论文标题

在数值上模仿量子气显微镜进行相互作用的晶格费米子

Numerically exact mimicking of quantum gas microscopy for interacting lattice fermions

论文作者

Humeniuk, Stephan, Wan, Yuan

论文摘要

提出了一种用于复制均衡中的费米子量子显微镜实验的数值方法。通过使用Fermion伪密度矩阵进行嵌套的截面直接采样,在确定性量子蒙特卡洛(QMC)模拟中自然出现,可以生成大型系统上职业数字的伪命中。即使可以使常规的QMC算法不含符号问题,并且每个伪SNAPSHOT都带有一个标志和重新加权因素,也存在一个标志问题。尽管如此,在大型相关参数制度中,这种“抽样标志问题”事实证明是弱且易于管理的。该方法允许计算职业数量空间中定义的任意数量的分布函数,从实际的角度来看,可以促进复杂条件相关函数的计算。 While the projective measurements in quantum gas microscope experiments achieve direct sampling of occupation number states from the density matrix, the presented numerical method requires a Markov chain as an intermediate step and thus achieves only indirect sampling, but the full distribution of pseudo-snapshots after (signed) reweighting is identical to the distribution of snapshots from projective measurements

A numerical method is presented for reproducing fermionic quantum gas microscope experiments in equilibrium. By employing nested componentwise direct sampling of fermion pseudo-density matrices, as they arise naturally in determinantal quantum Monte Carlo (QMC) simulations, a stream of pseudo-snapshots of occupation numbers on large systems can be produced. There is a sign problem even when the conventional determinantal QMC algorithm can be made sign-problem free, and every pseudo-snapshot comes with a sign and a reweighting factor. Nonetheless, this "sampling sign problem" turns out to be weak and manageable in a large, relevant parameter regime. The method allows to compute distribution functions of arbitrary quantities defined in occupation number space and, from a practical point of view, facilitates the computation of complicated conditional correlation functions. While the projective measurements in quantum gas microscope experiments achieve direct sampling of occupation number states from the density matrix, the presented numerical method requires a Markov chain as an intermediate step and thus achieves only indirect sampling, but the full distribution of pseudo-snapshots after (signed) reweighting is identical to the distribution of snapshots from projective measurements

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