Rapid initial state preparation for the quantum simulation of strongly correlated molecules

Dominic W. Berry, Yu Tong, Tanuj Khattar, Alec White, Tae In Kim, Sergio Boixo, Lin Lin, Seunghoon Lee, Garnet Kin-Lic Chan, Ryan Babbush, Nicholas C. Rubin
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Abstract

Studies on quantum algorithms for ground state energy estimation often assume perfect ground state preparation; however, in reality the initial state will have imperfect overlap with the true ground state. Here we address that problem in two ways: by faster preparation of matrix product state (MPS) approximations, and more efficient filtering of the prepared state to find the ground state energy. We show how to achieve unitary synthesis with a Toffoli complexity about $7 \times$ lower than that in prior work, and use that to derive a more efficient MPS preparation method. For filtering we present two different approaches: sampling and binary search. For both we use the theory of window functions to avoid large phase errors and minimise the complexity. We find that the binary search approach provides better scaling with the overlap at the cost of a larger constant factor, such that it will be preferred for overlaps less than about $0.003$. Finally, we estimate the total resources to perform ground state energy estimation of Fe-S cluster systems, including the FeMo cofactor by estimating the overlap of different MPS initial states with potential ground-states of the FeMo cofactor using an extrapolation procedure. {With a modest MPS bond dimension of 4000, our procedure produces an estimate of $\sim 0.9$ overlap squared with a candidate ground-state of the FeMo cofactor, producing a total resource estimate of $7.3 \times 10^{10}$ Toffoli gates; neglecting the search over candidates and assuming the accuracy of the extrapolation, this validates prior estimates that used perfect ground state overlap. This presents an example of a practical path to prepare states of high overlap in a challenging-to-compute chemical system.
强相关分子量子模拟的快速初始态准备
对基态能量估计量子算法的研究通常假定基态制备是完美的;然而,在现实中,初始态与真正的基态会有不完美的重叠。在这里,我们从两个方面解决了这个问题:更快地制备矩阵乘积态(MPS)近似,以及更有效地过滤制备态以找到基态能量。我们展示了如何以比先前工作低约 7 美元的托福复杂度实现单元合成,并利用这种方法衍生出一种更高效的矩阵乘积态制备方法。对于过滤,我们提出了两种不同的方法:采样和二进制搜索。对于这两种方法,我们都使用了窗函数理论来避免大的相位误差,并将复杂性降到最低。我们发现,二进制搜索方法以较大的常数因子为代价,提供了更好的重叠缩放性,因此,当重叠小于约 0.003 美元时,二进制搜索方法将更受青睐。最后,我们利用外推法估算了不同 MPS 初始状态与 FeMo 辅因子潜在基态的重叠,从而估算了进行包括 FeMo 辅因子在内的 Fe-S 团簇系统基态能量估算的总资源。9美元的重叠平方,得出的总资源估计值为7.3美元乘以10^{10}$ Toffoligates;忽略对候选物质的搜索并假设外推法的准确性,这验证了之前使用完美基态重叠的估计值。这为我们提供了一个实例,说明在一个难以计算的化学体系中制备高重叠态的实际途径。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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