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Tackling quantum many-body problems with (artificial) intelligence
Annabelle Bohrdt, Harvard University
New quantum simulation platforms provide an unprecedented microscopic perspective on the structure of strongly correlated quantum matter. This allows to revisit decade-old problems from a fresh perspective, such as the two-dimensional Fermi-Hubbard model, believed to describe the physics underlying high-temperature superconductivity. In order to fully use the experimental as well as numerical capabilities available today, we need to go beyond conventional observables, such as one- and two-point correlation functions. In this talk, I will give an overview of recent results on the Hubbard model obtained through novel analysis tools: using machine learning techniques to analyze quantum gas microscopy data allows us to take into account all available information without a potential bias by the choice of an observable and compare different theories on a microscopic level. I will introduce a novel, customized neural network architecture, which features full interpretability and thus enables direct physical insights. The analysis of data from quantum simulation experiments of the doped Fermi-Hubbard model with machine learning tools as well as through different higher-order correlations shows a qualitative change in behavior around 20% doping, consistent with condensed matter experiments on cuprate materials. As an outlook, I will discuss how our microscopic understanding of the low doping limit has led us to the discovery of a binding mechanism, which enables pairing of charge carriers at currently accessible experimental temperatures, thus paving the way for the study of pair formation in cold atom quantum simulators.