Quantum physics has changed the way we understand our environment, and one of its branches, quantum mechanics, has demonstrated accurate and consistent theoretical results. Quantum computing is the process of performing calculations using quantum mechanics. This field studies the quantum behavior of certain subatomic particles (photons, electrons, etc.) for subsequent use in performing calculations, as well as for large-scale information processing. These advantages are achieved through the use of quantum features, such as entanglement or superposition. These capabilities can give quantum computers an advantage in terms of computational time and cost over classical computers. Nowadays, scientific challenges are impossible to perform by classical computation due to computational complexity (more bytes than atoms in the observable universe) or the time it would take (thousands of years), and quantum computation is the only known answer. However, current quantum devices do not have yet the necessary qubits and are not fault-tolerant enough to achieve these goals. Nonetheless, there are other fields like machine learning, finance, or chemistry where quantum computation could be useful with current quantum devices. This manuscript aims to present a review of the literature published between 2017 and 2023 to identify, analyze, and classify the different types of algorithms used in quantum machine learning and their applications. The methodology follows the guidelines related to Systematic Literature Review methods, such as the one proposed by Kitchenham and other authors in the software engineering field. Consequently, this study identified 94 articles that used quantum machine learning techniques and algorithms and shows their implementation using computational quantum circuits or . 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. One of the most relevant applications in the machine learning field is image classification. Many articles, especially within the classification, 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 quantum hardware is required for this potential to be achieved since the existing quantum computers lack enough quality, speed, and scale to allow quantum computing to achieve its full potential.

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系统文献综述：量子机器学习及其应用

量子物理学改变了我们理解环境的方式，其分支之一——量子力学，已经证明了准确且一致的理论结果。量子计算是使用量子力学执行计算的过程。该领域研究某些亚原子粒子（光子、电子等）的量子行为，以便随后用于执行计算以及大规模信息处理。这些优点是通过使用量子特征（例如纠缠或叠加）来实现的。这些功能可以使量子计算机在计算时间和成本方面比传统计算机具有优势。如今，由于计算复杂性（比可观测宇宙中的原子更多的字节）或所需的时间（数千年），经典计算不可能完成科学挑战，而量子计算是唯一已知的答案。然而，当前的量子设备尚不具备必要的量子位，并且容错能力不足以实现这些目标。尽管如此，在机器学习、金融或化学等其他领域，量子计算也可以在当前的量子设备中发挥作用。本手稿旨在对 2017 年至 2023 年期间发表的文献进行回顾，以识别、分析和分类量子机器学习及其应用中使用的不同类型的算法。该方法遵循与系统文献综述方法相关的指南，例如Kitchenham 和软件工程领域其他作者提出的方法。因此，本研究确定了 94 篇使用量子机器学习技术和算法的文章，并展示了它们使用计算量子电路或 .已发现算法的主要类型是经典机器学习算法的量子实现，例如支持向量机或 k 最近邻模型，以及经典深度学习算法，例如量子神经网络。机器学习领域最相关的应用之一是图像分类。许多文章，尤其是分类文章，试图解决当前由经典机器学习解决但使用量子设备和算法的问题。尽管结果令人鼓舞，但量子机器学习还远未充分发挥其潜力。要实现这一潜力，需要改进量子硬件，因为现有的量子计算机缺乏足够的质量、速度和规模，无法让量子计算充分发挥其潜力。