KEY TAKEAWAY
Results developed by the Institute of Theoretical and Applied Informatics, Polish Academy of Sciences, within the scope of the Q-Fence project, were presented at conferences in June and July. The presented results focus on the capabilities of quantum computers to attack cryptographic protocols and the use of quantum random number generators.
Post-Quantum Cryptography and Quantum Machine Learning
During the Quantum Computing Workshop (QCW2026), part of the 26th International Conference on Computational Science (ICCS 2026), results concerning the utilisation of quantum machine learning were accepted for a talk.
The presented results explore the potential of Quantum Generative Adversarial Networks (QGANs), one of the promising architectures used in quantum machine learning, to identify loopholes and weaknesses in post-quantum cryptography protocols. In particular, the example application of a QGAN architecture demonstrated the loading of the probability distribution of hash-based digital signatures into the memory of a quantum computer.
Quantum machine learning is among the most promising applications of quantum computing, and the security assessment of cryptographic protocols needs to take developments in this field into account.
More details about these results are available in the arXiv preprint, published in the conference proceedings in Lecture Notes in Computer Science, vol. 16789 (2026).
Quantum Random Number Generators in Cryptography
Another important area investigated within the Q-Fence project is the impact of randomness on the security of post-quantum protocols. One of the project’s goals is to create a framework based on true random numbers generated using quantum mechanics.
The talk “Quantum Random Numbers and Where to Find Them”, presented at the Computability in Europe (CiE 2026) conference as part of the Quantum Information Session, provided an overview of recent progress in the field of quantum random number generators.
The presentation focused on issues related to the accessibility and quality of randomness sources, as well as their potential use in cryptographic applications. It also provided an opportunity to present Q-Fence project results to an audience interested in the theoretical aspects of computation.
Slides from the CiE conference are available on Zenodo.
The Impact of the Presented Results on the Q-Fence Project
The results in the area of quantum machine learning are important for developing a better understanding of the power of quantum computers in the context of cryptography. At the same time, the utilisation of quantum random number generators is crucial for securing post-quantum protocols.
The results presented during ICCS 2026 and CiE 2026 will contribute to the development of protocols and methods within the scope of the Q-Fence project.