Regensburg 2022 – wissenschaftliches Programm
Bereiche | Tage | Auswahl | Suche | Aktualisierungen | Downloads | Hilfe
TT: Fachverband Tiefe Temperaturen
TT 31: Superconducting Electronics and Cryogenics: Poster Session
TT 31.20: Poster
Donnerstag, 8. September 2022, 15:00–18:00, P1
Towards machine learning models for NISQ processors — •Andras Di Giovanni1, Hannes Rotzinger1,2, Alexey V. Ustinov1,2, Adrian Aasen3, Moritz Reh3, and Martin Gärttner3 — 1Karlsruhe Institute for Technology, Karlsruhe, Germany — 2Institut für QuantenMaterialien und Technologien, Karlsruhe, Germany — 3Heidelberg University, Heidelberg, Germany
Quantum simulators promise insights into quantum many-body problems in regimes where classical simulation methods hit a complexity wall. One challenge towards this goal is to develop well characterized building blocks that allow to scale up system sizes while conserving reliability in terms of errors. A promising platform for building such NISQ (noisy, intermediate-scale quantum) devices are superconducting quantum circuits. Our goal is to characterize small scale quantum processors with minimal experimental and post-processing cost. For this we implement schemes for machine learning assisted adaptive Bayesian tomography and apply them to experimental data obtained from a prototype few-qubit superconducting chip.