Faster amplitude estimation
Faster amplitude estimation
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更快的幅度估计
DOI：
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发表时间：
2020
影响因子：
1
通讯作者：
Kouhei Nakaji
中科院分区：
文献类型：
--
作者：
Kouhei Nakaji
本文介绍了一种为近期量子计算机量身定做的高效的量子幅度估计算法。量子幅度估计是一个重要的问题，在量子化学、机器学习、金融等领域有着广泛的应用。由于著名的利用相位估计的量子幅度估计算法在近期量子计算机中不起作用，因此在最近的文献中已经提出了替代的方法。其中一些给出了几乎达到海森堡标度的上界的证明。然而，常量因子很大，因此边界是松散的。我们在本文中的贡献是提供了这样的算法，使得查询复杂度的上界几乎达到了Heisenberg尺度，并且恒定因子很小。
In this paper, we introduce an efficient algorithm for the quantum amplitude estimation task which is tailored for near-term quantum computers. The quantum amplitude estimation is an important problem which has various applications in fields such as quantum chemistry, machine learning, and finance. Because the well-known algorithm for the quantum amplitude estimation using the phase estimation does not work in near-term quantum computers, alternative approaches have been proposed in recent literature. Some of them provide a proof of the upper bound which almost achieves the Heisenberg scaling. However, the constant factor is large and thus the bound is loose. Our contribution in this paper is to provide the algorithm such that the upper bound of query complexity almost achieves the Heisenberg scaling and the constant factor is small.
影响因子：
6.4
作者：
Preskill, John
通讯作者：
Preskill, John