Incorporating dynamic mean-field theory into diagrammatic Monte Carlo

Lode Pollet; Nikolay V. Prokofev; Boris V. Svistunov

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Incorporating dynamic mean-field theory into diagrammatic Monte Carlo

机译：将动态平均场理论纳入图解蒙特卡洛法

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The bold diagrammatic Monte Carlo (BDMC) method performs an unbiased sampling of Feynman's diagrammatic series using skeleton diagrams. For lattice models the efficiency of BDMC can be dramatically improved by incorporating dynamical mean-field theory (DMFT) solutions into renormalized propagators. From the DMFT perspective, combining it with BDMC leads to an unbiased method with well-defined accuracy. We illustrate the power of this approach by computing the single-particle propagator (and thus the density of states) in the non-perturbative regime of the Anderson localization problem, where a gain of the order of 10~4 is achieved with respect to conventional BDMC in terms of convergence to the exact answer.

机译：粗体图解蒙特卡洛（BDMC）方法使用骨架图对Feynman图解系列进行无偏采样。对于晶格模型，通过将动态平均场理论（DMFT）解决方案合并到重新规范化的传播子中，可以显着提高BDMC的效率。从DMFT的角度来看，将其与BDMC结合使用可产生具有明确定义的准确性的无偏方法。我们通过计算安德森局部化问题的非微扰状态下的单粒子传播体（从而计算出状态的密度）来说明这种方法的力量，相对于传统方法，该增益达到了10〜4的量级BDMC在收敛方面有确切的答案。

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来源
《Physical review》 |2011年第16期|p.161103.1-161103.4|共4页
作者
Lode Pollet; Nikolay V. Prokofev; Boris V. Svistunov;
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作者单位

Theoretische Physik, ETH Zurich, 8093 Zurich, Switzerland;

Department of Physics, University of Massachusetts, Amherst, Massachusetts 01003, USA,Russian Research Center "Kurchatov Institute," 123182 Moscow, Russia;

Department of Physics, University of Massachusetts, Amherst, Massachusetts 01003, USA,Russian Research Center "Kurchatov Institute," 123182 Moscow, Russia;

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正文语种 eng
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关键词
monte carlo methods; quantum monte carlo methods;

机译：蒙特卡洛方法;量子蒙特卡洛方法;

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Incorporating dynamic mean-field theory into diagrammatic Monte Carlo

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