Stack operation of tensor networks
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Abstract (arXiv)
The tensor network, as a facterization of tensors, aims at performing the operations that are common for normal tensors, such as addition, contraction and stacking. However, due to its non-unique network structure, only the tensor network contraction is so far well defined. In this paper, we propose a mathematically rigorous definition for the tensor network stack approach, that compress a large amount of tensor networks into a single one without changing their structures and configurations. We illustrate the main ideas with the matrix product states based machine learning as an example. Our results are compared with the for loop and the efficient coding method on both CPU and GPU.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 13 of its 39 references that are in the map. First posted on arXiv 28 Mar 2022.
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References · 13
- 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
- 2017In-Datacenter Performance Analysis of a Tensor Processing Unitcited by 610
- 2015Tensorizing Neural Networkscited by 246
- 2017Unsupervised Generative Modeling Using Matrix Product Statescited by 87
- 2019Tree Tensor Networks for Generative Modelingcited by 63
- 2017Learning Relevant Features of Data with Multi-scale Tensor Networkscited by 50
- 2017Machine Learning by Unitary Tensor Network of Hierarchical Tree Structurecited by 39
- 2018From probabilistic graphical models to generalized tensor networks for supervised learningcited by 31
- 2020Supervised Learning with Projected Entangled Pair Statescited by 30
- 2019TensorNetwork for Machine Learningcited by 29
- 2017Equivalence of restricted Boltzmann machines and tensor network statescited by 26
- 2019TensorNetwork: A Library for Physics and Machine Learningcited by 21
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- 2022Generative modeling with projected entangled-pair statescited by 14
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