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.
Otterpohl , author P
2 Pith papers cite this work. Polarity classification is still indexing.
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quant-ph 2years
2025 2verdicts
UNVERDICTED 2representative citing papers
A greybox framework combining whitebox physics with a neural-network blackbox trained on synthetic data achieves over 90% gate fidelity for a qubit under non-Markovian noise.
citing papers explorer
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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.
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Machine Learning-Aided Optimal Control of a Qubit Subjected to External Noise
A greybox framework combining whitebox physics with a neural-network blackbox trained on synthetic data achieves over 90% gate fidelity for a qubit under non-Markovian noise.