Gradient optimization of finite projected entangled pair states

The projected entangled pair states (PEPS) methods have been proved to be powerful tools to solve the strongly correlated quantum many-body problems in two-dimension. However, due to the high computational scaling with the virtual bond dimension D, PEPS are often limited to rather small bond dimensions. The optimization of the ground state using imaginary time evolution method with simple update scheme may go to a larger bond dimension. However, the accuracy of the rough approximation to the environment of the local tensors is questionable. We demonstrated that combining the Monte Carlo sampling techniques and gradient optimization will offer an efficient method to calculate the PEPS ground state. By taking the advantages of massive parallel computing, we can study the quantum systems with larger bond dimensions up to D=16 without resorting to any symmetry. Benchmark tests of the method on the J₁-J₂ model give impressive accuracy compared with exact results.