Adversarial Robustness in Fake Quantum Simulators
The study tested a quantum machine learning model using fake simulators to see how robust it is against adversarial attacks under realistic noise. It found that increasing the ratio of adversarial training samples to benign ones significantly improved robustness, showing that a balanced 50/50 retraining split is essential for achieving high accuracy in noisy quantum systems.