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

TL;DR

This paper introduces a data-driven quantum control method using graybox models that effectively manages strongly coupled non-Markovian noise, outperforming traditional approaches in fidelity for quantum systems.

Contribution

It presents a novel graybox machine learning approach for quantum control that handles complex noise without requiring precise models.

Findings

Achieves high-fidelity quantum control under complex noise conditions.

Demonstrates universal applicability to all open finite-dimensional quantum systems.

Outperforms traditional control methods significantly.

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.