Bulletin of the American Physical Society
2023 APS March Meeting
Volume 68, Number 3
Las Vegas, Nevada (March 5-10)
Virtual (March 20-22); Time Zone: Pacific Time
Session F62: Machine Learning for Quantum Matter II
8:00 AM–10:48 AM,
Tuesday, March 7, 2023
Room: Room 417
Sponsoring
Unit:
DCOMP
Chair: Iris Mowgood, Chapman University
Abstract: F62.00007 : Studying the Superfluid Ground-State of the Unitary Fermi Gas with Fermionic Neural Networks.*
9:36 AM–9:48 AM
Presenter:
Wan Tong Lou
(Imperial College London)
Authors:
Wan Tong Lou
(Imperial College London)
Gino W Cassella
(Imperial College London)
Halvard Sutterud
(Imperial College London)
W Matthew C Foulkes
(Imperial College London)
Johannes Knolle
(TU Munich, Germany)
David Pfau
(DeepMind)
James Spencer
(DeepMind)
We study the unitary Fermi gas (UFG), a system with strong, short-range two body interactions which possesses a superfluid ground state. We demonstrate key limitations of the FermiNet Ansatz in studying the UFG. We propose a simple modification, which outperforms the original FermiNet by a significant amount and is able to obtain comparable, if not better accuracy than the FN-DMC results using BCS trial wavefunction in the same system. We prove mathematically that this new Ansatz is a strict generalization of the original FermiNet architecture, despite the use of fewer parameters.
Our approach shares several key properties with FermiNet: the use of a neural network removes the need for an underlying basis set and its flexiblity enables extremely accurate ground state estimates within a VMC approach. A key advantage of VMC over DMC is that it provides access to unbiased estimates of expectation values such as the density matrices. The method we present here could be extended to study other s-wave superfluids.
*This work is supported by the President Scholarship scheme from Imperial College London.
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