gradient optimization 1implicit differentiation 1numerical stability 1projected entangled-pair states 1tensor networks 1
From the 1 of 2 linked papers with an AI index.
2 papers
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…
cond-mat.str-el2026
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…