Tackling old problems with new tools: from frustration to pairing in strongly correlated many body systems
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 and compare different theories on a microscopic level. In particular, we consider Anderson's RVB paradigm to the geometric string theory, which takes the interplay of spin and charge degrees of freedom microscopically into account. The analysis of data from quantum simulation experiments of the doped Fermi-Hubbard model shows a qualitative change in behavior around 20% doping, up to where the geometric string theory captures the experimental data better. This microscopic understanding of the low doping limit has led us to the discovery of a binding mechanism in so-called mixed-dimensional systems, which has enabled the observation of pairing of charge carriers in cold atom experiments.
Intriguingly, mixed-dimensional systems exhibit similar features as the original two-dimensional model, e.g. a stripe phase at low temperatures. At intermediate to high temperatures, we use Hamiltonian reconstruction tools to quantify the frustration in the spin sector induced by the hole motion and find that the spin background is best described by a highly frustrated J1-J2 model.