Can Hybrid Quantum Neural Networks Improve Adversarial AI Security?

Neural networks can achieve impressive classification accuracy, yet carefully crafted adversarial examples can cause them to make incorrect predictions.

In one of my research POCs, I explored whether a hybrid architecture combining classical deep learning and simulated quantum circuits could be trained to resist compounded adversarial attacks.

The framework integrates a Convolutional Neural Network (CNN) with a Quantum Neural Network (QNN) using Google's Cirq library.

Some interesting experimental findings:

• FGSM + PGD attacks reduced undefended classification accuracy from 98% to 20%.
• Adversarial training restored accuracy to 98% under the tested conditions.
• The framework also evaluates combinations involving Carlini-Wagner, BIM, and DeepFool attacks.
• Quantum circuit components incorporate parameterized rotations and CNOT entanglement gates.

The experiments demonstrate improvements from adversarial training, although they do not establish that the quantum component independently provides greater robustness than classical alternatives.

My question for the Lounge.ai community:
As quantum machine learning develops, could hybrid classical-quantum architectures offer new approaches to securing AI models, or will improvements primarily come from better classical adversarial training techniques?

Interested in hearing perspectives from AI security researchers, quantum developers, and machine learning engineers.