# Quantum control in the presence of strongly coupled non-Markovian noise

arXiv: https://arxiv.org/abs/2404.19251 · HTML: https://mlsanity.com/paper/2404.19251/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Apr 2024.

## Built on by (most cited first, 3 of 3)

- [Singularity-free dynamical invariants-based quantum control](https://mlsanity.com/paper/2510.15340/) (2025)
- [Model-free Distortion Canceling and Control of Quantum Devices](https://mlsanity.com/paper/2407.09877/) (2024)
- [Bayesian quantum sensing using graybox machine learning](https://mlsanity.com/paper/2601.17465/) (2026)

## Abstract (arXiv)

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.