Institute of Theoretical and Applied Informatics, Polish Academy of Sciences is applying quantum machine learning in a new way: not creating new cryptography, but proactively testing the defenses of existing protocols. The work details the successful demonstration of loading the probability distribution of hash-based digital signatures into quantum computer memory using Quantum Generative Adversarial Networks, or QGANs. This represents a concrete step toward utilizing quantum computing to actively attack these signatures, even with the limitations of near-term hybrid quantum-classical methods. The team views this approach as a first step in the workflow for utilizing quantum computing to attack post-quantum cryptographic primitives, moving beyond simply designing algorithms resistant to known quantum threats like Shor’s and Grover’s algorithms. Post-Quantum Cryptography & Quantum Threat Landscape Quantum Generative Adversarial Networks (QGANs) are now being directed toward proactively testing the defenses of post-quantum cryptography, rather than solely focusing on creating new cryptographic methods. This shift signifies a growing recognition that current quantum computers can reveal vulnerabilities in algorithms designed to withstand future, more powerful machines. Researchers are no longer solely concerned with building unbreakable codes; they are actively probing for weaknesses in those already proposed as quantum-resistant. This achievement isn’t about breaking the signatures now, but about establishing the capability to do so, and understanding how near-term quantum devices can be leveraged for attack. The work confirms that “near-term hybrid quantum-classical methods possess capabilities required for this purpose,” indicating that even current technology can be used to analyze and potentially compromise post-quantum schemes. This is a departure from simply theorizing about the future threat posed by cryptographically relevant quantum computers (CRQCs), which require a large number of qubits, long coherence times, and high-fidelity quantum gates. The researchers present an example application of QGANs for hash-based digital signatures, utilizing them to model their underlying probability distributions.