  The validation, verification, and uncertainty quantification of
computationally expensive theoretical models of quantum many-body systems
require the construction of fast and accurate emulators. In this work, we
develop emulators for auxiliary field diffusion Monte Carlo (AFDMC), a powerful
many-body method for nuclear systems. We introduce a reduced-basis method (RBM)
emulator for AFDMC and study it in the simple case of the deuteron.
Furthermore, we compare our RBM emulator with the recently proposed parametric
matrix model (PMM) that combines elements of RBMs with machine learning. We
contrast these two approaches with a traditional Gaussian Process emulator. All
three emulators constructed here are based on a very limited set of 5 training
points, as expected for realistic AFDMC calculations, but validated against
$\mathcal{O}(10^3)$ exact solutions. We find that the PMM, with emulator errors
of only $\approx 0.1 \%$ and speed-up factors of $\approx 10^7$, outperforms
the other two emulators when applied to AFDMC.
