发表机构
Shionogi & Co., Ltd.; Classiq Technologies G.K.（盐野义制药株式会社; Classiq科技有限公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出 Deep QSCI 方法，结合分治与量子选择组态相互作用，以 20 量子比特高效计算苯二聚体的 π-π 堆积能，得到与 CCSD(T) 接近的结果，并避免重复单体量子计算。
AI 中文摘要
精确评估 $\pi$--$\pi$ 堆积相互作用需要在小能量尺度上描述电子关联。对苯二聚体直接进行 CAS(28e,20o) 的变分量子计算需要 40 个量子比特和深电路。在此，我们提出分治量子选择组态相互作用（Deep QSCI），将 Deep VQE 的分治思想与 QSCI 相结合，并将其应用于夹心苯二聚体。我们对一个单体执行 QSCI，并通过对其基态施加粒子数守恒的单电子激发来构建缩减的局域基。利用该基和单体间相互作用哈密顿量，我们构建并对二聚体的有效哈密顿量进行对角化。由于单体具有相同的结构，QSCI 结果可重复用于两个单体以及不同的分子间距离，从而将单体 CAS(14e,10o) 所需的量子寄存器从 40 个量子比特减少到 20 个。使用 6-31G** 基组，模型在 4.0 $Å$ 处给出吸引极小值 -0.91 kcal/mol，而经平衡校正的 CCSD(T) 值为 -1.14 kcal/mol。使用 cc-pVDZ 基组时，乘积态相互作用能与对角化带来的能量降低之间存在较大抵消。这可能反映了单体间活性轨道和缩减局域基的一致性不足。轨道一致性、跨基组的缩减空间收敛性以及电荷转移仍是实现定量精度的关键问题。因此，Deep QSCI 提供了一种资源高效的方法来构建模型相互作用曲线，而无需在每个分离距离重复单体量子计算。
英文摘要
Accurate evaluation of $π$--$π$ stacking interactions requires a description of electron correlation on a small energy scale. Direct variational quantum calculations of the benzene dimer with CAS(28e,20o) require 40 qubits and deep circuits. Here, we propose Divide-and-Conquer Quantum-Selected Configuration Interaction (Deep QSCI), combining the divide-and-conquer idea of Deep VQE with QSCI, and apply it to the sandwich benzene dimer. We perform QSCI for one monomer and construct a reduced local basis by applying particle-number-conserving one-electron excitations to its ground state. Using this basis and the intermonomer interaction Hamiltonian, we build and diagonalize an effective Hamiltonian of the dimer. Since the monomers have the same structure, the QSCI result is reused for both monomers and across intermolecular distances, reducing the required quantum register from 40 to 20 qubits for the monomer CAS(14e,10o). With 6-31G**, the model gives an attractive minimum of -0.91 kcal/mol at 4.0 $Å$, compared with the counterpoise-corrected CCSD(T) value of -1.14 kcal/mol. With cc-pVDZ, a large cancellation appears between the product-state interaction energy and the energy lowering by diagonalization. This may reflect insufficient consistency of the active orbitals and reduced local bases between monomers. Orbital consistency, reduced-space convergence across basis sets, and charge transfer remain key issues for quantitative accuracy. Deep QSCI thus provides a resource-efficient approach to constructing model interaction curves without repeating monomer quantum calculations at each separation.
