Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA, 91125, USA.
Department of Chemistry, Columbia University, New York, NY, 10027, USA.
Nat Commun. 2023 Apr 7;14(1):1952. doi: 10.1038/s41467-023-37587-6.
Due to intense interest in the potential applications of quantum computing, it is critical to understand the basis for potential exponential quantum advantage in quantum chemistry. Here we gather the evidence for this case in the most common task in quantum chemistry, namely, ground-state energy estimation, for generic chemical problems where heuristic quantum state preparation might be assumed to be efficient. The availability of exponential quantum advantage then centers on whether features of the physical problem that enable efficient heuristic quantum state preparation also enable efficient solution by classical heuristics. Through numerical studies of quantum state preparation and empirical complexity analysis (including the error scaling) of classical heuristics, in both ab initio and model Hamiltonian settings, we conclude that evidence for such an exponential advantage across chemical space has yet to be found. While quantum computers may still prove useful for ground-state quantum chemistry through polynomial speedups, it may be prudent to assume exponential speedups are not generically available for this problem.
由于人们对量子计算的潜在应用产生了浓厚的兴趣,因此了解量子化学中潜在的指数级量子优势的基础至关重要。在这里,我们在量子化学中最常见的任务——基态能量估计中收集了这方面的证据,对于一般的化学问题,可以假设启发式量子态制备是有效的。那么,指数级量子优势的出现是否取决于物理问题的特征,这些特征既能使启发式量子态制备有效,又能使经典启发式算法有效解决。通过对量子态制备的数值研究和对经典启发式算法的经验复杂度分析(包括误差缩放),包括从头算和模型哈密顿量设置,我们得出的结论是,在化学空间中还没有找到这样的指数级优势的证据。虽然量子计算机通过多项式加速仍然可能对基态量子化学有用,但对于这个问题,假设指数级加速不是普遍可用的可能是明智的。