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Tensor Network Compressed Sensing with Unsupervised Machine Learning by Ran, Shi-Ju ;Sun, Zheng-Zhi ;Fei, Shao-Ming ;Su, Gang ;Lewenstein, Maciej is a Physics and Astronomy article available to read on EtoBox.

What is Tensor Network Compressed Sensing with Unsupervised Machine Learning about?

We propose the tensor-network compressed sensing (TNCS) by incorporating the ideas of compressed sensing, tensor network (TN), and machine learning. The primary idea is to compress and communicate the real-life information through the generative TN state and by making projective measurements in a designed way. First, the state | is obtained by the unsupervised learning of TN, and then the data to be communicated are encoded in the separable state with the minimal distance to the projected state | , where | can be acquired by partially projecting | . A protocol analogous to the compressed sensing assisted by neural-network machine learning is thus suggested, where the projections are designed to rapidly minimize the uncertainty of information in | . To characterize the efficiency of TNCS, we propose a quantity named as q sparsity to describe the sparsity of quantum states, which is analogous to the sparsity of the signals required in the standard compressed sensing. The need of the q sparsity in TNCS is essentially due to the fact that the TN states obey the area law of entanglement entropy. The tests on the real-life data (handwritten digits and fashion images) show that the TNCS h

Who reads Tensor Network Compressed Sensing with Unsupervised Machine Learning?

Author

Ran, Shi-Ju ;Sun, Zheng-Zhi ;Fei, Shao-Ming ;Su, Gang ;Lewenstein, Maciej

Publisher

American Physical Society (APS)

Published

2020

Language

EN

Field

Physics and Astronomy (Physical Sciences)