Machine Learning-Aided Optimal Control of a Qubit Subjected to External Noise

TL;DR

This paper introduces a machine-learning-enhanced framework for quantum optimal control that effectively manages non-Markovian noise, achieving high gate fidelities in open quantum systems.

Contribution

It combines physical models with neural networks trained on synthetic data to improve control protocols for noisy quantum systems, capturing complex noise effects.

Findings

Achieves gate fidelities above 90% under specific noise models

Successfully captures non-Markovian noise effects

Discusses critical issues of the approach

Abstract

We apply a machine-learning-enhanced greybox framework to a quantum optimal control protocol for open quantum systems. Combining a whitebox physical model with a neural-network blackbox trained on synthetic data, the method captures non-Markovian noise effects and achieves gate fidelities above 90% under Random Telegraph and Ornstein-Uhlenbeck noise. Critical issues of the approach are discussed.