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Quantum Information Processing

Machine learning on quantifying quantum steerability

  • 01-08-2020
Published in:
Authors
Ye-Qi Zhang
Li-Juan Yang
Qi-Liang He
Liang Chen
Published in
Quantum Information Processing | Issue 8/2020

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Abstract

We apply the artificial neural network to quantify two-qubit steerability based on the steerable weight, which can be computed through semidefinite programming. Due to the fact that the optimal measurement strategy is unknown, it is still very difficult and time-consuming to efficiently obtain the steerability for an arbitrary quantum state. In this work, we show the method via machine learning technique which provides an effective way to quantify steerability. Furthermore, the generalization ability of the trained model is also demonstrated by applying to the Werner state and that in dephasing noise channel. Our findings provide an new way to obtain steerability efficiently and accurately, revealing effective application of the machine learning method on exploring quantum steering.

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previous article Quantum Fisher information-based detection of genuine tripartite entanglement
next article Generation of optical-photon-and-magnon entanglement in an optomagnonics-mechanical system
Title
Machine learning on quantifying quantum steerability
Authors
Ye-Qi Zhang
Li-Juan Yang
Qi-Liang He
Liang Chen
Publication date
01-08-2020
Publisher
Springer US
Published in
Quantum Information Processing / Issue 8/2020
Print ISSN: 1570-0755
Electronic ISSN: 1573-1332
DOI
https://doi.org/10.1007/s11128-020-02769-4
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