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This paper explores the application of Quantum Generative Adversarial Networks (QGANs) to assess the resilience of post-quantum cryptographic protocols against quantum adversaries. By leveraging QGANs to load the probability distribution of hash-based digital signatures into quantum memory, the authors demonstrate that near-term hybrid quantum-classical methods can effectively identify vulnerabilities in these cryptographic systems. The findings highlight the potential of quantum machine learning techniques to inform and enhance the security of cryptographic protocols in the face of advancing quantum computing capabilities.
Near-term quantum machine learning can expose weaknesses in post-quantum cryptography, potentially undermining its security assurances.
The potential capabilities of quantum computers motivated the development of cryptographic protocols suitable for securing communication against adversaries with access to large fault-tolerant quantum computers. However, even though current quantum computers are limited in terms of size and precision, they can still be useful for finding loopholes and weaknesses in the post-quantum cryptographic protocols. In this work, we present 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. We describe an example application of QGAN architecture for the purpose of loading the probability distribution of the hash-based digital signatures into the memory of a quantum computer. Our results confirm that near-term hybrid quantum-classical methods possess capabilities required for this purpose. The presented approach can be used as a first step in the workflow, enabling the utilization of quantum computing for attacking post-quantum cryptographic primitives.
