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.
Tunable Coupling Scheme for Implementing High-Fidelity Two-Qubit Gates
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A review summarizing superconducting qubit types, DiVincenzo criteria implementations, coherence limits from defects, and large-scale integration strategies for quantum computing.
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Review of Superconducting Qubit Devices and Their Large-Scale Integration
A review summarizing superconducting qubit types, DiVincenzo criteria implementations, coherence limits from defects, and large-scale integration strategies for quantum computing.