Understanding superfluidity remains a major goal of condensed matter physics. Here we tackle this challenge utilizing the recently developed Fermionic neural network (FermiNet) wave function Ansatz for variational Monte Carlo calculations. We study the unitary Fermi gas, a system with strong, short-range, two-body interactions known to possess a superfluid ground state but difficult to describe quantitively. We demonstrate key limitations of the FermiNet Ansatz in studying the unitary Fermi gas and propose a simple modification that outperforms the original FermiNet significantly, giving highly accurate results. We prove mathematically that the new Ansatz is a strict generalization of the original FermiNet architecture, despite the use of fewer parameters. Our approach shares several advantanges with the FermiNet: the use of a neural network removes the need for an underlying basis set; and the flexiblity of the network yields extremely accurate results within a variational quantum Monte Carlo framework that provides access to unbiased estimates of arbitrary ground-state expectation values. We discuss how the method can be extended to study other superfluids.

翻译：理解超流性仍然是凝聚态物理学的主要目标之一。在此，我们利用近期发展的费米子神经网络（FermiNet）波函数拟设，在变分蒙特卡罗计算中应对这一挑战。我们研究具有强短程两体相互作用的幺正费米气体，该系统已知具有超流基态，但难以进行定量描述。我们证明了FermiNet拟设在研究幺正费米气体时存在关键局限性，并提出了一个简单改进，该改进显著优于原始FermiNet，获得了高度精确的结果。我们在数学上证明，新拟设是原始FermiNet架构的严格推广，尽管使用了更少的参数。我们的方法与FermiNet共享若干优势：使用神经网络消除了对底层基组的需求；网络的灵活性使得在变分量蒙特卡罗框架内可获得任意基态期望值的无偏估计，从而产生极其精确的结果。我们还讨论了如何将该方法扩展至研究其他超流体。