PSR-NQS makes recurrent neural quantum states scalable for variational Monte Carlo by using parallel scan recurrence, reaching accurate results on 52x52 two-dimensional lattices.
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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.
A shallow restricted Boltzmann machine variational Monte Carlo ansatz reproduces the main features of the adiabatic phase diagram and selected symmetry-broken insulating states for the one-dimensional Z2 Bose-Hubbard chain at half filling.
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Parallel Scan Recurrent Neural Quantum States for Scalable Variational Monte Carlo
PSR-NQS makes recurrent neural quantum states scalable for variational Monte Carlo by using parallel scan recurrence, reaching accurate results on 52x52 two-dimensional lattices.
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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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Benchmarking a restricted Boltzmann machine on the $\mathbb{Z}_2$ Bose-Hubbard chain in the adiabatic hard-core regime
A shallow restricted Boltzmann machine variational Monte Carlo ansatz reproduces the main features of the adiabatic phase diagram and selected symmetry-broken insulating states for the one-dimensional Z2 Bose-Hubbard chain at half filling.