Shipped: Classical vs. Quantum AI — Adversarial Robustness Comparison

An open-source AI security research proof of concept comparing the adversarial robustness of classical Convolutional Neural Networks (CNNs) and hybrid quantum-classical neural networks (HNNs).

Using the MNIST handwritten digit dataset, the framework trains and evaluates both architectures before and after individual and compounded white-box adversarial attacks.

The hybrid architecture integrates a classical CNN with a simulated parameterized quantum circuit containing rotation and entanglement operations.

The experiments examine how adversarial perturbations affect classification accuracy, prediction confidence, and model behavior across different architectures.

Results include model training curves, classification outcomes, adversarial examples, and comparisons of pre-attack and post-attack performance.

The primary research question is whether hybrid quantum-classical architectures demonstrate different robustness characteristics from conventional neural networks under controlled attack conditions.

The experiments are educational research demonstrations and do not establish a general quantum advantage or universal resistance to adversarial attacks.