Tensor networks for unsupervised machine learning
文献类型：期刊论文
| 作者 | Liu, Jing; Li, Sujie1,2; Zhang, Jiang3; Zhang, Pan1,4,5
|
| 刊名 | PHYSICAL REVIEW E
 |
| 出版日期 | 2023 |
| 卷号 | 107期号:1页码:L012103 |
| ISSN号 | 2470-0045 |
| DOI | 10.1103/PhysRevE.107.L012103 |
| 英文摘要 | Modeling the joint distribution of high-dimensional data is a central task in unsupervised machine learning. In recent years, many interests have been attracted to developing learning models based on tensor networks, which have the advantages of a principle understanding of the expressive power using entanglement properties, and as a bridge connecting classical computation and quantum computation. Despite the great potential, however, existing tensor network models for unsupervised machine learning only work as a proof of principle, as their performance is much worse than the standard models such as restricted Boltzmann machines and neural networks. In this Letter, we present autoregressive matrix product states (AMPS), a tensor network model combining matrix product states from quantum many-body physics and autoregressive modeling from machine learning. Our model enjoys the exact calculation of normalized probability and unbiased sampling. We demonstrate the performance of our model using two applications, generative modeling on synthetic and real-world data, and reinforcement learning in statistical physics. Using extensive numerical experiments, we show that the proposed model significantly outperforms the existing tensor network models and the restricted Boltzmann machines, and is competitive with state-of-the-art neural network models. |
| 学科主题 | Physics |
| 语种 | 英语 |
| 源URL | [http://ir.itp.ac.cn/handle/311006/28087]  |
| 专题 | 理论物理研究所_理论物理所1978-2010年知识产出 |
| 作者单位 | 1.Beijing Normal Univ, Sch Syst Sci, Beijing 100875, Peoples R China 2.Chinese Acad Sci, Inst Theoret Phys, CAS Key Lab Theoret Phys, Beijing 100190, Peoples R China 3.Univ Chinese Acad Sci, Sch Phys Sci, Beijing 100049, Peoples R China 4.Swarma Res, Beijing 102308, Peoples R China 5.UCAS, Hangzhou Inst Adv Study, Sch Fundamental Phys & Math Sci, Hangzhou 310024, Peoples R China 6.Int Ctr Theoret Phys Asia Pacific, Beijing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Liu, Jing,Li, Sujie,Zhang, Jiang,et al. Tensor networks for unsupervised machine learning[J]. PHYSICAL REVIEW E,2023,107(1):L012103. |
| APA | Liu, Jing,Li, Sujie,Zhang, Jiang,&Zhang, Pan.(2023).Tensor networks for unsupervised machine learning.PHYSICAL REVIEW E,107(1),L012103. |
| MLA | Liu, Jing,et al."Tensor networks for unsupervised machine learning".PHYSICAL REVIEW E 107.1(2023):L012103. |
入库方式： OAI收割
来源：理论物理研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。