混合高性能计算-量子模拟：面向分子体系的DFT量子嵌入方法
Hybrid HPC-Quantum Simulations: DFT-Quantum Embedding for Molecular Systems
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中文总结 AI 辅助
本研究提出混合DFT-量子嵌入框架，结合HPC的DFT与量子求解器，通过无噪声模拟评估其性能，结果显示该方法可在保留HPC可扩展性的同时改善电子结构性质，为HPC-量子混合模拟提供了新途径。
中文摘要 AI 辅助
科学模拟需要兼具可扩展性与预测精度的方法。高性能计算（HPC）上的密度泛函理论（DFT）可实现大规模电子结构模拟，但受限于近似方法，在强关联体系和带隙预测方面存在不足。量子计算为解决这一问题提供了途径，不过当前有噪中等规模量子（NISQ）硬件受限于量子比特资源、噪声和执行成本。本研究提出一种混合DFT-量子嵌入（QDFT）框架，将基于经典HPC的DFT与量子电子结构求解器相结合。对大体系进行划分，分离出具有化学意义的活性空间，通过变分量子本征求解器（VQE）处理，其余自由度由DFT描述。该框架包含活性空间选择、嵌入哈密顿量构建、对称性保持、算子映射、自洽密度更新以及模块化经典-量子耦合。本研究聚焦于无噪声量子模拟，系统评估精度、收敛性、活性空间依赖性、计算成本和HPC可扩展性（不考虑硬件噪声）。详细分析确定了计算瓶颈，并突出了基于CPU的量子模拟的局限性。此外，开发了一种QPU运行时估算方法，用于评估实际量子硬件上的执行需求。结果表明，量子嵌入在保留经典HPC可扩展性的同时，有潜力改善选定的电子结构性质。有噪量子模拟和QPU执行仍是未来的关键方向，随着硬件成熟，将为实用、可扩展的HPC-量子混合模拟提供途径。
英文摘要
Scientific simulations demand methods combining scalability with predictive accuracy. Density Functional Theory (DFT) on High-Performance Computing (HPC) enables large-scale electronic-structure simulations but is limited by approximations affecting strongly correlated systems and band-gap predictions. Quantum computing offers a pathway to address this, though current Noisy Intermediate-Scale Quantum (NISQ) hardware remains constrained by qubit resources, noise, and execution cost. This work presents a hybrid DFT-Quantum Embedding (QDFT) framework integrating classical HPC-based DFT with a quantum electronic-structure solver. Large systems are partitioned to isolate a chemically relevant active space, treated via the Variational Quantum Eigensolver (VQE), while the remaining degrees of freedom are described by DFT. The framework incorporates active-space selection, embedded Hamiltonian construction, symmetry preservation, operator mapping, self-consistent density updating, and modular classical-quantum coupling. We focus on noiseless quantum simulation to systematically evaluate accuracy, convergence, active-space dependence, computational cost, and HPC scalability without hardware noise. Detailed profiling identifies computational bottlenecks and highlights limitations of CPU-based quantum simulation. A QPU runtime-estimation methodology is additionally developed to assess execution requirements on actual quantum hardware. Results demonstrate quantum embedding's potential to improve selected electronic-structure properties while retaining classical HPC's scalability. Noisy quantum simulation and QPU execution remain key future directions, providing a pathway toward practical, scalable HPC-quantum hybrid simulations as hardware matures.
