更快的幅度估计

Kouhei Nakaji
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引用次数: 49

摘要

本文介绍了一种针对近期量子计算机的量子振幅估计任务的高效算法。量子振幅估计是一个重要的问题,在量子化学、机器学习和金融等领域有着广泛的应用。由于使用相位估计进行量子振幅估计的著名算法在近期量子计算机中不起作用,因此在最近的文献中提出了替代方法。其中一些给出了几乎达到海森堡标度的上界的证明。然而,常数因子很大,因此边界很松散。我们在本文中的贡献是提供了一种算法,使得查询复杂度的上界几乎达到Heisenberg尺度,并且常数因子很小。
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Faster amplitude estimation
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.
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