| 摘要: |
| 近年来, 机器学习一直是被关注和探讨的研究热点, 被应用到各领域并在其中起着重要作用. 但随着数据量的不断增加, 机器学习算法训练时间越来越长. 与此同时, 量子计算机表现出强大的运算能力. 因此, 有研究人员尝试用量子计算的方法解决机器学习训练时间长的问题, 量子机器学习这一领域应运而生. 量子主成分分析、量子支持向量机、量子深度学习等量子机器学习算法相继被提出, 并有实验证明了量子机器学习算法有显著的加速效果, 使得量子机器学习的研究展现出逐步走高的趋势. 对量子机器学习算法进行综述. 首先介绍量子计算基础; 然后对量子监督学习、量子无监督学习、量子半监督学习、量子强化学习以及量子深度学习5类量子机器学习算法进行介绍; 接着对量子机器学习的相关应用进行介绍并给出了算法实验; 最后进行总结和展望. |
| 关键词: 量子机器学习 量子计算 机器学习 量子深度学习 量子实验 |
| DOI：10.13328/j.cnki.jos.007042 |
| 分类号: |
| 基金项目:中央高校基本科研业务费专项资金(2022YJS027); 国家自然科学基金(61502016); 国家重点研发计划(2020YFB2103800); 智能交通数据安全与隐私保护技术北京市重点实验室开放课题(202209300499) |
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| Survey on Quantum Machine Learning |
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WANG Jian1, ZHANG Rui1, JIANG Nan2
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1.Beijing Key Laboratory of Security and Privacy in Intelligent Transportation (Beijing Jiaotong University), Beijing 100044, China;2.Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
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| Abstract: |
| In recent years, machine learning has always been a research hotspot, and has been applied to various fields with an important role played. However, as the data amount continues to increase, the training time of machine learning algorithms is getting longer. Meanwhile, quantum computers demonstrate a powerful computing ability. Therefore, researchers try to solve the problem of long machine learning training time, which leads to the emergence of quantum machine learning. Quantum machine learning algorithms have been proposed, including quantum principal component analysis, quantum support vector machine, and quantum deep learning. Additionally, experiments have proven that quantum machine learning algorithms have a significant acceleration effect, leading to a gradual upward trend in research on quantum machine learning. This study reviews research on quantum machine learning algorithms. First, the fundamental concepts of quantum computing are introduced. Then, five quantum machine learning algorithms are presented, including quantum supervised learning, quantum unsupervised learning, quantum semi-supervised learning, quantum reinforcement learning, and quantum deep learning. Next, related applications of quantum machine learning are demonstrated with the algorithm experiments provided. Finally, the relevant summary and prospect of future study are discussed. |
| Key words: quantum machine learning quantum computing machine learning quantum deep learning quantum experiment |
