Towards quantum machine learning for assessing the resilience of post-quantum cryptography
Jarosław A. Miszczak
quant-ph, cs.CR, cs.LG
Submitted: 2026-07-15
Comments: 14 pages, 7 figures, version accepted for ICCS 2026
Journal ref: Lecture Notes in Computer Science, vol 16789 (2026)
DOI: 10.1007/978-3-032-29918-5_3
Code: https://github.com/qiskit-community/qiskit-machine-learning
License: http://creativecommons.org/licenses/by-sa/4.0/
The gist: The potential capabilities of quantum computers motivated the development of cryptographic protocols suitable for securing communication against adversaries with access to large fault-tolerant
Terminology
Abstract
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.
Sources
- Provable and Verifiable Quantum Advantage in Sample Complexity
- Generative Adversarial Networks
- A Survey of Quantum Generative Adversarial Networks: Architectures, Use Cases, and Real-World Implementations
- Adam: A Method for Stochastic Optimization
- Variational quantum generators: Generative adversarial quantum machine learning for continuous distributions
- Qiskit Machine Learning: an open-source library for quantum machine learning tasks at scale on quantum hardware and classical simulators
- Optimizing Ansatz Design in Quantum Generative Adversarial Networks Using Large Language Models
- A Survey on Quantum Machine Learning: Current Trends, Challenges, Opportunities, and the Road Ahead
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