Classical RNNs trained on small instances provide parameter initializations for QAOA and VQE that reduce total optimization iterations and generalize across problem sizes.
Suzuki, Physics Letters A 146, 319 (1990)
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Explicit symplectic integrators of arbitrary even orders are constructed for massive point vortex dynamics in binary BEC mixtures; they preserve angular momentum exactly and nearly conserve the Hamiltonian without drift.
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Learning to learn with quantum neural networks via classical neural networks
Classical RNNs trained on small instances provide parameter initializations for QAOA and VQE that reduce total optimization iterations and generalize across problem sizes.