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- Title
Neural-network quantum states for ultra-cold Fermi gases.
- Authors
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. The theoretical description of ultra-cold Fermi gases is challenging due to the presence of strong, short-ranged interactions. This work introduces a Pfaffian-Jastrow neural-network quantum state that outperforms existing Slater-Jastrow frameworks and diffusion Monte Carlo methods.
- Subjects
BOSE Corp.; QUANTUM states; BCS-BEC crossover; MONTE Carlo method; ELECTRON gas; BOSE-Einstein condensation; PARTICLE interactions
- Publication
Communications Physics, 2024, Vol 7, Issue 1, p1
- ISSN
2399-3650
- Publication type
Article
- DOI
10.1038/s42005-024-01613-w