Derives KL_W and R_DV regularizers from Girsanov's theorem that reduce infidelity by up to 50% and improve robustness to noise mismatch on single- and multi-qubit benchmarks including an IBM Kingston calibration.
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
Machine learning classifies six Markovian and non-Markovian noise classes in two-qubit systems with over 94% accuracy using only final transfer efficiencies from a coherent population transfer protocol under three driving conditions.
citing papers explorer
-
QMaxCal: Path-Space Regularization for Open Quantum Control via Girsanov's Theorem
Derives KL_W and R_DV regularizers from Girsanov's theorem that reduce infidelity by up to 50% and improve robustness to noise mismatch on single- and multi-qubit benchmarks including an IBM Kingston calibration.
-
Detection of noise correlations in two qubit systems by Machine Learning
Machine learning classifies six Markovian and non-Markovian noise classes in two-qubit systems with over 94% accuracy using only final transfer efficiencies from a coherent population transfer protocol under three driving conditions.