AI 中文总结
研究针对强关联材料，提出集成DFT-万尼尔-量子嵌入的计算管道，利用ADAPT-VQE框架及相关增强策略，通过对Li-hBN的基准研究，量化活性空间与计算需求关系，为工作流程提供性能基线和对混合架构限制的见解。
AI 中文摘要
密度泛函理论（DFT）与量子变分算法的无缝集成对于强关联材料的预测模拟至关重要。在这项工作中，我们提出了一个端到端的计算管道，包括DFT几何弛豫、非自洽场（NSCF）计算和基于万尼尔的轨道定位，以制备用于量子嵌入的活性空间哈密顿量。我们利用自适应变分量子本征求解器（ADAPT-VQE）框架，通过贪婪算子交换性分区（GOCP）方法和泰勒展开的O(5)算子演化策略显著增强，以有效管理希尔伯特空间的指数缩放。我们通过对Li-hBN的系统基准研究来展示这个框架，将系统映射到量子比特寄存器上，并研究随着活性空间从8个空间轨道扩展到14个空间轨道的收敛行为。我们的结果量化了活性空间大小与计算需求之间的关系，确定了一个关键的“缩放墙”，在那里经典模拟成本从可管理转变为棘手。这项研究为DFT到ADAPT-VQE工作流程提供了严格的性能基线，并为使用先进协同处理策略的混合量子-经典架构目前面临的内存和处理限制提供了实证见解。
英文摘要
The seamless integration of Density Functional Theory (DFT) with quantum variational algorithms is essential for the predictive simulation of strongly correlated materials. In this work, we present an end-to-end computational pipeline - comprising DFT geometry relaxation, non-self-consistent field (NSCF) calculations, and Wannier-based orbital localization - to prepare active-space Hamiltonians for quantum embedding. We utilize the Adaptive Variational Quantum Eigensolver (ADAPT-VQE) framework, significantly enhanced by a Greedy-Operator Commutativity Partitioning (GOCP) approach and a Taylor-expanded O(5) operator evolution strategy to efficiently manage the exponential scaling of the Hilbert space. We demonstrate this framework through a systematic benchmark study of Li-hBN, mapping the system onto qubit registers and investigating the convergence behavior as the active space is expanded from 8 to 14 spatial orbitals. Our results quantify the relationship between active-space size and computational demand, identifying a critical "scaling wall" where classical simulation costs transition from manageable to intractable. This study provides a rigorous performance baseline for the DFT-to-ADAPT-VQE workflow and offers empirical insights into the memory and processing limits currently facing hybrid quantum-classical architectures using advanced co-processing strategies.
Comments12 pages, 1 figure
