基于密度矩阵的量子嵌入方法中最优浴结构的通用统一变换框架

IF 1.9 Q2 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS
Quentin Marécat, Matthieu Saubanère
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引用次数: 0

摘要

嵌入方法的性能直接关系到轨道结构的质量。在本文中,我们开发了一个通用的框架,使研究的最佳结构的轨道浴。到目前为止,在最先进的嵌入方法中,浴槽的轨道是通过对单体简化密度矩阵的杂质环境部分执行奇异值分解(SVD)来构建的,正如最初在密度矩阵嵌入理论中提出的那样。最近,SVD协议与使用酉变换(即所谓的块户主变换)之间的等价性已经建立起来。通过引入额外的灵活参数,我们提出了块户主变换的泛化。对附加参数进行了优化,使浴轨道满足物理激励约束。以半填充的一维Hubbard模型为例,讨论了该方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Versatile Unitary Transformation Framework for an Optimal Bath Construction in Density-Matrix Based Quantum Embedding Approaches
The performance of embedding methods is directly tied to the quality of the bath orbital construction. In this paper, we develop a versatile framework, enabling the investigation of the optimal construction of the orbitals of the bath. As of today, in state-of-the-art embedding methods, the orbitals of the bath are constructed by performing a Singular Value Decomposition (SVD) on the impurity-environment part of the one-body reduced density matrix, as originally presented in Density Matrix Embedding Theory. Recently, the equivalence between the SVD protocol and the use of unitary transformation, the so-called Block-Householder transformation, has been established. We present a generalization of the Block-Householder transformation by introducing additional flexible parameters. The additional parameters are optimized such that the bath-orbitals fulfill physically motivated constraints. The efficiency of the approach is discussed and exemplified in the context of the half-filled Hubbard model in one-dimension.
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来源期刊
Computation
Computation Mathematics-Applied Mathematics
CiteScore
3.50
自引率
4.50%
发文量
201
审稿时长
8 weeks
期刊介绍: Computation a journal of computational science and engineering. Topics: computational biology, including, but not limited to: bioinformatics mathematical modeling, simulation and prediction of nucleic acid (DNA/RNA) and protein sequences, structure and functions mathematical modeling of pathways and genetic interactions neuroscience computation including neural modeling, brain theory and neural networks computational chemistry, including, but not limited to: new theories and methodology including their applications in molecular dynamics computation of electronic structure density functional theory designing and characterization of materials with computation method computation in engineering, including, but not limited to: new theories, methodology and the application of computational fluid dynamics (CFD) optimisation techniques and/or application of optimisation to multidisciplinary systems system identification and reduced order modelling of engineering systems parallel algorithms and high performance computing in engineering.
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