering new avenues for enhancing reliability in predictive maintenance tasks.