Bayesian optimization with Gaussian process surrogate accelerates numerical calibration of Mølmer-Sørensen gate parameters, with performance tied to quantum projection noise.
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2026 2verdicts
UNVERDICTED 2representative citing papers
Hybrid quantum reservoir and projected kernel models report 37-62% MAE reductions versus classical baselines for multi-output energy time-series on NISQ hardware and simulators.
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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.
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Hybrid Quantum-Classical Machine Learning Algorithms for Multi-Output Time-Series Forecasting at Utility Scale
Hybrid quantum reservoir and projected kernel models report 37-62% MAE reductions versus classical baselines for multi-output energy time-series on NISQ hardware and simulators.