YuleZhang / QML-Summary

Summary about tensor network in machine learning.

Summary about tensor network (TN) in machine learning.

Workshop

• IJCAL: International Workshop on Tensor Network Representations in Machine Learning
• NeurIPS2020: Quantum tensor networks in machine learning
• ipam: Tensor Network States and Applications, APRIL 19 - 23, 2021

Basic TN Materials

• TN_tutorial是冉仕举老师对张量网络机器学习的一个详细的介绍，该仓库是Bilibili视频的配套代码，搭配食用效果更佳。
• Tensors.net是一个开源的网站，里面图解了各种张量网络算法，并给出了Python/Matlab/Julia的相关实现，简直良
• Tensor Networks提供了一些张量网络机器学习的视频、论文以及对应代码，也是做了于本仓库类似的内容，推荐大家结合代码去学习，事半功倍！
• awesome-quantum-machine-learning包含了全网所有的量子机器学习基础知识，算法，研究材料，项目以及项目描述，例如量子遗传算法、量子隐马尔可夫模型、基于主成分分析的量子分类算法等等，有很多比较前沿的工作在里面。
• tensornetwork.org张量网络算法、理论和软件的介绍

Helpful Tensor network library

Most of the tensor network contracted libraries are based on Python, and there are a small amount of C++, Matlab, which will be noted below

• TensorNetwork, Google's open source Python library in 2019 is currently the most widely used
• qml是用于量子机器学习的Python工具包，有回归、预测、学习曲线等等，能省很多功夫去造轮子
• netket是一个开源代码项目，它提供了用于通过ANN和ML研究多体量子系统的前沿方法，它是基于JAX构建的Python库。
• tenpy Python库，用于模拟具有张量网络的高度相关的量子系统，有比较详细的文档和算法演示示例（例如TEBD和DMRG）
• PyQPanda是本源量子开发的量子计算编程框架，提供了详细的文档和量子算法介绍，能够通过线路模拟算法。
• tncontract Python的开源张量网络库，比较小众，采用Numpy库做为后端，包含很多用于一维和二维张量网络的算法。
• TNML(C++) 出自大佬emstoudenmire之手
• ITensor(C++) 高效的张量网络计算库，提供了相关文档

Survery（Updating...）

2016

• Supervised Learning with Quantum-Inspired Tensor Networks [Code]
  • E.M. Stoudenmire et al. (NIPS 2016)

2018

• Tensorizing generative adversarial nets [Paper] [Code]
  • X. Cao, X. Zhao, Q. Zhao. [C] (ICCE Asia 2018 (International Conference On Consumer Electronics))
• Generative adversarial positive-unlabeled learning [Paper] [Code]
  • M. Hou, B. Chaib-draa, C. Li, Q. Zhao. [C] (IJCAI 2018)
• Unsupervised Generative Modeling Using Matrix Product States [arXiv] [Code]
  • Zhao-Yu Han et al. (PRX 2018)

2019

• Machine Learning by Two-Dimensional Hierarchical Tensor Networks: A Quantum Information Theoretic Perspective on Deep Architectures [arXiv] [Github]
  • Ding Liu et al. (NJP 2019)

2020

• Generative tensor network classification model for supervised machine learning [arXiv] [Code]
  • Zheng-Zhi Sun, et al. (PRB 2020)
• Tangent-Space Gradient Optimization of Tensor Network for Machine Learning [arXiv] [Code]
  • Zheng-Zhi Sun, et al. (PRE 2020)
• Multi-layered tensor networks for image classification [arXiv] [Code]
  • Raghavendra Selvan et al. (NeurIPS 2020)
• Quantum Tensor Network in Machine Learning: An Application to Tiny Object Classification [arXiv] [Code]
  • Fanjie Kong et al. (NeurIPS 2020)
• Tensor Networks for Medical Image Classification [Code]
  • Raghavendra Selvan et al. (MIDL 2020)
• Deep convolutional tensor network [arXiv] [Code]
  • Philip Blagoveschensky et al. (Preprint. Under review 2020)
• Evolutionary Topology Search for Tensor Network Decomposition [Paper] [Code]
  • C. Li, Z. Sun. [C] (ICML 2020)

2021

• Fully-Connected Tensor Network Decomposition and Its Application to Higher-Order Tensor Completion [Paper] [Code]
  • Y. Zheng, T. Huang, X. Zhao, Q. Zhao, T. Jiang (AAAI 2021)
• Locally orderless tensor networks for classifying two- and three-dimensional medical images [Paper] [Code]
  • Raghavendra Selvan, et, al. (MELBA 2021)
• Quantum-Classical Machine learning by Hybrid Tensor Networks [arXiv] [Code]
  • Fanjie Kong et, al. (Preprint 2021)
• Tensor networks for unsupervised machine learning [Paper] [Code]
  • Jing Liu et, al. (Preprint 2021)
• Segmenting two-dimensional structures with strided tensor networks [Paper] [Code]
  • Selvan et al. (IPMI-2021)