Benefiting from quantum? A comparative study of Q-Seg, quantum-inspired techniques, and U-Net for crack segmentation

Exploring the potential of quantum hardware for en hancing classical and real-world applications is an ongoing challenge. This study evaluates the performance of quantum and quantum-inspired methods compared to classical mod els for crack segmentation. Using annotated gray-scale im age patches of concrete samples, we benchmark a classical mean Gaussian mixture technique, a quantum-inspired fermion based method, Q-Seg a quantum annealing-based method, and a U-Net deep learning architecture. Our results indicate that quantum-inspired and quantum methods offer a promising alternative for image segmentation, particularly for complex crack patterns, and could be applied in near-future applications.