Benefiting from Quantum? A Comparative Study of Q-Seg, Quantum-Inspired Techniques, and U-Net for Crack Segmentation

• URL: http://arxiv.org/abs/2410.10713v1
• Date: Mon, 14 Oct 2024 16:51:59 GMT
• Title: Benefiting from Quantum? A Comparative Study of Q-Seg, Quantum-Inspired Techniques, and U-Net for Crack Segmentation
• Authors: Akshaya Srinivasan, Alexander Geng, Antonio Macaluso, Maximilian Kiefer-Emmanouilidis, Ali Moghiseh,
• Abstract summary: This study evaluates the performance of quantum and quantum-inspired methods compared to classical models for crack segmentation. 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.
• Score: 41.01256771536732
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Exploring 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 gray-scale 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 for image segmentation, particularly for complex crack patterns, and could be applied in near-future applications.

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