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Quantum Machine Learning

Entanglement Structure Detection via Machine Learning

arXiv
Authors: Changbo Chen, Changliang Ren, Hongqing Lin, He Lu

Year

2020

Paper ID

18836

Status

Preprint

Abstract Read

~2 min

Abstract Words

113

Citations

N/A

Abstract

Detecting the entanglement structure, such as intactness and depth, of an n-qubit state is important for understanding the imperfectness of the state preparation in experiments. However, identifying such structure usually requires an exponential number of local measurements. In this letter, we propose an efficient machine learning based approach for predicting the entanglement intactness and depth simultaneously. The generalization ability of this classifier has been convincingly proved, as it can precisely distinguish the whole range of pure generalized GHZ states which never exist in the training process. In particular, the learned classifier can discover the entanglement intactness and depth bounds for the noised GHZ state, for which the exact bounds are only partially known.

Why This Paper Matters

  • This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
  • It adds a 2020 reference point for readers tracking recent quantum research.
  • Detecting the entanglement structure, such as intactness and depth, of an n-qubit state is important for understanding the imperfectness of the state preparation in experiments.

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References & Citation Signals

[1] DOI https://doi.org/arXiv:2012.00526 [2] arXiv https://arxiv.org/abs/2012.00526 [3] Publisher https://arxiv.org/abs/2012.00526

Local Citation Graph (Related-Paper Links)

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