Neural quantum states with a tailored 3D convolutional architecture simulate quench dynamics up to 1000 qubits and verify the 3D quantum Kibble-Zurek mechanism with RG-derived logarithmic corrections and data collapse.
Hornik, Approximation capabilities of multilayer feed- forward networks, Neural Networks4, 251 (1991)
2 Pith papers cite this work. Polarity classification is still indexing.
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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.
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Real-time Dynamics in 3D for up to 1000 Qubits with Neural Quantum States: Quenches and the Quantum Kibble--Zurek Mechanism
Neural quantum states with a tailored 3D convolutional architecture simulate quench dynamics up to 1000 qubits and verify the 3D quantum Kibble-Zurek mechanism with RG-derived logarithmic corrections and data collapse.
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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.