Implementing Hybrid Quantum Neural Networks for Accurate Wildfire Detection

This study presents a pioneering approach to wildfire prediction through a Hybrid Quantum-Classical Neural Network (HQCNN), addressing the critical need for more accurate wildfire detection amid rising wildfire frequency and intensity. Traditional machine learning techniques, while effective, often encounter limitations like overfitting due to high computational demands. By incorporating a variational quantum circuit into a hybrid framework, the HQCNN leverages quantum mechanics to enhance feature extraction, achieving superior computational efficiency and predictive performance. Our model attained a testing accuracy of 95.50%, significantly outperforming the 88.43% achieved by a purely classical model. This marked improvement in accuracy, alongside reduced training time, highlights the transformative potential of quantum-enhanced models for environmental applications. Our results underscore the strategic advantage of quantum integration in machine learning tasks, setting a new benchmark for wildfire prediction and demonstrating the broader implications of quantum approaches for complex classification challenges.