Bayesian optimization with Gaussian process surrogate accelerates numerical calibration of Mølmer-Sørensen gate parameters, with performance tied to quantum projection noise.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
quant-ph 2verdicts
UNVERDICTED 2representative citing papers
Ensemble quantum token protocol benchmarked on IBM processors achieves bank acceptance of forged tokens below 10^{-22} while accepting legitimate tokens above 0.999.
citing papers explorer
-
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.
-
Ensemble-Based Quantum Token Protocol Benchmarked on IBM Quantum Processors
Ensemble quantum token protocol benchmarked on IBM processors achieves bank acceptance of forged tokens below 10^{-22} while accepting legitimate tokens above 0.999.