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Creators/Authors contains: "Demler, Eugene"

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  1. ; ; ; ; ( , Nature Physics)
    Abstract Interacting many-body systems in reduced-dimensional settings, such as ladders and few-layer systems, are characterized by enhanced quantum fluctuations. Recently, two-dimensional bilayer systems have sparked considerable interest because they can host unusual phases, including unconventional superconductivity. Here we present a theoretical proposal for realizing high-temperature pairing of fermions in a class of bilayer Hubbard models. We introduce a general and highly efficient pairing mechanism for mobile charge carriers in doped antiferromagnetic Mott insulators. The pairing is caused by the energy that one charge gains when it follows the path created by another charge. We show that this mechanism leads tomore »the formation of highly mobile but tightly bound pairs in the case of mixed-dimensional Fermi–Hubbard bilayer systems. This setting is closely related to the Fermi–Hubbard model believed to capture the physics of copper oxides, and can be realized in currently available ultracold atom experiments.« less
    Free, publicly-accessible full text available June 1, 2023
  2. ; ; ; ; ; ; ( , Physical Review B)
    Free, publicly-accessible full text available January 1, 2023
  3. ; ; ; ; ; ; ( , Physical Review Research)
    Free, publicly-accessible full text available January 1, 2023
  4. ; ; ; ; ; ( , Physical Review X)
    Free, publicly-accessible full text available December 1, 2022
  5. ; ; ; ; ; ; ; ; ; ( , Nature Communications)
    null (Ed.)
    Abstract 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 discoversmore »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.« less
    Free, publicly-accessible full text available December 1, 2022
  6. ; ; ; ; ; ( , Physical Review Research)
    Free, publicly-accessible full text available September 1, 2022
  7. ; ; ( , Physical Review Letters)
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  8. ; ; ; ; ( , Physical Review Research)
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  9. ; ( , Physical Review B)
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  10. ; ; ; ; ( , Physical Review Letters)
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