  This article provides a survey of recent research efforts on the application
of quasi-Monte Carlo (QMC) methods to elliptic partial differential equations
(PDEs) with random diffusion coefficients. It considers, and contrasts, the
uniform case versus the lognormal case, single-level algorithms versus
multi-level algorithms, first order QMC rules versus higher order QMC rules,
and deterministic QMC methods versus randomized QMC methods. It gives a summary
of the error analysis and proof techniques in a unified view, and provides a
practical guide to the software for constructing and generating QMC points
tailored to the PDE problems. The analysis for the uniform case can be
generalized to cover a range of affine parametric operator equations.
