A DMRG-style PEPS optimizer that builds an approximate canonical form and solves regular eigenvalue problems is tested on the 2D Heisenberg model, converging close to quantum Monte Carlo energies within about ten sweeps.
Symmetric cluster expansions with tensor networks
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abstract
Cluster expansions for the exponential of local operators are constructed using tensor networks. In contrast to other approaches, the cluster expansion does not break any spatial or internal symmetries and exhibits a very favourable prefactor to the error scaling versus bond dimension. This is illustrated by time evolving a matrix product state using very large time steps, and by constructing a novel robust algorithm for finding ground states of 2-dimensional Hamiltonians using projected entangled pair states as fixed points of 2-dimensional transfer matrices.
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cond-mat.str-el 1years
2019 1verdicts
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DMRG Approach to Optimizing Two-Dimensional Tensor Networks
A DMRG-style PEPS optimizer that builds an approximate canonical form and solves regular eigenvalue problems is tested on the 2D Heisenberg model, converging close to quantum Monte Carlo energies within about ten sweeps.