DMRG Approach to Optimizing Two-Dimensional Tensor Networks


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.

DMRG Approach to Optimizing Two-Dimensional Tensor Networks
Katharine Hyatt
Postdoctoral Scholar

My research focuses on developing new numerical methods to understand 2D correlated electronic systems, and finding interesting applications in condensed matter physics for these methods. I also moonlight as a sometime Julia language and package developer.