Machine Learning for Many Body Quantum Systems

Machine learning techniques can be used to grasp, reduce, and ultimately better understand the inherent complexity of many-body physical systems. A promising route for applying machine learning ideas to physics is by using artificial neural networks to describe quantum wave functions, which allows one to simulate interacting quantum models that would otherwise be very challenging.

The goal of this CCQ project is to research, deploy, and promote the systematic application of modern machine-learning methods to study a variety of fundamental problems in quantum physics. The resulting techniques will potentially benefit all fields where many-body effects are crucial, including condensed matter, ultra-cold atoms, and quantum computing devices.

Project Leaders: Antoine Georges, Anirvan Sengupta

Project Scientists: Anna Dawid, Dominik Kiese, Matija Medvidovic (Columbia), Andrew Millis, Christopher Roth, Agnes Valenti

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