QMCPy: A Python Framework for (Quasi-)Monte Carlo Algorithms

The Journal of Open Source Software 2026 Open access

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Authors: Aleksei G. Sorokin, Fred J. Hickernell, Sou-Cheng Terrya Choi, Jagadeeswaran Rathinavel, Pieterjan Robbe, Aadit Jain
DOI: 10.21105/joss.09705
Cited by: 0
References: 62

Abstract

Monte Carlo (MC) methods estimate high-dimensional integrals by computing sample averages at independent and identically distributed (IID) random points.Quasi-Monte Carlo (QMC) methods replace IID samples with low-discrepancy (LD) sequences which more uniformly cover the integration domain, leading to faster convergence and reduced computational requirements.

Fields of Study & Subjects

Mathematical Approximation and Integration Mathematical functions and polynomials Quantum Computing Algorithms and Architecture

Publication details

Journal / source
The Journal of Open Source Software
Volume
11
Issue
117
Pages
9705–9705
Publication date
19 January 2026