Bayesian optimization with Gaussian process surrogate accelerates numerical calibration of Mølmer-Sørensen gate parameters, with performance tied to quantum projection noise.
Booker, Design and analysis of computer experiments, in7th AIAA/USAF/NASA/ISSMO Symposium on Mul- tidisciplinary Analysis and Optimization(1998) p
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Active Learning for Calibrating Entangling Gates via Surrogate-Based Optimization
Bayesian optimization with Gaussian process surrogate accelerates numerical calibration of Mølmer-Sørensen gate parameters, with performance tied to quantum projection noise.