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 quantitatively. 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, which only differs from the original Ansatz by the method of antisymmetrization, is a strict generalization of the original FermiNet architecture, despite the use of fewer parameters. Our approach shares several advantages with the FermiNet: the use of a neural network removes the need for an underlying basis set; and the flexibility 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：理解超流性仍是凝聚态物理学的一个主要目标。本文利用近期发展的费米子神经网络（FermiNet）波函数变分假设进行变分蒙特卡洛计算，以应对这一挑战。我们研究了具有强短程二体相互作用、已知具有超流基态但难以定量描述的单位费米气体系统。我们论证了FermiNet变分假设在研究单位费米气体时的关键局限性，并提出了一种简单修正方法，该方法显著优于原始FermiNet，获得了高精度结果。我们从数学上证明，这一仅通过反对称化方法区别于原始假设的新变分假设，尽管参数更少，却是原始FermiNet架构的严格推广。该方法与FermiNet共享若干优势：神经网络的运用消除了对基组的需求；网络的灵活性在变分量子蒙特卡洛框架内可获得极高精度的结果，并能提供任意基态期望值的无偏估计。我们还讨论了如何将该方法扩展至研究其他超流体。