Ghersi Dario, Parakh Abhishek, Mezei Mihaly
School of Interdisciplinary Informatics, University of Nebraska at Omaha, Omaha, Nebraska, 68182.
Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, 10029.
J Comput Chem. 2017 Dec 5;38(31):2713-2720. doi: 10.1002/jcc.25065. Epub 2017 Sep 18.
Four pseudorandom number generators were compared with a physical, quantum-based random number generator using the NIST suite of statistical tests, which only the quantum-based random number generator could successfully pass. We then measured the effect of the five random number generators on various calculated properties in different Markov-chain Monte Carlo simulations. Two types of systems were tested: conformational sampling of a small molecule in aqueous solution and liquid methanol under constant temperature and pressure. The results show that poor quality pseudorandom number generators produce results that deviate significantly from those obtained with the quantum-based random number generator, particularly in the case of the small molecule in aqueous solution setup. In contrast, the widely used Mersenne Twister pseudorandom generator and a 64-bit Linear Congruential Generator with a scrambler produce results that are statistically indistinguishable from those obtained with the quantum-based random number generator. © 2017 Wiley Periodicals, Inc.
使用美国国家标准与技术研究院(NIST)的统计测试套件,将四个伪随机数生成器与一个基于量子的物理随机数生成器进行了比较,结果只有基于量子的随机数生成器能够成功通过测试。然后,我们在不同的马尔可夫链蒙特卡罗模拟中,测量了这五个随机数生成器对各种计算属性的影响。测试了两种类型的系统:在恒温恒压条件下,水溶液和液态甲醇中小分子的构象采样。结果表明,质量较差的伪随机数生成器产生的结果与基于量子的随机数生成器得到的结果有显著偏差,特别是在水溶液中小分子的设置中。相比之下,广泛使用的梅森旋转伪随机生成器和带有加扰器的64位线性同余生成器产生的结果,在统计上与基于量子的随机数生成器得到的结果没有区别。© 2017威利期刊公司。