论文标题
不均匀系统的数值链接群集扩展
Numerical linked cluster expansions for inhomogeneous systems
论文作者
论文摘要
我们开发了一种数值链接的群集扩展（NLCE）方法，该方法可以直接应用于不均匀系统，例如从不均匀初始状态引发的障碍和动力学的汉密尔顿人。我们通过计算平方晶格上二维自旋模型中的单旋旋转期望和自旋相关性的动力学来证明该方法，从棋盘状态开始。我们表明，NLCE可以比可比的计算成本的确切对角线化给予中等至显着的改进，并且随着包含簇的大小的增长，计算资源的优势呈指数增长。尽管该方法适用于任何类型的NLCE，但我们的明确基准使用矩形扩展。除了显示出处理不均匀系统的能力外，这些基准还证明了矩形扩展的效用。
We develop a numerical linked cluster expansion (NLCE) method that can be applied directly to inhomogeneous systems, for example Hamiltonians with disorder and dynamics initiated from inhomogeneous initial states. We demonstrate the method by calculating dynamics for single-spin expectations and spin correlations in two-dimensional spin models on a square lattice, starting from a checkerboard state. We show that NLCE can give moderate to dramatic improvement over an exact diagonalization of comparable computational cost, and that the advantage in computational resources grows exponentially as the size of the clusters included grows. Although the method applies to any type of NLCE, our explicit benchmarks use the rectangle expansion. Besides showing the capability to treat inhomogeneous systems, these benchmarks demonstrate the rectangle expansion's utility out of equilibrium.
