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.
Ibarra-Garc´ ıa-Padilla, H
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
Three Transformer backflow fermionic wave functions for the finite-doping Hubbard model converge, after accuracy improvements, to the same state with coexisting superconducting and stripe orders, demonstrating that variational energy is insufficient to identify the ground state.
citing papers explorer
-
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.
-
Beyond Variational Bias: Resolving Intertwined Orders in the Hubbard Model
Three Transformer backflow fermionic wave functions for the finite-doping Hubbard model converge, after accuracy improvements, to the same state with coexisting superconducting and stripe orders, demonstrating that variational energy is insufficient to identify the ground state.