Practitioners wishing to experience the efficiency gains from using low discrepancy sequences need correct, well-written software. This article, based on our MCQMC 2020 tutorial, describes some of the better quasi-Monte Carlo (QMC) software available. We highlight the key software components required to approximate multivariate integrals or expectations of functions of vector random variables by QMC. We have combined these components in QMCPy, a Python open source library, which we hope will draw the support of the QMC community. Here we introduce QMCPy.

中文翻译：

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准蒙特卡洛软件

希望通过使用低差异序列来体验效率提升的从业人员需要正确，编写良好的软件。本文基于我们的MCQMC 2020教程，介绍了一些可用的更好的准蒙特卡洛（QMC）软件。我们重点介绍了QMC逼近多元积分或对矢量随机变量函数的期望所需的关键软件组件。我们将这些组件组合到了QMCPy（一个Python开源库）中，希望它能吸引QMC社区的支持。在这里，我们介绍QMCPy。