主动空间嵌入方法的一般框架及其在量子计算中的应用

IF 9.4 1区 材料科学 Q1 CHEMISTRY, PHYSICAL
Stefano Battaglia, Max Rossmannek, Vladimir V. Rybkin, Ivano Tavernelli, Jürg Hutter
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引用次数: 0

摘要

我们开发了一个基于碎片轨道空间分离和环境自由度的分子和周期嵌入方法的混合量子经典计算的一般框架。我们展示了它的潜力,通过提出耦合到量子电路分析的周期性范围分离DFT的具体实现,其中变分量子特征解算器和量子运动方程算法被用来获得嵌入片段哈密顿量的低洼谱。通过对氧化镁(MgO)中中性氧空位光学性质的准确预测,展示了该方案在材料局域电子态研究中的应用。尽管在主吸收带的位置上存在一些差异,但与最先进的从头算方法相比,该方法表现出具有竞争力的性能,特别是与实验光致发光发射峰的良好一致性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

A general framework for active space embedding methods with applications in quantum computing

A general framework for active space embedding methods with applications in quantum computing

We developed a general framework for hybrid quantum-classical computing of molecular and periodic embedding approaches based on an orbital space separation of the fragment and environment degrees of freedom. We demonstrate its potential by presenting a specific implementation of periodic range-separated DFT coupled to a quantum circuit ansatz, whereby the variational quantum eigensolver and the quantum equation-of-motion algorithm are used to obtain the low-lying spectrum of the embedded fragment Hamiltonian. The application of this scheme to study localized electronic states in materials is showcased through the accurate prediction of the optical properties of the neutral oxygen vacancy in magnesium oxide (MgO). Despite some discrepancies in the position of the main absorption band, the method demonstrates competitive performance compared to state-of-the-art ab initio approaches, particularly evidenced by the excellent agreement with the experimental photoluminescence emission peak.

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来源期刊
npj Computational Materials
npj Computational Materials Mathematics-Modeling and Simulation
CiteScore
15.30
自引率
5.20%
发文量
229
审稿时长
6 weeks
期刊介绍: npj Computational Materials is a high-quality open access journal from Nature Research that publishes research papers applying computational approaches for the design of new materials and enhancing our understanding of existing ones. The journal also welcomes papers on new computational techniques and the refinement of current approaches that support these aims, as well as experimental papers that complement computational findings. Some key features of npj Computational Materials include a 2-year impact factor of 12.241 (2021), article downloads of 1,138,590 (2021), and a fast turnaround time of 11 days from submission to the first editorial decision. The journal is indexed in various databases and services, including Chemical Abstracts Service (ACS), Astrophysics Data System (ADS), Current Contents/Physical, Chemical and Earth Sciences, Journal Citation Reports/Science Edition, SCOPUS, EI Compendex, INSPEC, Google Scholar, SCImago, DOAJ, CNKI, and Science Citation Index Expanded (SCIE), among others.
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