Fermionic tensor network methods
Quinten Mortier, Lukas Devos, Lander Burgelman, Bram Vanhecke, Nick Bultinck, Frank Verstraete, Jutho Haegeman, Laurens Vanderstraeten
Abstract
We show how fermionic statistics can be naturally incorporated in tensor networks on arbitrary graphs through the use of graded Hilbert spaces. This formalism allows the use of tensor network methods for fermionic lattice systems in a local way, avoiding the need of a Jordan-Wigner transformation or the explicit tracking of leg crossings by swap gates in 2D tensor networks. The graded Hilbert spaces can be readily integrated with other internal and lattice symmetries, and only require minor extensions to an existing tensor network software package. We review and benchmark the fermionic versions of common algorithms for matrix product states and projected entangled-pair states.