Improving neural network performance for solving quantum sign structure

Recently, using neural quantum states to solve the ground state of non-stoquastic Hamiltonian has been widely studied. However, they rely on a priori knowledge of sign structure or a pre-trained phase network. Here we propose a modified stochastic reconfiguration method, so that two separate neural networks for phase and amplitude can be trained simultaneously. Moreover, a better optimization scheme is implemented to further speed up the energy minimization process. This technique is demonstrated on the Heisenberg J1-J2 model.