Quantum control in the presence of strongly coupled non-Markovian noise
CoRR(2024)
摘要
Controlling quantum systems under correlated non-Markovian noise,
particularly when strongly coupled, poses significant challenges in the
development of quantum technologies. Traditional quantum control strategies,
heavily reliant on precise models, often fail under these conditions. Here, we
address the problem by utilizing a data-driven graybox model, which integrates
machine learning structures with physics-based elements. We demonstrate
single-qubit control, implementing a universal gate set as well as a random
gate set, achieving high fidelity under unknown, strongly-coupled non-Markovian
non-Gaussian noise, significantly outperforming traditional methods. Our method
is applicable to all open finite-dimensional quantum systems, regardless of the
type of noise or the strength of the coupling.
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