Fermi Arcs From Dynamical Variational Monte Carlo
arXiv:2209.08092 · doi:10.1103/PhysRevB.106.245132
Abstract
Variational Monte Carlo is a many-body numerical method that scales well with system size. It has been extended to study the Green function only recently by Charlebois and Imada (2020). Here we generalize the approach to systems with open boundary conditions in the absence of translational invariance. Removing these constraints permits the application of embedding techniques like Cluster perturbation theory (CPT). This allows us to solve an enduring problem in the physics of the pseudogap in cuprate high-temperature superconductors, namely the existence or absence of Fermi arcs in the one-band Hubbard model. We study the behavior of the Fermi surface and of the density of states as a function of hole doping for clusters of up to 64 sites, well beyond the reach of modern exact diagonalization solvers. We observe that the technique reliably captures the transition from a Mott insulator at half filling to a pseudogap, evidenced by the formation of Fermi arcs, and finally to a metallic state at large doping. The ability to treat large clusters with quantum cluster methods helps to minimize potential finite size effects and enables the study of systems with long range orders, which will help extend the reach of these already powerful methods and provide important insights on the nature of various strongly correlated many-electron systems, including the high-T cuprate superconductors.
13 pages, 8 figures
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- Mott transition and pseudogap of the square-lattice Hubbard model: results from center-focused cellular dynamical mean-field theory
- One- and two-particle correlation functions in the cluster perturbation theory for cuprates
- Perturbative solution of fermionic sign problem in lattice Quantum Monte Carlo
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- Probing the pseudogap and beyond: Examining single-particle properties of the hole- and electron-doped Hubbard model
- An occupation number quantum subspace expansion approach to compute the single-particle Green function: an opportunity for noise filtering