Gaussian variational method to Fermi Hubbard model in one and two dimensions
Yue-Ran Shi, Yuan-Yao He, Ruijin Liu, and Wei Zhang

TL;DR
This paper introduces a Gaussian variational method to efficiently approximate the ground states of the Fermi-Hubbard model in one and two dimensions, achieving high accuracy with low computational cost.
Contribution
The study develops a Gaussian variational approach for the Fermi-Hubbard model, providing accurate ground state energies and properties in higher dimensions with minimal computational resources.
Findings
Achieves ~4% error in total energy across interaction strengths.
Accurately predicts double occupancy matching other methods and experiments.
Effective for large systems and higher dimensions with negligible size dependence.
Abstract
The study of ground-state properties of the Fermi-Hubbard model is a long-lasting task in the research of strongly correlated systems. Owing to the exponentially growing complexity of the system, a quantitative analysis usually demands high computational cost and is restricted to small samples, especially in two or higher dimensions. Here, we introduce a variational method in the frame of fermionic Gaussian states, and obtain the ground states of one- and two-dimensional attractive Hubbard models via imaginary-time evolution. We calculate the total energy and benchmark the results in a wide range of interaction strength and filling factor with those obtained via exact two-body results, the density matrix renormalization group based on matrix product states (MPS), and projector Quantum Monte Carlo (QMC) method. For both 1D and 2D cases, the Gaussian variational method presents accurate…
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Taxonomy
TopicsPhysics of Superconductivity and Magnetism · Cold Atom Physics and Bose-Einstein Condensates · Advanced Condensed Matter Physics
