Neural Networks for Many-Body Physics

Artificial neural networks have been recently introduced as a general ansatz to compactly represent many-body wave functions. In conjunction with Variational Monte Carlo, this ansatz has been applied to find Hamiltonian ground states and their energies. Here we introduce the tools we use to do such calculations and also provide extensions of this method to study properties of excited states, a central task in several many-body quantum calculations. In addition, we also present some results on the J1-J2 model on the square lattice to show the viability of such ansatz.