# Quantum Transfer Learning with Adversarial Robustness for Classification of High-Resolution Image Datasets Source: [Quantum Transfer Learning with Adversarial Robustness for Classification of High-Resolution Image Datasets]() Collection index: [llms.txt]() Data updated: 2025-10-25T06:41:34.804Z Authors: Amena Khatun, Muhammad Usman Published: 2024-01-30T13:45:39.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()