量子机器学习的全面审查:从NISQ到容错能力
Yunfei Wang1,2, Junyu Liu3,4,5,6
1Joint Center for Quantum Information and Computer Science, NIST/University of Maryland, College Park, MD 20742, United States of America.
Reports on progress in physics. Physical Society (Great Britain)
|September 25, 2024
概括
这篇评论探讨了量子机器学习,涵盖了噪音中等规模量子 (NISQ) 技术和容错算法. 它提供了对量子计算应用的基本概念,算法和统计学习理论的公正概述.
科学领域:
- 量子计算是一种量子计算.
- 机器学习 机器学习
- 人工智能的人工智能
背景情况:
- 量子机器学习 (QML) 将量子计算与机器学习相结合.
- 有关QML的学术界和行业的重大兴趣.
- 量子硬件的快速发展需要对QML概念进行审查.
研究的目的:
- 提供QML的全面和公正的审查.
- 涵盖噪音中等尺度量子 (NISQ) 和容错量子计算的技术.
- 在QML中巩固基本概念,算法和统计学习理论.
主要方法:
- 现有QML研究的文献综述.
- 基于硬件兼容性的QML技术的分类 (NISQ与故障耐受).
- 分析QML核心概念和相关的统计学学习理论.
主要成果:
- 详细概述QML算法及其理论基础.
- 讨论NISQ时代的挑战和机遇 QML.
- 探索未来容错量子计算的方法.
结论:
- QML是一个快速发展的领域,具有多样化的算法方法.
- 了解NISQ和容错范式对于QML开发至关重要.
- 进一步研究统计学学习理论对于推进QML至关重要.
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