  The application of quantum machine learning to large-scale high-resolution
image datasets is not yet possible due to the limited number of qubits and
relatively high level of noise in the current generation of quantum devices. In
this work, we address this challenge by proposing a quantum transfer learning
(QTL) architecture that integrates quantum variational circuits with a
classical machine learning network pre-trained on ImageNet dataset. Through a
systematic set of simulations over a variety of image datasets such as Ants &
Bees, CIFAR-10, and Road Sign Detection, we demonstrate the superior
performance of our QTL approach over classical and quantum machine learning
without involving transfer learning. Furthermore, we evaluate the adversarial
robustness of QTL architecture with and without adversarial training,
confirming that our QTL method is adversarially robust against data
manipulation attacks and outperforms classical methods.
