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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Coupling to mesoscopic reservoirs generates temperature-increasing entropic barriers that suppress topological defect creation and transport, yielding three-regime correlation lengths in 1D Ising chains and double error reduction in finite-size 2D toric codes.
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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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Entropic Barriers and the Kinetic Suppression of Topological Defects
Coupling to mesoscopic reservoirs generates temperature-increasing entropic barriers that suppress topological defect creation and transport, yielding three-regime correlation lengths in 1D Ising chains and double error reduction in finite-size 2D toric codes.