Benefiting from Quantum? A Comparative Study of Q-Seg, Quantum-Inspired Techniques, and U-Net for Crack Segmentation
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866929541356716032 |
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| author | Srinivasan, Akshaya Geng, Alexander Macaluso, Antonio Kiefer-Emmanouilidis, Maximilian Moghiseh, Ali |
| author_facet | Srinivasan, Akshaya Geng, Alexander Macaluso, Antonio Kiefer-Emmanouilidis, Maximilian Moghiseh, Ali |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_10713 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Benefiting from Quantum? A Comparative Study of Q-Seg, Quantum-Inspired Techniques, and U-Net for Crack Segmentation Srinivasan, Akshaya Geng, Alexander Macaluso, Antonio Kiefer-Emmanouilidis, Maximilian Moghiseh, Ali Computer Vision and Pattern Recognition Disordered Systems and Neural Networks Image and Video Processing 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. |
| title | Benefiting from Quantum? A Comparative Study of Q-Seg, Quantum-Inspired Techniques, and U-Net for Crack Segmentation |
| topic | Computer Vision and Pattern Recognition Disordered Systems and Neural Networks Image and Video Processing |
| url | https://arxiv.org/abs/2410.10713 |