Accurately describing strong electron correlation in complex systems remains a prominent challenge in computational chemistry as near-term quantum algorithms treating total correlation often require prohibitively deep circuits. Here we present a hybrid strategy combining the Variational Quantum Eigensolver with Multiconfiguration Pair-Density Functional Theory to efficiently decouple correlation effects. This approach confines static correlation to a compact multireference quantum state while recovering dynamic correlation through a classical on-top density functional using reduced-density information. By enabling self-consistent orbital optimization, the method significantly reduces quantum resource overheads without sacrificing physical rigor. We demonstrate chemical accuracy on standard benchmarks by reproducing C$_2$ equilibrium bond lengths and benzene excitation energies with mean absolute errors of 0.006 Å and 0.048 eV respectively. Most notably, for the strongly correlated Cr$_2$ dimer requiring a large complete active space (48e, 42o), the framework yields a bound potential-energy curve and recovers qualitative dissociation behavior despite realistic hardware noise. These results establish that separating correlation types provides a practical route to reliable predictions on near-term quantum hardware.

翻译：精确描述复杂体系中的强电子关联仍是计算化学中的显著挑战，因为处理总关联的近期量子算法通常需要过深的量子线路。本文提出一种混合策略，将变分量子特征求解器与多组态对密度泛函理论相结合，以有效解耦关联效应。该方法将静态关联限制在紧凑的多参考量子态中，同时通过基于约化密度信息的经典顶密度泛函回收动态关联。通过实现自洽轨道优化，该方法在不牺牲物理严谨性的前提下显著降低了量子资源开销。通过再现C₂平衡键长和苯激发能（平均绝对误差分别为0.006 Å和0.048 eV），我们在标准基准测试上展示了化学精度。特别值得注意的是，对于需要大型完全活性空间（48电子，42轨道）的强关联Cr₂二聚体，该框架在存在实际硬件噪声的情况下仍能生成有界势能曲线并恢复定性解离行为。这些结果表明，分离关联类型为在近期量子硬件上实现可靠预测提供了一条实用路径。