From the 1 of 18 linked papers with an AI index.
2 citations · 5 across the 10 of their papers we have counts for
4 papers · 1 filter
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…
Matrix Product Operator Encodings of the Magnus Expansion and Dyson Series
Victor Vanthilt, Maarten Van Damme, Jutho Haegeman +2
We introduce a matrix product operator (MPO) encoding of the Magnus expansion and the Dyson series for one-dimensional quantum lattice models with time-dependent Hamiltonians. The…
Finite-Element Matrix Product States for Continuum Models in One Dimension
Akshay Shankar, Karel Van Acoleyen, Jutho Haegeman
We present a matrix product state framework for simulating one-dimensional quantum many-body systems in the continuum using non-orthogonal single-particle basis sets. By mapping th…
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…