Machine learning reconstruction accuracy is substantially higher for spectral-edge eigenstates than for mid-spectrum eigenstates, providing a new quantitative measure of information content in many-body quantum states.
Laflorencie, Physics Reports646, 1 (2016)
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A QMC-based framework tests the lattice-Bisognano-Wichmann ansatz for reconstructing entanglement Hamiltonians in 2D systems without Lorentz invariance or translational symmetry, finding good accuracy for ordinary boundaries.
Entanglement asymmetry for space-inversion symmetry of free fermions on honeycomb lattices exhibits nonanalytic dependence on energy imbalance and persists after a quench to the symmetric point due to flat bands in certain geometries.
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Information in Many-body Eigenstates: A Question of Learnability
Machine learning reconstruction accuracy is substantially higher for spectral-edge eigenstates than for mid-spectrum eigenstates, providing a new quantitative measure of information content in many-body quantum states.
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Exploring the limit of the Lattice-Bisognano-Wichmann form describing the Entanglement Hamiltonian: A quantum Monte Carlo study
A QMC-based framework tests the lattice-Bisognano-Wichmann ansatz for reconstructing entanglement Hamiltonians in 2D systems without Lorentz invariance or translational symmetry, finding good accuracy for ordinary boundaries.
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Dynamics of entanglement asymmetry for space-inversion symmetry of free fermions on honeycomb lattices
Entanglement asymmetry for space-inversion symmetry of free fermions on honeycomb lattices exhibits nonanalytic dependence on energy imbalance and persists after a quench to the symmetric point due to flat bands in certain geometries.