用量子生成对抗网络模拟哈希签名分布，探索后量子密码弱点。
Towards quantum machine learning for assessing the resilience of post-quantum cryptography
- 用量子生成对抗网络加载哈希签名的概率分布
- 近中期混合量子经典方法可实现该分布的存储与生成
- 为未来量子攻击后量子密码提供初步技术路径
量子计算机的潜在能力推动了抗量子攻击密码协议的发展。尽管当前量子计算机在规模和精度上受限，但仍可用于发现后量子密码协议中的漏洞。本文尝试利用量子生成对抗网络（QGAN）这一量子机器学习架构，将基于哈希的数字签名概率分布加载至量子计算机内存中。结果表明，近中期的混合量子-经典方法具备完成该任务的能力。该方法可作为后续量子攻击后量子密码原语的起点。
原文摘要 · Abstract (English)
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.
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