Real-Time Situational Awareness at National Borders: A Comparative Analysis of Classical and Hybrid QCNN Approaches

Situational awareness at national borders is critical for maintaining security and enabling timely responses to potential threats. In this study, we explore and compare the performance of Classical Convolutional Neural Networks (CNNs) and Hybrid Quantum Convolutional Neural Networks (QCNNs) for real-time alert systems. The primary objective is to identify the algorithm that can deliver the fastest alerts, prioritizing speed over accuracy to ensure quick decision-making and action. By taking advantage of the computational advantages of quantum computing, we aim to determine whether QCNNs can outperform classical CNNs in scenarios where rapid response is paramount. Experimental results demonstrate the potential of both methodologies, with a detailed analysis of their speed performance, offering valuable insights for the deployment of efficient situational awareness systems at national borders.