Neural network representation for minimally entangled typical thermal state

We investigate a new approach for modeling finite temperature quantum systems by generating and sampling minimally entangled typical thermal state(METTS). A restricted Boltzmann machine (RBM), a type of artificial neural network, is used as a variational wave function to represent METTS in variational Monte Carlo. We evolve the parameters to match the imaginary time evolution and calculated the thermal average of physical observables. Our results demonstrate that the properties of finite temperature quantum systems can be precisely explored by variational Monte Carlo methods.