论文标题
具有低对称性的大型量子杂质模型的稀疏建模
Sparse modeling of large-scale quantum impurity models with low symmetries
论文作者
论文摘要
量子嵌入理论为获得相关材料的定量描述提供了可行的途径。但是，一个关键的挑战是解决嵌入电子浴中的相关轨道的有效杂质模型。许多先进的杂质求解器需要使用有限数量的浴缸水平近似浴缸连续体，从而产生高度非概念，条件不足的逆问题。为了解决这一缺点，本研究提出了一种基于数据科学方法，稀疏建模和Matsubara Green功能的紧凑表示，用于基质值杂交函数的有效拟合算法。通过将随机杂交函数与大型外元素以及高温下Laasfeo的20轨道杂质模型拟合，在低温（t）下，通过将随机杂交函数以及20轨道杂质模型的效率以及20个轨道杂质模型的效率证明。该结果设定了杂质求解器将来开发的定量目标，以使复杂相关材料的量子嵌入模拟。
Quantum embedding theories provide a feasible route for obtaining quantitative descriptions of correlated materials. However, a critical challenge is solving an effective impurity model of correlated orbitals embedded in an electron bath. Many advanced impurity solvers require the approximation of a bath continuum using a finite number of bath levels, producing a highly nonconvex, ill-conditioned inverse problem. To address this drawback, this study proposes an efficient fitting algorithm for matrix-valued hybridization functions based on a data-science approach, sparse modeling, and a compact representation of Matsubara Green's functions. The efficiency of the proposed method is demonstrated by fitting random hybridization functions with large off-diagonal elements as well as those of a 20-orbital impurity model for a high-Tc compound, LaAsFeO, at low temperatures (T). The results set quantitative goals for the future development of impurity solvers toward quantum embedding simulations of complex correlated materials.
