18 citations · 18 across the 1 of their papers we have counts for
5 papers
Control of Stochastic Quantum Dynamics by Differentiable Programming
Frank Schäfer, Pavel Sekatski, Martin Koppenhöfer +2
Control of the stochastic dynamics of a quantum system is indispensable in fields such as quantum information processing and metrology. However, there is no general ready-made appr…
Interpretable and unsupervised phase classification
Julian Arnold, Frank Schäfer, Martin Žonda +1
Fully automated classification methods that yield direct physical insights into phase diagrams are of current interest. Here, we demonstrate an unsupervised machine learning method…
A differentiable programming method for quantum control
Frank Schäfer, Michal Kloc, Christoph Bruder +1
Optimal control is highly desirable in many current quantum systems, especially to realize tasks in quantum information processing. We introduce a method based on differentiable pr…
Unsupervised identification of topological order using predictive models
Eliska Greplova, Agnes Valenti, Gregor Boschung +3
Machine-learning driven models have proven to be powerful tools for the identification of phases of matter. In particular, unsupervised methods hold the promise to help discover ne…
Divergence of predictive model output as indication of phase transitions
Frank Schäfer, Niels Lörch
We introduce a new method to identify phase boundaries in physical systems. It is based on training a predictive model such as a neural network to infer a physical system's paramet…