  We apply a series of projection techniques on top of tensor networks to
compute energies of ground state wave functions with higher accuracy than
tensor networks alone with minimal additional cost. We consider both matrix
product states as well as tree tensor networks in this work. Building on top of
these approaches, we apply fixed-node quantum Monte Carlo, Lanczos steps, and
exact projection. We demonstrate these improvements for the triangular lattice
Heisenberg model, where we capture up to 57 percent of the remaining energy not
captured by the tensor network alone. We conclude by discussing further ways to
improve our approach.
