REVIEW 3 cited by
Quantum control in the presence of strongly coupled non-Markovian noise
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
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.
Forward citations
Cited by 3 Pith papers
-
Singularity-free dynamical invariants-based quantum control
Invariant-based qubit control is made singularity-free by trajectory splitting, and the pulse family is optimized against non-Markovian noise via whitebox or graybox models.
-
Machine Learning-aided Optimal Control of a noisy qubit
A transformer-based greybox surrogate trained on Monte Carlo data predicts single-qubit gate fidelities under telegraph and Ornstein-Uhlenbeck noise, and gradient-based control with this emulator yields simulated fide...
-
Quantum Engineering of Qudits with Interpretable Machine Learning
A graybox machine-learning framework is extended to qudits and demonstrated on simulated qutrits, with a local Taylor expansion for interpreting the learned noise dynamics.
Discussion (0). Sign in to comment.