$t$ - $V$模型的随机级数展开模拟
Stochastic series expansion simulation of the $t$-$V$ model
摘要：
本文提出了一种有效模拟二分格点上半满无自旋$t$ - $V$模型的算法，该算法结合了费米子模拟中广泛使用的随机级数展开方法和确定性量子Monte Carlo技术.该算法在逆温度下线性缩放，与系统尺寸成立方关系，并且没有时间离散化误差。我们用它绘制了蜂窝晶格上无自旋$t$ - $V$模型的有限温度相图，观察到在费米量子临界点附近电荷密度波相的临界温度被抑制.
英文摘要：
We present an algorithm for the efficient simulation of the half-filled spinless $t$-$V$ model on bipartite lattices, which combines the stochastic series expansion method with determinantal quantum Monte Carlo techniques widely used in fermionic simulations. The algorithm scales linearly in the inverse temperature, cubically with the system size and is free from the time-discretization error. We use it to map out the finite temperature phase diagram of the spinless $t$-$V$ model on the honeycomb lattice and observe a suppression of the critical temperature of the charge density wave phase in the vicinity of a fermionic quantum critical point.
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