Liu Longju, Yang Jie, Wu Mei, Liu Jinlu, Huang Wei, Li Yang, Xu Bingjie
National Key Laboratory of Security Communication, Institute of Southwestern Communication, Chengdu 610041, China.
Entropy (Basel). 2025 Jan 14;27(1):68. doi: 10.3390/e27010068.
Quantum Random Number Generators (QRNGs) have been theoretically proven to be able to generate completely unpredictable random sequences, and have important applications in many fields. However, the practical implementation of QRNG is always susceptible to the unwanted classical noise or device imperfections, which inevitably diminishes the quality of the generated random bits. It is necessary to perform the post-processing to extract the true quantum randomness contained in raw data generated by the entropy source of QRNG. In this work, a novel post-processing method for QRNG based on Zero-phase Component Analysis (ZCA) whitening is proposed and experimentally verified through both time and spectral domain analysis, which can effectively reduce auto-correlations and flatten the spectrum of the raw data, and enhance the random number generation rate of QRNG. Furthermore, the randomness extraction is performed after ZCA whitening, after which the final random bits can pass the NIST test.
量子随机数发生器（QRNGs）在理论上已被证明能够生成完全不可预测的随机序列，并且在许多领域都有重要应用。然而，QRNG的实际实现总是容易受到不必要的经典噪声或设备缺陷的影响，这不可避免地会降低所生成随机比特的质量。有必要进行后处理，以提取QRNG熵源生成的原始数据中包含的真正量子随机性。在这项工作中，提出了一种基于零相位分量分析（ZCA）白化的QRNG新型后处理方法，并通过时域和频域分析进行了实验验证，该方法可以有效降低自相关性并使原始数据的频谱平坦化，提高QRNG的随机数生成率。此外，在ZCA白化之后进行随机数提取，之后最终的随机比特可以通过美国国家标准与技术研究院（NIST）测试。
