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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.
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Absence of a BCS-BEC crossover in the cuprate superconductors
Abstract We examine key aspects of the theory of the Bardeen–Cooper–Schrieffer (BCS) to Bose–Einstein condensation (BEC) crossover, focusing on the temperature dependence of the chemical potential,μ. We identify an accurate method of determining the change ofμin the cuprate high temperature superconductors from angle-resolved-photoemission data (along the ‘nodal’ direction), and show thatμvaries by less than a few percent of the Fermi energy over a range of temperatures from far below to several times above the superconducting transition temperature,Tc. This shows, unambiguously, that not only are these materials always on the BCS side of the crossover (which is a phase transition in thed-wave case), but are nowhere near the point of the crossover (where the chemical potential approaches the band bottom).
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- Award ID(s):
- 2132343
- PAR ID:
- 10415366
- Publisher / Repository:
- Nature Publishing Group
- Date Published:
- Journal Name:
- npj Quantum Materials
- Volume:
- 8
- Issue:
- 1
- ISSN:
- 2397-4648
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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