图解耦合簇蒙特卡罗
Diagrammatic Coupled Cluster Monte Carlo
摘要：
我们提出了一种改进的耦合簇Monte Carlo算法，随机采样连接的条款内截断的Baker-Campbell-Hausdorff展开的相似性变换的哈密顿量的耦合簇图的建设上飞。我们的新方法-- diagCCMC --允许只使用相似变换的哈密顿量的连接分量进行传播，大大降低了与耦合簇方程的随机解相关的内存成本。我们发现，完美的本地，非相互作用的系统，diagCCMC是能够代表耦合集群波函数与系统大小线性缩放的内存成本。有利的内存成本观察到的唯一假设的固定随机粒度，是有效的耦合集群理论的任意级别。显着降低存储器成本也显示出顺利出现与氦原子的有限链的解离。这种方法也被证明不会打破强相关性的存在下，通过拉伸氮分子的例子。我们的新方法移动的理论基础耦合簇蒙特卡罗更接近确定性的方法。
英文摘要：
We propose a modified coupled cluster Monte Carlo algorithm that stochastically samples connected terms within the truncated Baker--Campbell--Hausdorff expansion of the similarity transformed Hamiltonian by construction of coupled cluster diagrams on the fly. Our new approach -- diagCCMC -- allows propagation to be performed using only the connected components of the similarity-transformed Hamiltonian, greatly reducing the memory cost associated with the stochastic solution of the coupled cluster equations. We show that for perfectly local, noninteracting systems, diagCCMC is able to represent the coupled cluster wavefunction with a memory cost that scales linearly with system size. The favorable memory cost is observed with the only assumption of fixed stochastic granularity and is valid for arbitrary levels of coupled cluster theory. Significant reduction in memory cost is also shown to smoothly appear with dissociation of a finite chain of helium atoms. This approach is also shown not to break down in the presence of strong correlation through the example of a stretched nitrogen molecule. Our novel methodology moves the theoretical basis of coupled cluster Monte Carlo closer to deterministic approaches.
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