gradient optimization 1implicit differentiation 1numerical stability 1projected entangled-pair states 1tensor networks 1
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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…