Kim, JanePescia, Gabriel MariaFore, BryceNys, JannesCarleo, GiuseppeGandolfi, StefanoHjorth-Jensen, MortenLovato, Alessandro2024-05-162024-05-162024-05-162024-05-0810.1038/s42005-024-01613-whttps://infoscience.epfl.ch/handle/20.500.14299/207994WOS:001216180700001Ultra-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.Physical SciencesSystemstext::journal::journal article::research article