Summer 2021

Seminar: Machine Learning Quantum Matter

The field of machine learning and artificial intelligence has flourished over the past years, fueled by a new generation of algorithms, ever growing resources of computational power, and the availability of “big data” in many fields. These technological advances have a strong impact also on the natural sciences — just recently, for example, the AlphaFold machine learning algorithm set new standards in the fundamentally difficult problem of protein folding. These developments have motivated researchers across all fields to explore machine learning techniques as an extension of their numerical toolbox.

In this seminar, we will concentrate mostly on applications of machine learning (ML) in condensed matter physics. This is a perfect match: The core functionality of ML — pattern recognition and dimensional reduction— might be precisely what is needed to cure the “curse of dimensionality”, i.e. the exponential explosion of states in a Hilbert space, while, e.g., typical wavefunctions of quantum many-body systems are known to be “relatively simple”, occupying only a few important basis states. This suggests novel routes for data analysis and numerical simulation, but also inspires new conceptional developments. The talks will provide a basic introduction to various machine learning techniques by considering exemplary applications in condensed matter physics. A tentative list of topics is included below.

Organizatorial Info

The seminar will take place on Mondays at 4pm.


26.4.Hands-on tutorial: Supervised learning with neural networksMarkus
3.5.Supervised learning of many-body phasesNikkinMarkus
10.5.Unsupervised learning of phase transitionsSimon-DominikKai
31.5.Generative modelling for statistical physicsRajatChae-Yeun
7.6.Neural network wave functionsAna-LuizaChae-Yeun
14.6.Neural network quantum state tomographyMoChae-Yeun
21.6.Machine learning to analyze experimental dataBernhardMarkus
28.6.Reinforcement learning for quantum controlJasonMarkus
5.7.Reinforcement learning for quantum error correctionSakshiKai
12.7.Quantum machine learningJuliusChae-Yeun
19.7.Discovering physical conceptsJiangtianMarkus