Tensor Networks 2022 – Overview
Prof. Dr. Jan von Delft, LS Theoretical solid state physics
Lecture: Wednesday 12:15 - 13:45 , Thursday 14:15 - 15:45 (hybrid = classroom + zoom)
Tutorial: Tuesday 12:15 - 13:45 (hybrid)
(On the first Tuesday of the semester, 26.04.22, there will be a lecture instead of a tutorial.)
Lecturer: Prof. Dr. Jan von Delft
Tutor: Andreas Gleis, Jheng-Wei Li, Jeongmin Shim
During the last two decades, tensor networks have emerged as a powerful new language for encoding the wave functions of quantum many-body states, and the operators acting on them, in terms of contractions of tensors. Insights from quantum information theory have led to highly efficient and accurate tensor network representations for a variety of situations, particularly for one- and two-dimensional (1d, 2d) systems. For these, tensor network-based approaches rank among the most accurate and reliable numerical methods currently available.
This course offers an introduction to tensor network-based numerical methods, including
- the density matrix renormalization group (DMRG) for 1d quantum lattice models,
- the numerical renormalization group (NRG) for quantum impurity models,
- pair-wise entangled pair states (PEPS) for 2d quantum lattice models,
- the tensor renormalization group (TRG) for 2d classical lattice models,
- the exponential TRG (XTRG) for 1d models at finite temperature.
The course website is run via Moodle.
To get access to lecture materials (lecture notes/tutorials/exam), please visit
and proceed as follows:
Klick on "Nutzungsbedingungen bestätigen" (at the very bottom)
Selbsteinschreibung (Teilnehmer/in): klick on "Weiter".
Klick on "LMU Kennung" or "TUM Kennung".
Bestätigen: Klick on "Ja" (at the very bottom).
Selbsteinschreibung (Teilnehmer/in): Enter the access key: 22tensornetworks
After these steps, you should have access to the Moodle-Website of the course 'Tensor Networks 2021'. All further details can be found there.
Access to the Moodle Course website is possible only if you are immatriculated at LMU and have an LMU account. Access without an LMU account is possible for a previous version of this course: Tensor Networks 2021.