量子机器学习全面回顾：从 NISQ 到容错。

IF 19 1区物理与天体物理 Q1 PHYSICS, MULTIDISCIPLINARY Reports on Progress in Physics Pub Date : 2024-09-25 DOI:10.1088/1361-6633/ad7f69

Yunfei Wang,Junyu Liu

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引用次数: 0

摘要

量子机器学习涉及在量子设备上运行机器学习算法，在学术界和商界都引起了极大的关注。在本文中，我们对量子机器学习领域出现的各种概念进行了全面、公正的评述。其中包括噪声中量子（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。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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A comprehensive review of quantum machine learning: from NISQ to fault tolerance.

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.

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来源期刊

Reports on Progress in Physics 物理-物理：综合

CiteScore

31.90

自引率

0.00%

发文量

审稿时长

6-12 weeks

期刊介绍： Reports on Progress in Physics is a highly selective journal with a mission to publish ground-breaking new research and authoritative invited reviews of the highest quality and significance across all areas of physics and related areas. Articles must be essential reading for specialists, and likely to be of broader multidisciplinary interest with the expectation for long-term scientific impact and influence on the current state and/or future direction of a field.

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