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Determinant Monte Carlo for irreducible Feynman diagrams

Fedor Šimkovic, Evgeny Kozik

Published Dec 28, 2017• 0 citations
OpenAlex

Abstract

We introduce a numerically exact method for efficient summation of irreducible Feynman diagrams for the fermionic self-energy in the thermodynamic limit. The technique, based on the Diagrammatic Determinant Monte Carlo, allows us to reach unprecedentedly high ($\sim 10$) orders of the weak-coupling expansion for the self-energy of the two-dimensional Hubbard model. Access to high orders reveals a non-trivial analytic structure of the self-energy and enables its controlled reconstruction with arbitrary momentum resolution in the non-perturbative regime of essentially strong correlations, which has recently been reached with ultracold atoms in optical lattices.

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We introduce a numerically exact method for efficient summation of irreducible Feynman diagrams for the fermionic self-energy in the thermodynamic limit. The technique, based on the Diagrammatic Determinant Monte Carlo, allows us to reach unprecedentedly high (∼10\sim 10∼10) orders of the weak-coupling expansion for the self-energy of the two-dimensional Hubbard model. Access to high orders reveals a non-trivial analytic structure of the self-energy and enables its controlled reconstruction with arbitrary momentum resolution in the non-perturbative regime of essentially strong correlations, which has recently been reached with ultracold atoms in optical lattices.
We develop a numerically exact method for the summation of irreducible Feynman diagrams for fermionic self-energy in the thermodynamic limit. The technique, based on the diagrammatic determinant Monte Carlo and its recent extension to connected diagrams, allows us to reach high $(\ensuremath{\sim}10)$ orders of the weak-coupling expansion for the self-energy of the two-dimensional Hubbard model. Access to high orders reveals a nontrivial analytic structure of the self-energy and enables its controlled reconstruction with arbitrary momentum resolution in the nonperturbative regime of essentially strong correlations, which has recently been reached with ultracold atoms in optical lattices.
We introduce a Monte Carlo determinantal algorithm that allows to efficiently sample dynamical quantities, such as the self-energy, of fermionic quantum systems in their thermodynamic limit. The method expresses the sum of a factorial number of one-particle-irreducible diagrams as a recursive sum of determinants with exponential complexity. By comparing results of this algorithm for the lattice Hubbard model with those obtained from diagrammatic Monte Carlo and the recently introduced connected determinant approach, we show that we can reach higher perturbation orders and greater accuracy for the same computational effort.
We introduce and compare three different Monte Carlo determinantal algorithms that allow one to compute dynamical quantities, such as the self-energy, of fermionic systems in their thermodynamic limit. We show that the most efficient approach expresses the sum of a factorial number of one-particle-irreducible diagrams as a recursive sum of determinants with exponential complexity. By comparing results for the two-dimensional Hubbard model with those obtained from state-of-the-art diagrammatic Monte Carlo, we show that we can reach higher perturbation orders and greater accuracy for the same computational effort.
We present a simple trick that allows us to consider the sum of all connected Feynman diagrams at fixed position of interaction vertices for general fermionic models, such that the thermodynamic limit can be taken analytically. With our approach one can achieve superior performance compared to conventional diagrammatic Monte Carlo algorithm, while rendering the algorithmic part dramatically simpler. By considering the sum of all connected diagrams at once, we allow for massive cancellations between different diagrams, greatly reducing the sign problem. In the end, the computational effort increases only exponentially with the order of the expansion, which should be contrasted with the factorial growth of the standard diagrammatic technique. We illustrate the efficiency of the technique for the two-dimensional Fermi-Hubbard model.
We show that Monte Carlo sampling of the Feynman diagrammatic series (DiagMC) can be used for tackling hard fermionic quantum many-body problems in the thermodynamic limit by presenting accurate results for the repulsive Hubbard model in the correlated Fermi liquid regime. Sampling Feynman's diagrammatic series for the single-particle self-energy we can study moderate values of the on-site repulsion (U/t∼4U/t \sim 4U/t∼4) and temperatures down to T/t=1/40T/t=1/40T/t=1/40. We compare our results with high temperature series expansion and with single-site and cluster dynamical mean-field theory.
We present a technique that enables the evaluation of perturbative expansions based on one-loop-renormalized vertices up to large expansion orders. Specifically, we show how to compute large-order corrections to the random phase approximation in either the particle-hole or particle-particle channels. The algorithm's efficiency is achieved by the summation over contributions of all symmetrized Feynman diagram topologies using determinants, and by integrating out analytically the two-body long-range interactions in order to yield an effective zero-range interaction. Notably, the exponential scaling of the algorithm as a function of perturbation order leads to a polynomial scaling of the approximation error with computational time for a convergent series. To assess the performance of our approach, we apply it to the nonperturbative regime of the square-lattice fermionic Hubbard model away from half-filling and report, as compared to the bare interaction expansion algorithm, significant improvements of the Monte Carlo variance as well as the convergence properties of the resulting perturbative series.