Towards Quantum Machine Learning for Assessing the Resilience of Post-quantum Cryptography
This work presents an attempt to utilize the capabilities of Quantum Generative Adversarial Networks (QGANs), one of the promising architectures used in quantum machine learning, for this purpose, and confirms that near-term hybrid quantum-classical methods possess capabilities required for this purpose.