在IQM Spark上实现真正的随机数生成器
True Random Number Generators on IQM Spark
AI总结：
本文研究了在IQM Spark量子计算机上实现的5种TRNG电路，分析了其生成的随机序列质量，并综述了现有文献。
摘要：
随机数生成对于许多现代应用至关重要，包括密码学、模拟和机器学习。传统伪随机数虽然在统计上具有不可预测性，但最终是确定性的。另一方面，真正的随机数生成（TRNG）提供真正的随机性。获取这种随机性的一种方法是量子系统，包括量子计算机。因此，近年来利用量子计算机进行TRNG引起了广泛关注。然而，现有研究几乎只考虑IBM量子计算机，往往止于使用模拟，并且通常只测试少量不同的TRNG量子电路。在本文中，我们通过在Wrocław科技大学安装的现实量子计算机Odra 5上研究TRNG电路来解决这些问题。这也是首次利用IQM超导架构进行研究。由于Odra 5在本地可用，因此可以更全面地研究各种TRNG电路。特别是，我们考虑了5种类型的TRNG电路，总计105个电路变体。每种电路用于生成100万位的随机位。然后，我们使用NIST SP 800-22和NIST SP 800-90B测试套件对获得的随机序列的质量进行了分析。我们还提供了关于基于量子计算机的TRNG现有文献的全面回顾。
英文摘要：
Random number generation is fundamental for many modern applications including cryptography, simulations and machine learning. Traditional pseudo-random numbers may offer statistical unpredictability, but are ultimately deterministic. On the other hand, True Random Number Generation (TRNG) offers true randomness. One way of obtaining such randomness are quantum systems, including quantum computers. As such the use of quantum computers for TRNG has received considerable attention in recent years. However, existing studies almost exclusively consider IBM quantum computers, often stop at using simulations and usually test only a handful of different TRNG quantum circuits. In this paper, we address those issues by presenting a study of TRNG circuits on Odra 5 a real-life quantum computer installed at Wrocław University of Science and Technology. It is also the first study to utilize the IQM superconducting architecture. Since Odra 5 is available on-premises it allows for much more comprehensive study of various TRNG circuits. In particular, we consider 5 types of TRNG circuits with 105 circuit subvariants in total. Each circuit is used to generate 1 million bits. We then perform an analysis of the quality of the obtained random sequences using the NIST SP 800-22 and NIST SP 800-90B test suites. We also provide a comprehensive review of existing literature on quantum computer-based TRNGs.
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