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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A protocol leverages the disordered phase to set unbiased boundary configurations in finite Rydberg arrays, stabilizing bulk-like quantum order in 1D and 2D simulations.
A protocol extracts scaling dimensions of d=3 CFTs from the spectrum of qubit Hamiltonians on polyhedral lattices, achieving few-percent accuracy on the 3D Ising model with 20 qubits.
Dual-parameter modulation of detuning and Rabi frequency broadens the dynamical freezing regime in interacting Rydberg arrays by coherently canceling interaction-induced absorption pathways identified through perturbative Floquet analysis.
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Qubit discretizations of d=3 conformal field theories
A protocol extracts scaling dimensions of d=3 CFTs from the spectrum of qubit Hamiltonians on polyhedral lattices, achieving few-percent accuracy on the 3D Ising model with 20 qubits.