61 citations · 396 across the 20 of their papers we have counts for
21 papers
On the origin of finite entanglement scaling
Luke Hodgkiss, Laurens Lootens, Atsushi Ueda +2
The concept of finite entanglement scaling forms one of the pillars on which the tensor network ecosystem is built. In this paper, we resolve the open problem of determining the ac…
Implicit differentiation of tensor network algorithms
Lander Burgelman, Anna Francuz, Paul Brehmer +4
The current leading approach to the variational optimization of projected entangled-pair states (PEPS) is based on automatic differentiation, which allows for a convenient evaluati…
Fermionic tensor network methods
Quinten Mortier, Lukas Devos, Lander Burgelman +5
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 to use tensor…
Stable and efficient differentiation of tensor network algorithms
Anna Francuz, Norbert Schuch, Bram Vanhecke
Gradient based optimization methods are the established state-of-the-art paradigm to study strongly entangled quantum systems in two dimensions with Projected Entangled Pair States…
Detecting emergent continuous symmetries at quantum criticality
Mingru Yang, Bram Vanhecke, Norbert Schuch
New or enlarged symmetries can emerge at the low-energy spectrum of a Hamiltonian that does not possess the symmetries, if the symmetry breaking terms in the Hamiltonian are irrele…
Learning Generative Models for Active Inference using Tensor Networks
Samuel T. Wauthier, Bram Vanhecke, Tim Verbelen +1
Active inference provides a general framework for behavior and learning in autonomous agents. It states that an agent will attempt to minimize its variational free energy, defined…