Improvements to Neural Network Backflow Wavefunctions

The Neural Network backflow (NNBF) is a highly accurate variational wave-function ansatz for fermionic Hamiltonians. In this work, we consider a number of methodological developments to improve upon NNBF including looking at multideterminant expansions. We also compare the NNBF both empirically and analytically to other variational architectures understanding where it fits into the hiearchy of variational ansatz. We benchmark these new improvements on various model systems comparing the energetics and observables with other high-accuracy simulations.