2022
DOI: 10.21468/scipostphys.12.1.006
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Automatic differentiation applied to excitations with projected entangled pair states

Abstract: The excitation ansatz for tensor networks is a powerful tool for simulating the low-lying quasiparticle excitations above ground states of strongly correlated quantum many-body systems. Recently, the two-dimensional tensor network class of infinite projected entangled-pair states gained new ground state optimization methods based on automatic differentiation, which are at the same time highly accurate and simple to implement. Naturally, the question arises whether these new ideas can also be used to optimize t… Show more

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Cited by 22 publications
(12 citation statements)
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“…The tensor-network renormalization group methods [30,31,[35][36][37] are used to calculate dynamical response functions of the anisotropic Heisenberg model, defined in Eq. ( 1), with J = 1.67 meV and ∆ = 0.95.…”
Section: Methodsmentioning
confidence: 99%
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“…The tensor-network renormalization group methods [30,31,[35][36][37] are used to calculate dynamical response functions of the anisotropic Heisenberg model, defined in Eq. ( 1), with J = 1.67 meV and ∆ = 0.95.…”
Section: Methodsmentioning
confidence: 99%
“…A translation invariant PEPS on an infinite lattice is used to represent the ground state with 120 • antiferromagnetic order. An ansatz based on the single-mode approximation [31,[35][36][37] is used to construct the wave functions of excitation states. The local tensors for both the ground state and the excitation states are optimized by minimizing the corresponding energies with the aid of automatic differentiation [30,31].…”
Section: Methodsmentioning
confidence: 99%
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