Gaussian fermionic tensor networks and finite entanglement scaling in 2D

by Minghao Li (Oxford University)

Tuesday 20 Feb 2024, 11:00 → 12:00 Europe/Zurich

WHGA/121

Tensor network state (TNS) is a set of wavefunctions that naturally exhibit the area law scaling of entanglement entropy. Because of this, it is a powerful method to simulate systems with gapped local Hamiltonian. The theory of finite entanglement scaling is developed to portray how TNS is used to study area law breaking states, such as 1D critical systems and higher dimensional Fermi liquids. Although quite some work has been done in the 1D case, the difficulty to contract higher dimensional TNS has slowed the studies in higher dimensions. In this talk, I will talk about how we use Gaussian fermionic tensor network to study the finite entanglement scaling of the Fermi surface states. In our main result, we discover a dimensionless scaling function that controls how the optimised TNS at different bond dimension D approximate the entanglement entropy of Fermi surface.

Link: https://arxiv.org/abs/2305.02091