论文标题
对“用量子计算机无偏量子蒙特卡洛算法的指数挑战的反应”
Response to "Exponential challenges in unbiasing quantum Monte Carlo algorithms with quantum computers"
论文作者
论文摘要
Mazzola和Carleo最近的预印本数字地研究了我们作品中引入的QC-QMC算法可能会引起的指数挑战，“公正的Fermionic量子蒙特卡洛（Monte Carlo）使用量子计算机”。正如我们原始论文所讨论的那样，我们同意这一普遍关注的问题。但是，在这里，我们提供了更多的细节和数字，以强调QC-QMC中实用量子优势的前景保持开放。 QC-QMC中的指数挑战取决于（1）QMC方法的选择，（2）基础系统以及（3）试验和Walker波函数的形式。尽管可以找到具有特定方法，特定系统和特定步行者/试验表的困难示例，但对于这些选择的某些组合，该方法可能比其他近期量子算法更可扩展。未来的研究应旨在确定QC-QMC实现实际量子优势的示例。
A recent preprint by Mazzola and Carleo numerically investigates exponential challenges that can arise for the QC-QMC algorithm introduced in our work, "Unbiasing fermionic quantum Monte Carlo with a quantum computer." As discussed in our original paper, we agree with this general concern. However, here we provide further details and numerics to emphasize that the prospects for practical quantum advantage in QC-QMC remain open. The exponential challenges in QC-QMC are dependent on (1) the choice of QMC methods, (2) the underlying system, and (3) the form of trial and walker wavefunctions. While one can find difficult examples with a specific method, a specific system, and a specific walker/trial form, for some combinations of these choices, the approach is potentially more scalable than other near-term quantum algorithms. Future research should aim to identify examples for which QC-QMC enables practical quantum advantage.
