Teaching overview

Comments and suggestions are always welcome (teaching@mkliesch.eu).

Statistische Mechanik (Winter 19/20)

  • Grundlagen der Wahrscheinlichkeitstheorie

  • Informationsmaße

  • Jaynes'sches Prinzip

  • Ensembles der Statistischen Physik

  • Ideale Quantengase, Bose-Einstein-Kondensation

  • Thermodynamik (Potentiale, Eulergleichung, Gibbs-Duhem-Relation, Maxwellbeziehungen, thermodynamische Prozesse, Hauptsätze)

  • Phasenübergänge (Van-der-Waals-Gleichung, Ising-Modell, kritische Phänomene)

Ein Skript zur Vorlesung kann hier gefunden werden.

Group seminar: Theoretical quantum science and technology (every term)

Group members will present and discuss their latest progress and results and give tutorial talks. Some talks will also be given by external visitors.

Interested students are always very welcome.

Click here for details.

Course: Characterization and verification of quantum simulations (summer 19)

  • Quantum state tomography

  • Fidelity estimation and certification of quantum states

  • Randomized benchmarking for quantum dynamics

  • Quantum process tomography

Click here for details

…and here for lecture notes.

Course: Machine learning in quantum physics (winter 18/19)

Quantum machine learning is an emerging research field. This first course focuses on the subfield where classical machine learning is applied in quantum physics. The goal of this course is to provide the students with the necessary skills to understand the main ideas and some details of the ongoing research.

Click here for details.