Neural Network Backflow for ab initio quantum chemistry in second quantization

There has been recent interest in using machine learning architectures such as restricted Boltzmann machines and autoregressive neural networks for finding ground states of physical systems in second quantization. Recent work has proposed an alternative approach - the neural network backflow (NNBF) - and tested it on model systems. In this work, we focus on getting the NNBF to work with ab-initio quantum chemistry Hamiltonians. We explore and improve various different optimization techniques including deterministic approaches and benchmark the efficacy of NNBF as we increase the system size as well as approach the complete basis set limit. This research paves the way for more efficient ab-initio simulations.