tensor networks 2critical systems 1finite entanglement scaling 1gradient optimization 1implicit differentiation 1matrix product states 1numerical stability 1projected entangled pair states 1projected entangled-pair states 1sparse linear solver 1
From the 2 of 3 linked papers with an AI index.
3 papers
quant-ph2026
On the origin of finite entanglement scaling
Luke Hodgkiss, Laurens Lootens, Atsushi Ueda +2
The paper identifies the actual perturbations introduced by matrix product state approximations of critical quantum systems, showing they differ from conformal field theory predict…
quant-ph2026
Implicit differentiation of tensor network algorithms
Lander Burgelman, Anna Francuz, Paul Brehmer +4
The paper applies implicit differentiation to the gradient computation in projected entangled-pair state (PEPS) optimization, reducing computational cost and eliminating numerical…
quant-ph2024
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…