From the 1 of 4 linked papers with an AI index.
4 papers
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…
PEPSKit.jl: A Julia package for projected entangled-pair state simulations
Paul Brehmer, Lander Burgelman, Zheng-Yuan Yue +3
We present PEPSKitjl, a Julia package for simulating two-dimensional quantum many-body systems with infinite projected entangled-pair states (iPEPS). PEPSKitjl builds on the…
Interface roughening in the 3-D Ising model with tensor networks
Atsushi Ueda, Lander Burgelman, Luca Tagliacozzo +1
Interfaces in three-dimensional many-body systems can exhibit rich phenomena beyond the corresponding bulk properties. In particular, they can fluctuate and give rise to massless l…
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…