PIRSA:24030116

Video URL

https://pirsa.org/24030116

Neural-network quantum states for ultra-cold Fermi gases

Kim, J. (2024). Neural-network quantum states for ultra-cold Fermi gases. Perimeter Institute for Theoretical Physics. https://pirsa.org/24030116

Kim, Jane. Neural-network quantum states for ultra-cold Fermi gases. Perimeter Institute for Theoretical Physics, Mar. 15, 2024, https://pirsa.org/24030116 

          @misc{ scivideos_PIRSA:24030116,
            doi = {10.48660/24030116},
            url = {https://pirsa.org/24030116},
            author = {Kim, Jane},
            keywords = {Other Physics},
            language = {en},
            title = {Neural-network quantum states for ultra-cold Fermi gases},
            publisher = {Perimeter Institute for Theoretical Physics},
            year = {2024},
            month = {mar},
            note = {PIRSA:24030116 see, \url{https://scivideos.org/index.php/pirsa/24030116}}
          }

Jane Kim Michigan State University (MSU)

Talk numberPIRSA:24030116
DOI10.48660/24030116
Source RepositoryPIRSA
Collection
Talk Type Scientific Series
Subject

Abstract

Ultra-cold Fermi gases exhibit a rich array of quantum mechanical properties, including the transition from a fermionic superfluid Bardeen-Cooper-Schrieffer (BCS) state to a bosonic superfluid Bose-Einstein condensate (BEC), which can be precisely probed experimentally. However, accurately describing these properties poses significant theoretical challenges due to strong pairing correlations and non-perturbative interactions. In this talk, I will discuss our recent development—a Pfaffian-Jastrow neural-network quantum state equipped with a message-passing architecture, designed to efficiently capture pairing and backflow correlations. We benchmark our approach against existing Slater-Jastrow frameworks and state-of-the-art diffusion Monte Carlo methods. Analysis of pair distribution functions and pairing gaps reveals the emergence of strong pairing correlations around unitarity. We demonstrate that transfer learning stabilizes the training process in the presence of strong, short-ranged interactions, allowing for an effective exploration of the BCS-BEC crossover region. Our findings highlight the potential of neural-network quantum states as a promising strategy for investigating ultra-cold Fermi gases.

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