量子机器学习综述：从嘈杂中等规模量子到容错量子计算
A comprehensive review of Quantum Machine Learning: from NISQ to Fault Tolerance
- Kadanoff Center for Theoretical Physics, The University of Chicago（芝加哥大学卡达诺夫理论物理中心）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文全面综述了量子机器学习领域，涵盖从NISQ技术到容错量子计算硬件的基础概念、算法和统计学习理论。
AI中文摘要：
量子机器学习涉及在量子设备上运行机器学习算法，已在学术界和产业界引起广泛关注。本文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，涵盖用于嘈杂中等规模量子（NISQ）技术的各类方法，以及与容错量子计算硬件兼容的算法方案。综述内容包括基础概念、算法以及与量子机器学习相关的统计学习理论。
英文摘要：
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.
