Quantum Computing (QC) claims to improve the efficiency of solving complex problems, compared to classical computing. When QC is applied to Machine Learning (ML) applications, it forms a Quantum Machine Learning (QML) system. After discussing the basic concepts of QC and its advantages over classical computing, this paper reviews the key aspects of QML in a comprehensive manner. We discuss different QML algorithms and their domain applicability, quantum datasets, hardware technologies, software tools, simulators, and applications. In this survey, we provide valuable information and resources for readers to jumpstart into the current state-of-the-art techniques in the QML field.

翻译：量子计算（QC）声称与经典计算相比能够提升复杂问题的求解效率。当量子计算应用于机器学习（ML）应用时，便构成了量子机器学习（QML）系统。在讨论量子计算的基本概念及其相较于经典计算的优势之后，本文全面综述了量子机器学习的关键方面。我们探讨了不同的QML算法及其领域适用性、量子数据集、硬件技术、软件工具、模拟器及应用场景。通过本综述，我们为读者提供宝贵的信息与资源，助其快速了解QML领域当前最先进的技术发展态势。