The recent advent of noisy intermediate-scale quantum devices, especially near-term quantum computers, has sparked extensive research efforts concerned with their possible applications. At the forefront of the considered approaches are variational methods that use parametrized quantum circuits. The classical and quantum Fisher information are firmly rooted in the field of quantum sensing and have proven to be versatile tools to study such parametrized quantum systems. Their utility in the study of other applications of noisy intermediate-scale quantum devices, however, has only been discovered recently. Hoping to stimulate more such applications, this article aims to further popularize classical and quantum Fisher information as useful tools for near-term applications beyond quantum sensing. We start with a tutorial that builds an intuitive understanding of classical and quantum Fisher information and outlines how both quantities can be calculated on near-term devices. We also elucidate their relationship and how they are influenced by noise processes. Next, we give an overview of the core results of the quantum sensing literature and proceed to a comprehensive review of recent applications in variational quantum algorithms and quantum machine learning.

中文翻译：

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嘈杂中尺度量子应用中的 Fisher 信息

最近出现的嘈杂的中等规模量子设备，尤其是近期量子计算机，引发了有关其可能应用的广泛研究工作。在所考虑的方法的最前沿是使用参数化量子电路的变分方法。经典和量子 Fisher 信息牢牢扎根于量子传感领域，并已被证明是研究此类参数化量子系统的通用工具。然而，它们在研究嘈杂的中等规模量子设备的其他应用中的效用直到最近才被发现。为了刺激更多此类应用，本文旨在进一步普及经典和量子 Fisher 信息，将其作为用于量子传感之外的近期应用的有用工具。我们从一个教程开始，该教程建立了对经典和量子 Fisher 信息的直观理解，并概述了如何在近期设备上计算这两个量。我们还阐明了它们的关系以及它们如何受到噪声过程的影响。接下来，我们概述了量子传感文献的核心结果，并全面回顾了变分量子算法和量子机器学习中的最新应用。