  Virtually, every ab-initio electronic structure method (Coupled Cluster,
DMRG, etc.) can be viewed as an algorithm to compress the ground-state
wavefunction. This compression is usually obtained by exploiting some physical
structure of the wavefunction, which leads to issues when the system changes
and that structure is lost. Compressions which are efficient near equilibrium
(coupled cluster) or in 1-D systems (DMRG) often fail catastrophically
elsewhere. To overcome these issues, we seek a scheme that compresses
wavefunctions without any supervised physical information. In this manuscript,
we introduce a scheme to compress molecular wavefunctions using a model for
high dimensional functions from machine learning: a restricted Boltzmann
machine (RBM). We show that, while maintaining chemical accuracy, the RBM can
significantly compress the exact wavefunction.
