Machine learning effective models for quantum systems

Understand

The construction of good effective models is an essential part of understanding and simulating complex systems in many areas of science.

• It is a particular challenge for correlated many body quantum systems displaying emergent physics.
• We propose a machine learning approach that optimizes an effective model based on an estimation of its partition function.
• The success of the method is demonstrated by application to the single impurity Anderson model and double quantum dots, where non-perturbative results are obtained for the old problem of mapping to effective Kondo models.