论文标题
改进的最大似然量子振幅估计
Improved maximum-likelihood quantum amplitude estimation
论文作者
论文摘要
量子振幅估计是许多强大的量子算法中的关键子例程，包括量子增强的蒙特卡洛模拟和量子机学习。最大似然量子振幅估计（MLQAE）是许多最近的方法之一，这些方法比基于量子相估计的原始算法采用了简单得多的量子电路。在本文中，我们加深了对MLQAE的分析，以使算法更具规范性形式，包括量子电路深度有限的方案。在此过程中，我们观察并解释了算法无法实现所需精度的目标幅度的“特殊”值的特定范围。然后，我们提出并在数字上验证对算法的启发式修改以克服这个问题，从而使该算法更接近成为近期和中期量子硬件的实用子例程。
Quantum amplitude estimation is a key subroutine in a number of powerful quantum algorithms, including quantum-enhanced Monte Carlo simulation and quantum machine learning. Maximum-likelihood quantum amplitude estimation (MLQAE) is one of a number of recent approaches that employ much simpler quantum circuits than the original algorithm based on quantum phase estimation. In this article, we deepen the analysis of MLQAE to put the algorithm in a more prescriptive form, including scenarios where quantum circuit depth is limited. In the process, we observe and explain particular ranges of `exceptional' values of the target amplitude for which the algorithm fails to achieve the desired precision. We then propose and numerically validate a heuristic modification to the algorithm to overcome this problem, bringing the algorithm even closer to being useful as a practical subroutine on near- and mid-term quantum hardware.
