Adversarial attacks on hybrid classical-quantum Deep Learning models for
  Histopathological Cancer Detection
Biswaraj Baral Reek Majumdar Bhavika Bhalgamiya Taposh Dutta Roy
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.