An accurate assessment of how quantum computers can be used for chemical simulation, especially their potential computational advantages, provides important context on how to deploy these future devices. To perform this assessment reliably, quantum resource estimates must be coupled with classical computations attempting to answer relevant chemical questions and to define the classical algorithms simulation frontier. Herein, we explore the quantum computation and classical computation resources required to assess the electronic structure of cytochrome P450 enzymes (CYPs) and thus define a classical–quantum advantage boundary. This is accomplished by analyzing the convergence of density matrix renormalization group plus n -electron valence state perturbation theory (DMRG+NEVPT2) and coupled-cluster singles doubles with noniterative triples [CCSD(T)] calculations for spin gaps in models of the CYP catalytic cycle that indicate multireference character. The quantum resources required to perform phase estimation using qubitized quantum walks are calculated for the same systems. Compilation into the surface code provides runtime estimates to compare directly to DMRG runtimes and to evaluate potential quantum advantage. Both classical and quantum resource estimates suggest that simulation of CYP models at scales large enough to balance dynamic and multiconfigurational electron correlation has the potential to be a quantum advantage problem and emphasizes the important interplay between classical computations and quantum algorithms development for chemical simulation.

中文翻译：

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在今天的经典计算机和明天的量子计算机上可靠地评估细胞色素 P450 的电子结构

准确评估量子计算机如何用于化学模拟，尤其是其潜在的计算优势，为如何部署这些未来设备提供了重要背景。为了可靠地执行此评估，量子资源估计必须与试图回答相关化学问题并定义经典算法模拟边界的经典计算相结合。在此，我们探索了评估细胞色素 P450 酶 (CYP) 的电子结构所需的量子计算和经典计算资源，从而定义了经典-量子优势边界。这是通过分析密度矩阵重整化群加的收敛性来完成的n-电子价态微扰理论 (DMRG+NEVPT2) 和耦合簇单打与非迭代三元组 [CCSD(T)] 计算表明多参考特征的 CYP 催化循环模型中的自旋间隙。为相同的系统计算使用量子位化量子游走执行相位估计所需的量子资源。编译到表面代码中可提供运行时估计，以直接与 DMRG 运行时进行比较并评估潜在的量子优势。经典和量子资源估计都表明，在足以平衡动态和多构型电子相关性的大尺度下模拟 CYP 模型有可能成为一个量子优势问题，并强调了经典计算与化学模拟的量子算法开发之间的重要相互作用。