Adversarial attacks on hybrid classical-quantum Deep Learning models for Histopathological Cancer Detection

TL;DR

This paper explores the robustness of hybrid classical-quantum deep learning models in histopathological cancer detection, demonstrating their superior accuracy over classical models under adversarial attacks.

Contribution

It introduces a hybrid classical-quantum transfer learning approach for cancer detection and evaluates its resilience against adversarial attacks, comparing multiple models.

Findings

Hybrid classical-quantum models outperform classical models under adversarial attacks.

Quantum transfer learning improves accuracy in histopathological cancer detection.

Multiple classical feature extractors integrated with quantum circuits enhance model robustness.

Abstract

We present an effective application of quantum machine learning in histopathological cancer detection. The study here emphasizes two primary applications of hybrid classical-quantum Deep Learning models. The first application is to build a classification model for histopathological cancer detection using the quantum transfer learning strategy. The second application is to test the performance of this model for various adversarial attacks. Rather than using a single transfer learning model, the hybrid classical-quantum models are tested using multiple transfer learning models, especially ResNet18, VGG-16, Inception-v3, and AlexNet as feature extractors and integrate it with several quantum circuit-based variational quantum circuits (VQC) with high expressibility. As a result, we provide a comparative analysis of classical models and hybrid classical-quantum transfer learning models for histopathological cancer detection under several adversarial attacks. We compared the performance accuracy of the classical model with the hybrid classical-quantum model using pennylane default quantum simulator. We also observed that for histopathological cancer detection under several adversarial attacks, Hybrid Classical-Quantum (HCQ) models provided better accuracy than classical image classification models.