  In the past decade, quantum diffusion Monte Carlo (DMC) has been demonstrated
to successfully predict the energetics and properties of a wide range of
molecules and solids by numerically solving the electronic many-body
Schr\"odinger equation. We show that when coupled with quantum machine learning
(QML) based surrogate methods the computational burden can be alleviated such
that QMC shows clear potential to undergird the formation of high quality
descriptions across chemical space. We discuss three crucial approximations
necessary to accomplish this: The fixed node approximation, universal and
accurate references for chemical bond dissociation energies, and scalable
minimal amons set based QML (AQML) models. Numerical evidence presented
includes converged DMC results for over one thousand small organic molecules
with up to 5 heavy atoms used as amons, and 50 medium sized organic molecules
with 9 heavy atoms to validate the AQML predictions. Numerical evidence
collected for $\Delta$-AQML models suggests that already modestly sized QMC
training data sets of amons suffice to predict total energies with near
chemical accuracy throughout chemical space.
