  Quantum Monte Carlo (QMC) can play a very important role in generating
accurate data needed for constructing potential energy surfaces. We argue that
QMC has advantages in terms of a smaller systematic bias and an ability to
cover phase space more completely. The stochastic noise can ease the training
of the machine learning model. We discuss how stochastic errors affect the
generation of effective models by analyzing the errors within a linear least
squares procedure, finding that there is an advantage to having many relatively
imprecise data points for constructing models. We then analyze the effect of
noise on a model of many-body silicon finding that noise in some situations
improves the resulting model. We then study the effect of QMC noise on two
machine learning models of dense hydrogen used in a recent study of its phase
diagram. The noise enable us to estimate the errors in the model. We conclude
with a discussion of future research problems.
