Approximating power of machine-learning ansatz for quantum many-body states
Approximating power of machine-learning ansatz for quantum many-body states
An artificial neural network (ANN) with the restricted Boltzmann machine (RBM) architecture was recently proposed as a versatile variational quantum many-body wave function. In this work we provide physical insights into the performance of this ansatz. We uncover the connection between the structure of RBM and perturbation series, which explains …