Exact and Asymptotically Complete Robust Verifications of Neural Networks via Quantum Optimization

Researchers have developed quantum optimization models for robust verification of deep neural networks against adversarial attacks. The approach provides exact verification for ReLU networks and asymptotically complete verification for networks with general activation functions like sigmoid and tanh.