Adversarially Robust Quantum Transfer Learning

Abstract

Quantum machine learning (QML) has emerged as a promising area of research for enhancing the performance of classical machine learning systems by leveraging quantum computational principles. However, practical deployment of QML remains limited due to current hardware constraints such as limited number of qubits and quantum noise. This chapter introduces a hybrid quantum-classical architecture that combines the advantages of quantum computing with transfer learning techniques to address high-resolution image classification. Specifically, we propose a Quantum Transfer Learning (QTL) model that integrates classical convolutional feature extraction with quantum variational circuits. Through extensive simulations on diverse datasets including Ants \& Bees, CIFAR-10, and Road Sign Detection, we demonstrate that QTL achieves superior classification performance compared to both conventional and quantum models trained without transfer learning. Additionally, we also investigate the model's vulnerability to adversarial attacks and demonstrate that incorporating adversarial training significantly boosts the robustness of QTL, enhancing its potential for deployment in security sensitive applications.