We deploy machine learning methods to learn the dynamics of quantum systems.
Dominik Zietlow,
Georg Martius,
Paolo Mazza
(Institut für Theoretische Physik and Center for Quantum Science, Universität Tübingen),
Frederico Carollo
(Institut für Theoretische Physik and Center for Quantum Science, Universität Tübingen),
Sabine Andergassen
(Institut für Theoretische Physik and Center for Quantum Science, Universität Tübingen),
Igor Lesanovsky
(Institut für Theoretische Physik and Center for Quantum Science, Universität Tübingen)
We are interested in deploying machine learning methods to improve the understanding of quantum systems. Given a physical system in which a subsystem is embedded in a larger system, i.e. a bath, we used neural networks to learn the generator of the subsystem's dynamics. The network was trained with pre-simulated trajectories of Bloch vectors arising from different initial states.