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 2021 to identify, analyze and classify the different algorithms used in quantum machine learning and their applications. Consequently, this study identified 52 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:量子计算是使用量子力学进行计算的过程。 本字段研究某些亚原子粒子的量子行为, 用于随后进行计算, 以及用于大规模的信息处理。 这些能力可以使量子计算机在计算时间和成本方面比古典计算机具有优势。 现在,由于计算的复杂性或计算需要的时间, 有一些科学挑战无法通过古典计算来完成。 然而, 量子计算是可能的答案之一。 然而, 目前量子设备还没有必要的qubits, 也没有足够耐错以实现这些目标。 尽管如此, 还有其他一些领域, 比如机器学习或化学, 量子计算可以用当前的量子设备来进行。 这些能力可以让量子计算机在计算时间和成本方面拥有优势。 这些手稿的目的是为2017-2021年出版的论文提供系统文学评论, 以便识别、分析和分类量子机学习及其应用中所使用的不同算法。 因此, 所发现的主要算法类型是古典机的量学算算的量算应用量子, 比如支持矢量机或基近邻模型, 而古典的量质量计算质量计算质量计算方法可以用目前的精度计算方法, 而古典的精细的精度算算算算算法则要通过量级算系统学习现有的量级算算法, 。