While machine learning holds great promise for quantum technologies, most current methods focus on predicting or controlling a specific quantum system. Meta-learning approaches, however, can adapt to new systems for which little data is available, by leveraging knowledge obtained from previous data associated with similar systems. In this paper, we meta-learn dynamics and characteristics of closed and open two-level systems, as well as the Heisenberg model. Based on experimental data of a Loss-DiVincenzo spin-qubit hosted in a Ge/Si core/shell nanowire for different gate voltage configurations, we predict qubit characteristics i.e. -factor and Rabi frequency using meta-learning. The algorithm we introduce improves upon previous state-of-the-art meta-learning methods for physics-based systems by introducing novel techniques such as adaptive learning rates and a global optimizer for improved robustness and increased computational efficiency. We benchmark our method against other meta-learning methods, a vanilla transformer, and a multilayer perceptron, and demonstrate improved performance.
View on arXiv@article{schorling2025_2503.10492,
title={ Meta-learning characteristics and dynamics of quantum systems },
author={ Lucas Schorling and Pranav Vaidhyanathan and Jonas Schuff and Miguel J. Carballido and Dominik Zumbühl and Gerard Milburn and Florian Marquardt and Jakob Foerster and Michael A. Osborne and Natalia Ares },
journal={arXiv preprint arXiv:2503.10492},
year={ 2025 }
}