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Metalearning characteristics and dynamics of quantum systems
Lucas Schorling (University of Oxford), Pranav Vaidhyanathan (University of Oxford), Jonas Schuff (University of Oxford), Miguel J. Carballido (University of Basel), Dominik M. Zumbühl (University of Basel), Gerard J Milburn (The University of Queensland), Florian Marquardt (Friedrich-Alexander-Universität Erlangen-Nürnberg, Max Planck Institute for the Science of Light), Jakob Foerster (University of Oxford), Michael A. Osborne (University of Oxford), Natalia Ares (University of Oxford)
Abstract: While machine learning holds great promise for quantum technologies, most current methods focus on predicting or controlling a specific quantum system. Metalearning approaches, however, can adapt to new systems for which little data are available, by leveraging knowledge obtained from previous data associated with similar systems. In this paper, we metalearn 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., g factor and Rabi frequency using metalearning. The algorithm we introduce improves upon previous state-of-the-art metalearning methods for physics-based systems by introducing techniques such as adaptive learning rates and a global optimizer for improved robustness and increased computational efficiency. We benchmark our method against other metalearning methods, a vanilla transformer, and a multilayer perceptron, and demonstrate improved performance.