Neural-network quantum states for ultra-cold Fermi gases

Journal Article · 07 May 2024 · Communications Physics

DOI: https://doi.org/10.1038/s42005-024-01613-w · OSTI ID:2346271

Kim, Jane ; Pescia, Gabriel ; Fore, Bryce ; Nys, Jannes ; Carleo, Giuseppe ; Gandolfi, Stefano ; Hjorth-Jensen, Morten; Lovato, Alessandro

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). While these properties can be precisely probed experimentally, accurately describing them poses significant theoretical challenges due to strong pairing correlations and the non-perturbative nature of particle interactions. In this work, we introduce a Pfaffian-Jastrow neural-network quantum state featuring a message-passing architecture to efficiently capture pairing and backflow correlations. We benchmark our approach on existing Slater-Jastrow frameworks and state-of-the-art diffusion Monte Carlo methods, demonstrating a performance advantage and the scalability of our scheme. We show that transfer learning stabilizes the training process in the presence of strong, short-ranged interactions, and allows 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.

• Sponsoring Organization: USDOE
• Grant/Contract Number: AC02-06CH11357
• OSTI ID: 2346271
• Alternate ID(s): OSTI ID: 2432312; OSTI ID: 2526245
• Journal Information: Communications Physics, Journal Name: Communications Physics Journal Issue: 1 Vol. 7; ISSN 2399-3650
• Publisher: Nature Publishing GroupCopyright Statement
• Country of Publication: United Kingdom
• Language: English