arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2008.07377cond-mat.str-el
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高效的连通行列式单圈重正化顶点展开图Monte Carlo方法

Efficient One-Loop-Renormalized Vertex Expansions with Connected Determinant Diagrammatic Monte Carlo

Fedor Šimkovic, Riccardo Rossi, Michel Ferrero

摘要:

我们提出了一种技术,使微扰膨胀的基础上的一个循环重正化顶点大的扩展订单的评价。具体来说,我们将展示如何计算大的顺序修正的随机相位近似的粒子-孔或粒子-粒子通道。该算法的效率是通过使用行列式的所有对称化的费曼图拓扑结构的贡献求和,并通过分析积分出的两体长程相互作用,以产生一个有效的零程相互作用。值得注意的是,作为扰动阶数的函数的算法的指数缩放导致收敛级数的近似误差随计算时间的多项式缩放。为了评估我们的方法的性能,我们将其应用到非微扰制度的正方形晶格费米子哈伯德模型远离半填充和报告,相比裸相互作用展开算法,显着改善的Monte Carlo方差以及所产生的微扰系列的收敛性能。

英文摘要:

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 non-perturbative 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.

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