Engineering topics
Bohrdt, Annabelle
Publications and source records attributed to Bohrdt, Annabelle.
Microscopic evolution of doped Mott insulators from polaronic metal to Fermi liquid
From polarons to a Fermi liquid Superconductivity in the cuprates emerges by doping an antiferromagnetic “parent” state with holes or electrons. With increased doping, antiferromagnetism gives way to unconventional superconductivity, and the system eventually becomes a Fermi liquid. Koepsell et al . simulated this progression using cold, strongly interacting lithium-6 atoms trapped in an optical lattice. Although the equivalent ordered phases are not yet reachable at the experimentally available temperatures, the researchers were able to measure multipoint spin and hole correlations over a wide range of hole doping. The evolution of these correlators with doping revealed a crossover from a polaronic regime to a Fermi liquid. —JS
Higher-order spin-hole correlations around a localized charge impurity
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Correlator convolutional neural networks as an interpretable architecture for image-like quantum matter data
Image-like data from quantum systems promises to offer greater insight into the physics of correlated quantum matter. However, the traditional framework of condensed matter physics lacks principled approaches for analyzing such data. Machine learning models are a powerful theoretical tool for analyzing image-like data including many-body snapshots from quantum simulators. Recently, they have successfully distinguished between simulated snapshots that are indistinguishable from one and two point correlation functions. Thus far, the complexity of these models has inhibited new physical insights from such approaches. Here, we develop a set of nonlinearities for use in a neural network architecture that discovers features in the data which are directly interpretable in terms of physical observables. Applied to simulated snapshots produced by two candidate theories approximating the doped Fermi-Hubbard model, we uncover that the key distinguishing features are fourth-order spin-charge correlators. Our approach lends itself well to the construction of simple, versatile, end-to-end interpretable architectures, thus paving the way for new physical insights from machine learning studies of experimental and numerical data.