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arXiv · 2410.10713

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

Akshaya Srinivasan·Alexander Geng·Antonio Macaluso·Maximilian Kiefer-Emmanouilidis·Ali Moghiseh
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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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Akshaya Srinivasan, Alexander Geng, Antonio Macaluso, Maximilian Kiefer-Emmanouilidis, Ali Moghiseh. 2024-10-14. Benefiting from Quantum? A Comparative Study of Q-Seg, Quantum-Inspired Techniques, and U-Net for Crack Segmentation. https://arxiv.org/abs/2410.10713

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