整合子系统嵌入子代数和耦合簇格林函数:激励流形中量子嵌入的理论基础

IF 2.9 Q3 CHEMISTRY, PHYSICAL
Bo Peng, Karol Kowalski
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引用次数: 0

摘要

在本研究中,我们通过将传统的耦合簇格林函数(CCGF)理论与最先进的子系统嵌入子代数耦合簇(SES-CC)形式主义无缝整合,引入了一种新颖的耦合簇格林函数(CCGF)嵌入方法。这种整合主要集中在划定子系统的特征和格林函数的相应部分,这些部分由活动轨道明确定义。最重要的是,我们的工作涉及对 SES-CC 范式的调整,通过一种独特形式的汉密尔顿相似性变换来解决左特征值问题。这一进步不仅有助于全面表示嵌入式子系统与其周围环境之间的相互作用,还为多个嵌入域的量子力学描述铺平了道路,特别是通过采用新兴量子流算法。我们的理论基础进一步为推广到多个嵌入式子系统奠定了基础。这一扩展为非平衡量子系统的探索和应用带来了重大希望,增强了人们对系统与环境相互作用的理解。在此过程中,研究强调了 SES-CC 嵌入在量子计算和多尺度模拟领域的潜力,有望在精度和计算效率之间取得良好平衡。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Integrating subsystem embedding subalgebras and coupled cluster Green’s function: a theoretical foundation for quantum embedding in excitation manifold
In this study, we introduce a novel approach to coupled-cluster Green’s function (CCGF) embedding by seamlessly integrating conventional CCGF theory with the state-of-the-art sub-system embedding sub-algebras coupled cluster (SES-CC) formalism. This integration focuses primarily on delineating the characteristics of the sub-system and the corresponding segments of the Green’s function, defined explicitly by active orbitals. Crucially, our work involves the adaptation of the SES-CC paradigm, addressing the left eigenvalue problem through a distinct form of Hamiltonian similarity transformation. This advancement not only facilitates a comprehensive representation of the interaction between the embedded sub-system and its surrounding environment but also paves the way for the quantum mechanical description of multiple embedded domains, particularly by employing the emergent quantum flow algorithms. Our theoretical underpinnings further set the stage for a generalization to multiple embedded sub-systems. This expansion holds significant promise for the exploration and application of non-equilibrium quantum systems, enhancing the understanding of system–environment interactions. In doing so, the research underscores the potential of SES-CC embedding within the realm of quantum computations and multi-scale simulations, promising a good balance between accuracy and computational efficiency.
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来源期刊
CiteScore
3.70
自引率
11.50%
发文量
46
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