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Journal of the Physical Society of Japan

A Fermion Simulation Algorithm by Molecular Dynamics Techique Combined with Monte Carlo Method

Masatoshi Imada

Published Aug 15, 1988• 8 citations
Publisher

Abstract

A hybrid algorithm for lattice fermions is examined. The fermion determinant is evaluated by a replacement of a matrix inversion with a stochastic iteration procedure. An advantage is that the computation time increases linearly with the increase of spatial system size. Efficiency and convergence in Hubbard-type models are investigated in various cases and temperatures.

DOI
10.1143/jpsj.57.2689

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A hybrid algorithm for lattice fermions is examined. The fermion determinant is evaluated by a replacement of a matrix inversion with a stochastic iteration procedure. An advantage is that the computation time increases linearly with the increase of spatial system size. Efficiency and convergence in Hubbard-type models are investigated in various cases and temperatures.
We present an algorithm for the numerical simulation of many electron systems, which is based upon the hybrid stochastic approach presently being used to study lattice gauge theory. A method for preconditioning the fermion matrices is described which significantly reduces the computing time needed in the simulation. Results are presented for the three-dimensional Hubbard model on lattices ranging in size from 43{4}^{3}43 to 103{10}^{3}103.
Efficient continuous-time quantum Monte Carlo (CT-QMC) algorithms that do not suffer from time discretization errors have become the state of the art for most discrete quantum models. They have not been widely used yet for fermionic quantum lattice models, such as the Hubbard model, nor other fermionic lattice systems due to a suboptimal scaling of $O({\ensuremath{\beta}}^{3})$ with inverse temperature $\ensuremath{\beta}$, compared to the linear scaling of discrete-time algorithms. Here we present a CT-QMC algorithm for fermionic lattice systems that matches the scaling of discrete-time methods but is more efficient and free of time discretization errors. This provides an efficient simulation scheme that is free from the systematic errors opening an avenue to more precise studies of large systems at low and zero temperature.
The Hubbard model arises naturally when electron-electron interactions are added to the tight-binding descriptions of many condensed matter systems. For instance, the two-dimensional Hubbard model on the honeycomb lattice is central to the ab initio description of the electronic structure of carbon nanomaterials, such as graphene. Such low-dimensional Hubbard models are advantageously studied with Markov chain Monte Carlo methods, such as hybrid Monte Carlo (HMC). HMC is the standard algorithm of the lattice gauge theory community, as it is well suited to theories of dynamical fermions. As HMC performs continuous, global updates of the lattice degrees of freedom, it provides superior scaling with system size relative to local updating methods. A potential drawback of HMC is its susceptibility to ergodicity problems due to so-called exceptional configurations, for which the fermion operator cannot be inverted. Recently, ergodicity problems were found in some formulations of HMC simulations of the Hubbard model. Here, we address this issue directly and clarify under what conditions ergodicity is maintained or violated in HMC simulations of the Hubbard model. We study different lattice formulations of the fermion operator and provide explicit, representative calculations for small systems, often comparing to exact results. We show that a fermion operator can be found which is both computationally convenient and free of ergodicity problems.
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.
Featuring detailed explanations of the major algorithms used in quantum Monte Carlo simulations, this is the first textbook of its kind to provide a pedagogical overview of the field and its applications. The book provides a comprehensive introduction to the Monte Carlo method, its use, and its foundations, and examines algorithms for the simulation of quantum many-body lattice problems at finite and zero temperature. These algorithms include continuous-time loop and cluster algorithms for quantum spins, determinant methods for simulating fermions, power methods for computing ground and excited states, and the variational Monte Carlo method. Also discussed are continuous-time algorithms for quantum impurity models and their use within dynamical mean-field theory, along with algorithms for analytically continuing imaginary-time quantum Monte Carlo data. The parallelization of Monte Carlo simulations is also addressed. This is an essential resource for graduate students, teachers, and researchers interested in quantum Monte Carlo techniques.