DMRG Approach to Optimizing Two-Dimensional Tensor Networks

Katharine Hyatt; E. M. Stoudenmire

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arXiv · 1908.08833

DMRG Approach to Optimizing Two-Dimensional Tensor Networks

Katharine Hyatt·E. M. Stoudenmire

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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.

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Katharine Hyatt, E. M. Stoudenmire. 2020-04-20. DMRG Approach to Optimizing Two-Dimensional Tensor Networks. https://arxiv.org/abs/1908.08833

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