标题： 用于剩余有用寿命预测的混合量子循环神经网络 显示英文标题

标题： Hybrid Quantum Recurrent Neural Network For Remaining Useful Life Prediction

摘要： 航空航天领域的预测性维护高度依赖于对喷气发动机剩余使用寿命的准确估计。 本文介绍了一种混合量子循环神经网络框架，结合了量子长短期记忆层和经典密集层，用于NASA商用模块化航空推进系统仿真数据集的剩余使用寿命预测。 每个量子长短期记忆门用量子深度注入电路取代传统的线性变换，使网络能够更有效地学习高频成分。 实验结果表明，尽管可训练参数较少，但混合量子循环神经网络在均方根误差和平均绝对误差方面比基于堆叠长短期记忆层的循环神经网络提高了多达5%。 此外，与随机森林、卷积神经网络和多层感知器等已建立的技术进行的全面比较表明，我们的方法达到了15.46的均方根误差，分别超过了这些基线约13.68%、16.21%和7.87%。 然而，它仍逊色于某些先进的联合架构。 我们的研究结果强调了在有限数据条件下，混合量子-经典方法在时间序列预测中的潜力，为提高预测性维护任务的可靠性提供了新的途径。 显示英文摘要

摘要： 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.