"Matrix product states for physics: Tensor Network Python (TeNPy)"

Abstract:

Tensor networks have been invented as a powerful technique to overcome the exponential Hilbert space growth for the classical simulation of Quantum Many Body system. Their success is motivated by the are law of the entanglement entropy, stating that ground states of gapped Hamiltonians have only limited entanglement. I will first introduce the basic ideas and a few exemplary algorithms for tensor networks. Then we will have a hands-on session with a examples and toy codes implementing the algorithms in just a few lines of Python to show how things work together, and how to setup equivalent simulations in the more powerful TeNPy library. Finally, I will briefly discuss how ideas from classical tensor networks simulations can be lifted to quantum circuits, e.g. for state preparation on NISQ devices.

Bio:

Johannes Hauschild did his PhD on tensor network simulations under the supervision of Frank Pollmann, started at the Max Planck Institute for complex physics in Dresden in 2015 and finished at Technical University Munich in 2019. During his PhD, he developed the Tensor Network Python (TeNPy) library, which has grown steady since. After his PhD, he was a Postdoc at University of California, Berkeley, before he moved back to TU Munich in 2022, where he became project coordinator for the theory consortium of the Munich quantum valley and IT admin of the local computing cluster besides continuing his research.

Instructions for students:

Please bring your laptops/tablets (fully charged), ideally with python and jupyter notebook or jupyter lab installed (conda install jupyter-lab numpy scipy).

Alternatively, you can use your favorite Python IDE supporting jupyter notebooks, e.g. Pycharm or visual studio code with addons).

If you don't manage to install jupyter, use https://colab.research.google.com