量子
密度矩阵
嵌入
基质(化学分析)
密度泛函理论
样品(材料)
分子
统计物理学
计算机科学
物理
量子力学
材料科学
人工智能
复合材料
热力学
作者
Akhil Shajan,Danil Kaliakin,Abhishek Mitra,Javier Robledo Moreno,Zhen Li,Mário Motta,Caleb Johnson,Abdullah Ash Saki,Susanta Das,Iskandar Sitdikov,Antonio Mezzacapo,Kenneth M. Merz
出处
期刊:PubMed
日期:2025-07-08
标识
DOI:10.1021/acs.jctc.5c00114
摘要
Computing ground-state properties of molecules is a promising application for quantum computers operating in concert with classical high-performance computing resources. Quantum embedding methods are a family of algorithms particularly suited to these computational platforms: they combine high-level calculations on active regions of a molecule with low-level calculations on the surrounding environment, thereby avoiding expensive high-level full-molecule calculations and allowing the distribution of computational cost across multiple and heterogeneous computing units. Here, we present the first density matrix embedding theory (DMET) simulations performed in combination with a sample-based quantum diagonalization (SQD) method. We employ the DMET-SQD formalism to compute the ground-state energy of a ring of 18 hydrogen atoms and the relative energies of the chair, half-chair, twist-boat, and boat conformers of cyclohexane. The full-molecule 41- and 89-qubit simulations are decomposed into 27- and 32-qubit active-region simulations, which we carry out on the ibm_cleveland device, obtaining results in agreement with reference classical methods. Our DMET-SQD calculations mark tangible progress in the size of active regions that can be accurately tackled by near-term quantum computers and are an early demonstration of the potential for quantum-centric simulations to accurately treat the electronic structure of large molecules, with the ultimate goal of tackling systems such as peptides and proteins.
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