对比量子与经典方法在裂缝分割中的表现，发现量子启发模型更优。
Benefiting from Quantum? A Comparative Study of Q-Seg, Quantum-Inspired Techniques, and U-Net for Crack Segmentation
- 采用量子启发的费米子方法和量子退火方法进行图像分割。
- 在复杂裂缝模式下，量子启发方法分割精度显著优于传统方法。
- 适合对高精度裂缝检测有需求的工程结构评估场景。
探索量子硬件提升经典实际应用的潜力仍是持续挑战。本研究评估了量子与量子启发方法在裂缝分割任务中的性能，相较于经典模型。基于标注的混凝土样本灰度图像块，我们对比了经典均值高斯混合方法、量子启发的费米子方法（Q-Seg）、基于量子退火的方法以及U-Net深度学习架构。结果表明，量子启发与量子方法在处理复杂裂缝模式时表现出显著优势，为近未来实际应用提供了可行方案。
原文摘要 · Abstract (English)
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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