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Implicit differentiation of tensor network algorithms
Lander Burgelman, Anna Francuz, Paul Brehmer +4
The current leading approach to the variational optimization of projected entangled-pair states (PEPS) is based on automatic differentiation, which allows for a convenient evaluati…
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…
Quantum error correction thresholds for the universal Fibonacci Turaev-Viro code
Alexis Schotte, Guanyu Zhu, Lander Burgelman +1
We consider a two-dimensional quantum memory of qubits on a torus which encode the extended Fibonaccistring-net code, and devise strategies for error correction when those qubits a…