Quantum computers possess the potential to process data using a remarkably reduced number of qubits compared to conventional bits, as per theoretical foundations. However, recent experiments have indicated that the practical feasibility of retrieving an image from its quantum encoded version is currently limited to very small image sizes. Despite this constraint, variational quantum machine learning algorithms can still be employed in the current noisy intermediate scale quantum (NISQ) era. An example is a hybrid quantum machine learning approach for edge detection. In our study, we present an application of quantum transfer learning for detecting cracks in gray value images. We compare the performance and training time of PennyLane's standard qubits with IBM's qasm\_simulator and real backends, offering insights into their execution efficiency.

翻译：量子计算机理论上具备以远少于传统比特数量的量子比特处理数据的潜力。然而，近期实验表明，从量子编码版本中恢复图像的可行性目前仅限于极小尺寸的图像。尽管如此，在当前含噪中等规模量子（NISQ）时代，变分量子机器学习算法依然可以应用，例如混合量子机器学习方法可用于边缘检测。在本研究中，我们提出一种量子迁移学习应用以检测灰度图像中的裂纹。我们比较了PennyLane的标准量子比特与IBM的qasm_simulator及真实后端的性能与训练时间，揭示了其执行效率的相关特征。