This module handbook serves to describe contents, learning outcome, methods and examination type as well as linking to current dates for courses and module examination in the respective sections.
IN2388 is a semester module in English language at Bachelor’s level and Master’s level which is offered irregular.
This Module is included in the following catalogues within the study programs in physics.
- Catalogue of non-physics elective courses
|Total workload||Contact hours||Credits (ECTS)|
|150 h||60 h||5 CP|
Content, Learning Outcome and Preconditions
- Mathematical approximation theory
- Backpropagation through tensor network operations
- Simulating strongly correlated quantum systems and digital quantum computers
- Probability distribution sampling using tensor networks
• MA0902 Analysis for Informatics
• IN0018 Discrete Probability Theory
• Knowledge about quantum mechanics or computing helpful (but not a formal prerequisite)
Courses, Learning and Teaching Methods and Literature
Courses and Schedule
|VI||4||Tensor Networks (IN2388)||Huang, Q. Lopez Gutierrez, I. Mendl, C.||
Mon, 10:00–12:00, virtuell
Thu, 10:00–12:00, virtuell
Learning and Teaching Methods
M. Espig, W. Hackbusch, S. Handschuh, R. Schneider: Optimization problems in contracted tensor networks. Comput. Visual Sci. 14, 271 (2011)
U. Schollwöck: The density-matrix renormalization group in the age of matrix product states. Annals of Physics 326, 96 (2011)
R. Orús: Tensor networks for complex quantum systems. Nature Reviews Physics 1, 538 (2019)
J. Haegeman, Ch. Lubich, I. Oseledets, B. Vandereycken, F. Verstraete: Unifying time evolution and optimization with matrix product states. Phys. Rev. B 94, 165116 (2016)
L. Vanderstraeten, J. Haegeman, F. Verstraete: Tangent-space methods for uniform matrix product states. SciPost Phys. Lect. Notes 7 (2019)
H.-J. Liao, J.-G. Liu, L. Wang, T. Xiang: Differentiable programming tensor networks. Phys. Rev. X 9, 031041 (2019)
Description of exams and course work
There is a possibility to take the exam in the following semester.