61 citations · 396 across the 20 of their papers we have counts for
8 papers · 1 filter
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…
A scaling hypothesis for projected entangled-pair states
Bram Vanhecke, Juraj Hasik, Frank Verstraete +1
We introduce a new paradigm for scaling simulations with projected entangled-pair states (PEPS) for critical strongly-correlated systems, allowing for reliable extrapolations of PE…
Efficient variational contraction of two-dimensional tensor networks with a non-trivial unit cell
A. Nietner, B. Vanhecke, F. Verstraete +2
Tensor network states provide an efficient class of states that faithfully capture strongly correlated quantum models and systems in classical statistical mechanics. While tensor n…