Response to"Exponential challenges in unbiasing quantum Monte Carlo algorithms with quantum computers"
Response to"Exponential challenges in unbiasing quantum Monte Carlo algorithms with quantum computers"
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发表时间：
2022-07
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通讯作者：
Joonho Lee;D. Reichman;R. Babbush;N. Rubin;F. Malone;B. O’Gorman;W. Huggins
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作者：
Joonho Lee;D. Reichman;R. Babbush;N. Rubin;F. Malone;B. O’Gorman;W. Huggins
Mazzola和Carleo最近的预印本数值研究了在我们的工作中引入的QC-QMC算法可能出现的指数挑战，“使用量子计算机的无偏费米子量子蒙特卡罗。“正如我们在原始文件中所讨论的，我们同意这一普遍关切。然而，在这里，我们提供了更多的细节和数值，以强调QC-QMC的实际量子优势的前景仍然是开放的。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）试验和步行者波函数的形式。虽然人们可以找到具有特定方法、特定系统和特定步行者/试验形式的困难示例，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具可扩展性。未来的研究应该旨在确定QC-QMC实现实际量子优势的例子。
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.