DMRG Approach to Optimizing Two-Dimensional Tensor Networks
arXiv:1908.08833
Abstract
Tensor network algorithms have been remarkably successful solving a variety of problems in quantum many-body physics. However, algorithms to optimize two-dimensional tensor networks known as PEPS lack many of the aspects that make the seminal density matrix renormalization group (DMRG) algorithm so powerful for optimizing one-dimensional tensor networks known as matrix product states. We implement a framework for optimizing two-dimensional PEPS tensor networks which includes all of steps that make DMRG so successful for optimizing one-dimension tensor networks. We present results for several 2D spin models and discuss possible extensions and applications.
Cited by in corpus (7)
- The ITensor Software Library for Tensor Network Calculations
- Fermionic Wave Functions from Neural-Network Constrained Hidden States
- Selected Configuration Interaction in a Basis of Cluster State Tensor Products
- Accurate simulation for finite projected entangled pair states in two dimensions
- Gauging tensor networks with belief propagation
- On Stability of Tensor Networks and Canonical Forms
- Topological crystals and soliton lattices in a Gross-Neveu model with Hilbert-space fragmentation