Srinivasan, AkshayaAkshayaSrinivasanGeng, AlexanderAlexanderGengMacaluso, AntonioAntonioMacalusoKiefer-Emmanouilidis, MaximilianMaximilianKiefer-EmmanouilidisMoghiseh, AliAliMoghiseh2025-06-102025-06-102025-04-15https://publica.fraunhofer.de/handle/publica/48846810.1515/teme-2025-0017Exploring the potential of quantum hardware for enhancing classical and real-world applications is an ongoing challenge. This study evaluates the performance of quantum and quantum-inspired methods compared to classical models for crack segmentation. Using annotated grayscale image 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 to image segmentation, particularly for complex crack patterns, and could be applied in near-future applications.enquantum computingquantum imagesegmentationquantum optimizationimage processing500 Naturwissenschaften und Mathematikjournal article