迈向用于评估后量子密码学弹性的量子机器学习
Towards quantum machine learning for assessing the resilience of post-quantum cryptography
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中文总结 AI 辅助
研究尝试利用量子生成对抗网络（QGANs）评估后量子密码学弹性，通过将基于哈希数字签名概率分布加载到量子计算机内存的示例应用，证实近期混合量子 - 经典方法有此能力，为利用量子计算攻击后量子密码原语迈出第一步。
中文摘要 AI 辅助
量子计算机的潜在能力推动了适用于保护通信免受能够访问大型容错量子计算机的对手攻击的加密协议的发展。然而，尽管当前量子计算机在尺寸和精度方面有限，但它们仍可用于发现后量子密码协议中的漏洞和弱点。在这项工作中，我们尝试利用量子生成对抗网络（QGANs）的能力，QGANs是量子机器学习中一种有前景的架构，用于此目的。我们描述了QGAN架构的一个示例应用，即将基于哈希的数字签名的概率分布加载到量子计算机的内存中。我们的结果证实了近期的混合量子 - 经典方法具备实现此目的所需的能力。所提出的方法可作为工作流程的第一步，实现利用量子计算来攻击后量子密码原语。
英文摘要
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.
发表机构
- Institute of Theoretical and Applied Informatics（理论与应用信息研究所）
- Polish Academy of Sciences（波兰科学院）
机构由 AI 辅助整理，请以论文原文为准。
