Stack operation of tensor networks

Abstract

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.

arXiv comment9 pages, 10 figures, close to the online published version, for the code on Github, see this https://github.com/veya2ztn/Stack_of_Tensor_Network

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Cited by in corpus (2)

• High-fidelity realization of the AKLT state on a NISQ-era quantum processor
• Efficient preparation of the AKLT State with Measurement-based Imaginary Time Evolution