Spin-charge transformation of lattice fermion models: duality approach for diagrammatic simulation of strongly correlated systems

TL;DR

This paper introduces a dual spin-charge transformation for lattice fermion models like t-J and Hubbard, enabling efficient diagrammatic simulation of strongly correlated systems by reducing complexity and nonlinear corrections.

Contribution

It develops a novel dual description using spin-charge transformation and fermionisation, facilitating diagrammatic techniques in strongly correlated lattice fermion models.

Findings

Models have no large expansion parameters even in strong correlation regimes.

Significantly smaller nonlinear corrections to the density matrix.

Enables the use of diagrammatic and stochastic sampling methods for simulation.

Abstract

I derive a dual description of lattice fermions, specifically focusing on the t-J and Hubbard models, that allow diagrammatic techniques to be employed efficiently in the strongly correlated regime, as well as for systems with a restricted Hilbert space. These constructions are based on spin-charge transformation, where the lattice fermions of the original model are mapped onto spins and spin-less fermions. This mapping can then be combined with Popov-Fedotov fermionisation, where the spins are mapped onto lattice fermions with imaginary chemical potential. The resulting models do not contain any large expansion parameters, even for strongly correlated systems. Also, they exhibit dramatically smaller corrections to the density matrix from nonlinear terms in the Hamiltonian. The combination of these two properties means that they can be addressed with diagrammatic methods, including simulation techniques based on stochastic sampling of diagrammatic expansions.