Systematic lowering of the scaling of Monte Carlo calculations by partitioning and subsampling.

We propose to compute physical properties by Monte Carlo calculations using conditional expectation values. The latter are obtained on top of the usual Monte Carlo sampling by partitioning the physical space in several subspaces or fragments, and subsampling each fragment (i.e.

Stochastic effective core potentials, improving efficiency using a spin-dependent core definition.

Numerically cheap single-core subsamplings have been used to build improved estimators for molecular properties in the variational Monte Carlo framework. The resulting estimators depend only on the valence electron positions and can be thought of as an exact effective core potential for the total energy. We are proposing a spin-dependent core definition which enables exploiting these single-core subsamplings (or sidewalks) not only to decrease the variance of the estimators but also to restrict the main variational Monte Carlo dynamics to the valence region.