论文标题

修改的迭代量子振幅估计是渐近最佳的

Modified Iterative Quantum Amplitude Estimation is Asymptotically Optimal

论文作者

Fukuzawa, Shion, Ho, Christopher, Irani, Sandy, Zion, Jasen

论文摘要

在这项工作中,我们提供了第一种无QFT的算法,用于量子振幅估计(QAE),在保持领先的数值性能的同时,它在渐近上是最佳的。 QAE算法在许多用于量子计算机的应用中是子例程。 QAE的量子算法可实现的最佳查询复杂性是$ o \ left(\ frac {1}ε\ log \ frac {1}α\ right)$查询$ QUERIES $ QUERIES $ QUERIES,在任何其他经典算法上,对于同一问题的其他经典算法提供了$ 1/ε$的速度。 QAE的原始算法利用了量子傅立叶变换(QFT),这对于近期量子硬件来说是一个挑战。为了解决这个问题,人们有兴趣设计避免使用QFT的QAE算法。最近,Grinko等人引入了迭代QAE算法(IQAE)。用近乎最佳的$ o \ left(\ frac {1}ε\ log \ left(\ frac {1}α\ log \ frac {1}ε\ right)\ right)\ right)$ query复杂性和小常数因子。在这项工作中,我们结合了前面的工作中的想法,以引入一种无QFT的QAE算法,该算法获得了$ \ frac {62}ε\ ln \ frac {6}α$的查询复杂性上限,该算法与最佳$ o \ weft(\ frac {1} $ \ frac \ frac)复杂性。我们通过数字实验对分析进行补充,将我们的性能与IQAE进行比较,在该实验中,我们发现我们的修改保留了高性能,在某些情况下甚至可以改善数值结果。

In this work, we provide the first QFT-free algorithm for Quantum Amplitude Estimation (QAE) that is asymptotically optimal while maintaining the leading numerical performance. QAE algorithms appear as a subroutine in many applications for quantum computers. The optimal query complexity achievable by a quantum algorithm for QAE is $O\left(\frac{1}ε\log \frac{1}α\right)$ queries, providing a speedup of a factor of $1/ε$ over any other classical algorithm for the same problem. The original algorithm for QAE utilizes the quantum Fourier transform (QFT) which is expected to be a challenge for near-term quantum hardware. To solve this problem, there has been interest in designing a QAE algorithm that avoids using QFT. Recently, the iterative QAE algorithm (IQAE) was introduced by Grinko et al. with a near-optimal $O\left(\frac{1}ε\log \left(\frac{1}α \log \frac{1}ε\right)\right)$ query complexity and small constant factors. In this work, we combine ideas from the preceding line of work to introduce a QFT-free QAE algorithm that achieves a query complexity upper bound of $\frac{62}ε\ln\frac{6}α$ which matches the optimal $O\left(\frac{1}ε\log \frac{1}α\right)$ query complexity. We supplement our analysis with numerical experiments comparing our performance with IQAE where we find that our modifications retain the high performance, and in some cases even improve the numerical results.

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