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DY: Fachverband Dynamik und Statistische Physik

DY 2: Focus Session: Physics Meets ML I – Machine Learning for Complex Quantum Systems (joint session TT/DY)

DY 2.5: Vortrag

Montag, 27. März 2023, 11:45–12:00, HSZ 03

Machine learning optimization of Majorana hybrid nanowires — •Matthias Thamm and Bernd Rosenow — Institut für Theoretische Physik, Universität Leipzig

As the complexity of quantum systems such as quantum bit arrays increases, efforts to automate expensive tuning are increasingly worthwhile. We investigate machine learning based tuning of gate arrays using the CMA-ES algorithm for the case study of Majorana wires with strong disorder. We find that the algorithm is able to efficiently improve the topological signatures, learn intrinsic disorder profiles, and completely eliminate disorder effects. For example, with only 20 gates, it is possible to fully recover Majorana zero modes destroyed by disorder by optimizing gate voltages.