Quantum Transfer Learning with Adversarial Robustness for Classification of High‐Resolution Image Datasets

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, this challenge is addressed 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, the superior performance of the QTL approach over classical and quantum machine learning without involving transfer learning is demonstrated. Furthermore, the adversarial robustness of QTL architecture with and without adversarial training is evaluated, confirming that our QTL method is adversarially robust against data manipulation attacks and outperforms classical methods. A quantum transfer learning (QTL) architecture is proposed to address the challenges in high‐dimensional image processing and classification tasks. The adversarial robustness of the QTL architecture with and without adversarial training, is also evaluated, confirming that the QTL method is adversarially robust against data manipulation attacks and outperforms classical methods.