  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.
