HAML meta-learns a mapping from control inputs and device parameters to effective two-qubit Hamiltonian coefficients via simulation training, then adapts online with few measurements, recovering coefficients where Schrieffer-Wolff perturbation theory fails.
Quantum optimal control of superconducting qubits based on machine-learning characterization
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A comprehensive review organizing progress at the AI-quantum information intersection from both directions.
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Data-Driven Hamiltonian Reduction for Superconducting Qubits via Meta-Learning
HAML meta-learns a mapping from control inputs and device parameters to effective two-qubit Hamiltonian coefficients via simulation training, then adapts online with few measurements, recovering coefficients where Schrieffer-Wolff perturbation theory fails.
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When AI meets quantum information: A comprehensive review
A comprehensive review organizing progress at the AI-quantum information intersection from both directions.