A new neural quantum state ansatz for bosons in the grand canonical ensemble achieves competitive variational energies in 1D and 2D systems and provides access to one-body reduced density matrices.
Neural- network quantum states for many-body physics
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The Universal Neural Propagator is a single neural model trained self-supervised to predict time evolution in driven quantum many-body systems across arbitrary protocols and initial states.
Low-energy eigenstates of a spin chain allow a neural network to reconstruct the Hamiltonian accurately, while mid-spectrum eigenstates do not, defining a spectral 'learnability' gap.
citing papers explorer
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Neural network quantum states in the grand canonical ensemble
A new neural quantum state ansatz for bosons in the grand canonical ensemble achieves competitive variational energies in 1D and 2D systems and provides access to one-body reduced density matrices.
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Universal Neural Propagator: Learning Time Evolution in Many-Body Quantum Systems
The Universal Neural Propagator is a single neural model trained self-supervised to predict time evolution in driven quantum many-body systems across arbitrary protocols and initial states.
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Information in Many-body Eigenstates: A Question of Learnability
Low-energy eigenstates of a spin chain allow a neural network to reconstruct the Hamiltonian accurately, while mid-spectrum eigenstates do not, defining a spectral 'learnability' gap.