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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Cyclic inverse design on athermal disordered sphere packings produces an emergent marginally absorbing manifold that encodes return-point memory of the training range through gradient discontinuities.
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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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Learning by training: emergent return-point memory from cyclically tuning disordered sphere packings
Cyclic inverse design on athermal disordered sphere packings produces an emergent marginally absorbing manifold that encodes return-point memory of the training range through gradient discontinuities.