Hybrid quantum transfer learning for crack image classification on NISQ hardware
This work addresses crack detection for infrastructure inspection, but it is incremental as it adapts existing quantum methods to a specific domain.
The study tackled crack detection in gray value images using quantum transfer learning, achieving comparable performance to classical methods with a 5% accuracy drop but 30% faster training on NISQ hardware.
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.