论文标题
多轨道杂质模型的自适应变分量子本素体的比较研究
Comparative study of adaptive variational quantum eigensolvers for multi-orbital impurity models
论文作者
论文摘要
相关材料模拟的混合量子古典嵌入方法为潜在的量子优势提供了途径。但是，由$ d $和$ f $电子材料的多波段性质产生的所需量子资源在很大程度上尚未探索。在这里，我们比较了不同变异量子本质体在相互作用的多轨嵌入杂质模型的基础状态准备中的性能，这是计算上最苛刻的量子嵌入理论的步骤。我们专注于具有8个自旋轨道的自适应算法和型号，我们表明，使用大约$ 2^{14} $ shots $ shots shots的状态准备可以优于$ 99.9 \％$ $。当包括门噪声时，我们会观察到，如果两数Qubit门误差位于$ 10^{ - 3} $以下，则可以执行参数优化，该错误比当前的硬件级别略小。最后，我们使用收敛的自适应ANSATZ测量IBM和Quantinuum硬件上的基态能量，并获得$ 0.7 \％$的相对误差。
Hybrid quantum-classical embedding methods for correlated materials simulations provide a path towards potential quantum advantage. However, the required quantum resources arising from the multi-band nature of $d$ and $f$ electron materials remain largely unexplored. Here we compare the performance of different variational quantum eigensolvers in ground state preparation for interacting multi-orbital embedding impurity models, which is the computationally most demanding step in quantum embedding theories. Focusing on adaptive algorithms and models with 8 spin-orbitals, we show that state preparation with fidelities better than $99.9\%$ can be achieved using about $2^{14}$ shots per measurement circuit. When including gate noise, we observe that parameter optimizations can still be performed if the two-qubit gate error lies below $10^{-3}$, which is slightly smaller than current hardware levels. Finally, we measure the ground state energy on IBM and Quantinuum hardware using a converged adaptive ansatz and obtain a relative error of $0.7\%$.
