Description
Accurate simulations of the Hubbard model are crucial to understanding strongly correlated phenomena, where small energy differences between competing orders demand high numerical precision. In this talk, I will present how Neural Quantum States are used to probe the strongly coupled and underdoped regime of the square-lattice Hubbard model. We systematically compare the Hidden Fermion Determinant State and the Jastrow-Backflow ansatz, both parametrized by the same Vision Transformer, finding that in practice their accuracy is similar. We also test different symmetrization strategies, finding that output averaging yields the lowest energies, though it becomes costly for larger system sizes. Finally, I will show our physical results for cylinders and the torus, demonstrating both the promise and current challenges of neural quantum states for correlated fermions.