Stabilized classification control using multi-stage quantum convolutional neural networks for autonomous driving

Real-time processing with high classification accuracy is a fundamental requirement in autonomous driving systems. However, existing neural network models for classification often face a tradeoff between computational efficiency and accuracy, necessitating the development of advanced optimization methods to address this limitation. Additionally, dynamic driving environments offer opportunities to enhance classification performance by leveraging the principles of quantum computing, particularly the properties of superposition and entanglement. In response to these challenges, a multi-stage quantum convolutional neural network (MS-QCNN) approach is proposed, designed to improve image analysis performance by effectively utilizing the multi-stage structure of QCNN. A Lyapunov optimization framework is applied to achieve optimal performance, which maximizes time-averaged efficiency while ensuring system stability. This framework dynamically adjusts the MS-QCNN model in response to environmental variations, promoting enhanced queue stability and achieving optimal time-averaged performance.