从动态团簇嵌入方法研究热力学
Accessing thermodynamics from dynamical cluster-embedding approaches
摘要：
动力学量子团簇方法，如不同的团簇扩展的动力学平均场论（团簇DMFT）或变分团簇近似（VCA），结合有效的团簇求解器，如量子蒙特卡罗（QMC）方法，提供控制近似的单粒子格林函数的晶格模型的强关联电子。然而，要获得热力学，需要一个热力学势。我们提出了一个有效的数值算法来计算的集群嵌入方法，是基于新的连续时间QMC计划内的巨势：它表明，数值精确的集群巨势可以从量子王-朗道技术重新加权系数的配分函数的扩展。格点对巨势的贡献通过对松原频率的适当无限求和来计算。将VCA应用于有限温度下二维哈伯德模型的反铁磁（短程）序，给出了原理的证明.
英文摘要：
Dynamical quantum-cluster approaches, such as different cluster extensions of the dynamical mean-field theory (cluster DMFT) or the variational cluster approximation (VCA), combined with efficient cluster solvers, such as the quantum Monte-Carlo (QMC) method, provide controlled approximations of the single-particle Green's function for lattice models of strongly correlated electrons. To access the thermodynamics, however, a thermodynamical potential is needed. We present an efficient numerical algorithm to compute the grand potential within cluster-embedding approaches that are based on novel continuous-time QMC schemes: It is shown that the numerically exact cluster grand potential can be obtained from a quantum Wang-Landau technique to reweight the coefficients in the expansion of the partition function. The lattice contributions to the grand potential are computed by a proper infinite summation over Matsubara frequencies. A proof of principle is given by applying the VCA to antiferromagnetic (short-range) order in the two-dimensional Hubbard model at finite temperatures.
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