Quantum computing is the process of performing calculations using quantum mechanics. This field studies the quantum behavior of certain subatomic particles for subsequent use in performing calculations, as well as for large-scale information processing. These capabilities can give quantum computers an advantage in terms of computational time and cost over classical computers. Nowadays, there are scientific challenges that are impossible to perform by classical computation due to computational complexity or the time the calculation would take, and quantum computation is one of the possible answers. However, current quantum devices have not yet the necessary qubits and are not fault-tolerant enough to achieve these goals. Nonetheless, there are other fields like machine learning or chemistry where quantum computation could be useful with current quantum devices. This manuscript aims to present a Systematic Literature Review of the papers published between 2017 and 2023 to identify, analyze and classify the different algorithms used in quantum machine learning and their applications. Consequently, this study identified 94 articles that used quantum machine learning techniques and algorithms. The main types of found algorithms are quantum implementations of classical machine learning algorithms, such as support vector machines or the k-nearest neighbor model, and classical deep learning algorithms, like quantum neural networks. Many articles try to solve problems currently answered by classical machine learning but using quantum devices and algorithms. Even though results are promising, quantum machine learning is far from achieving its full potential. An improvement in the quantum hardware is required since the existing quantum computers lack enough quality, speed, and scale to allow quantum computing to achieve its full potential.

翻译：量子计算是利用量子力学执行计算的过程。该领域研究某些亚原子粒子的量子行为，以便后续用于执行计算及大规模信息处理。这些能力可使量子计算机在计算时间和成本上优于经典计算机。当前，存在因计算复杂性或计算耗时过长而无法通过经典计算完成的科学挑战，而量子计算是可能的解决方案之一。然而，现有量子设备尚未具备足够的量子比特数量，且容错能力不足以实现这些目标。不过，在机器学习和化学等其他领域，量子计算借助现有设备已能发挥作用。本文旨在对2017年至2023年间发表的论文进行系统性文献综述，以识别、分析并分类量子机器学习中使用的不同算法及其应用。由此，本研究共识别出94篇采用量子机器学习技术与算法的文章。所发现的主要算法类型包括经典机器学习算法的量子实现（如支持向量机或k近邻模型）以及经典深度学习算法的量子实现（如量子神经网络）。许多文章尝试利用量子设备与算法解决当前由经典机器学习应对的问题。尽管结果令人期待，但量子机器学习远未充分发挥其潜力。由于现有量子计算机在质量、速度和规模上仍存在不足，量子硬件需进一步改进，才能使量子计算充分发挥其潜能。