Wang Yunfei, Liu Junyu
Joint Center for Quantum Information and Computer Science, NIST/University of Maryland, College Park, MD 20742, United States of America.
Maryland Center for Fundamental Physics, University of Maryland, College Park, MD 20742, United States of America.
Rep Prog Phys. 2024 Oct 15;87(11). doi: 10.1088/1361-6633/ad7f69.
Quantum machine learning, which involves running machine learning algorithms on quantum devices, has garnered significant attention in both academic and business circles. In this paper, we offer a comprehensive and unbiased review of the various concepts that have emerged in the field of quantum machine learning. This includes techniques used in Noisy Intermediate-Scale Quantum (NISQ) technologies and approaches for algorithms compatible with fault-tolerant quantum computing hardware. Our review covers fundamental concepts, algorithms, and the statistical learning theory pertinent to quantum machine learning.
量子机器学习，即涉及在量子设备上运行机器学习算法，已在学术界和商界引起了广泛关注。在本文中，我们对量子机器学习领域中出现的各种概念进行了全面且客观的综述。这包括噪声中等规模量子（NISQ）技术中使用的技术以及与容错量子计算硬件兼容的算法方法。我们的综述涵盖了与量子机器学习相关的基本概念、算法和统计学习理论。
