AI 中文总结
提出量子-经典有效碎片势（Q-EFP）方法，用量子算法处理活性区、EFP描述环境，验证了嵌入哈密顿量，将量子寄存器需求缩减10-35倍，适用于更大溶剂化体系。
AI 中文摘要
在真实环境中进行化学的量子模拟受到显式溶剂、离子及其他周围环境的轨道成本的限制。我们提出了量子-经典有效碎片势（Q-EFP）方法，其中化学活性区域用量子算法处理，而环境由GAMESS中的有效碎片势（EFP）表示。库仑势和极化势进入活性区域的单电子哈密顿量，而EFP-EFP和短程环境贡献则经典地组装。对于混合水/甲醇环境中的LiH、五水环境中的H2O以及铵/硝酸盐环境中的BeH2，态矢量UCCSD/STO-3G基准与匹配的经典CCSD/EFP计算分别相差0.03、0.38和0.01 kcal/mol。活性计算需要4到8个量子比特，而估计的全系统数量约为84到246个量子比特，对应寄存器缩减约10倍至35倍。这些概念验证结果验证了嵌入哈密顿量和Q-GAMESS工作流程，同时将环境规模与量子寄存器规模分离。Q-EFP与实空间Q-EFMO碎片化和虚拟轨道Q-FVO缩减互补，为更大的溶剂化系统提供了一条分层路径。
英文摘要
Quantum simulation of chemistry in realistic environments is constrained by the orbital cost of explicit solvent, ions, and other surroundings. We present the quantum-classical effective fragment potential (Q-EFP) method, in which a chemically active region is treated with a quantum algorithm and the environment is represented by the effective fragment potential (EFP) in GAMESS. Coulomb and polarization potentials enter the active-region one-electron Hamiltonian, while EFP-EFP and short-range environment contributions are assembled classically. Statevector UCCSD/STO-3G benchmarks for LiH in a mixed water/methanol environment, H2O in a five-water environment, and BeH2 in an ammonium/nitrate environment differ from matched classical CCSD/EFP calculations by 0.03, 0.38, and 0.01 kcal/mol, respectively. The active calculations require 4 to 8 qubits, compared with estimated full-system counts of approximately 84 to 246 qubits, corresponding to register reductions of about 10-fold to 35-fold. These proof-of-concept results validate the embedded Hamiltonian and Q-GAMESS workflow while separating environmental size from quantum-register size. Q-EFP is complementary to real-space Q-EFMO fragmentation and virtual-orbital Q-FVO reduction, enabling a layered route to larger solvated systems.
Comments9 pages, 3 figures, 5 tables
