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Machine learning on quantifying quantum steerability

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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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Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grant Nos. 11805065 and 11504106, the Natural Science and Technology Foundation of Guizhou Province under Grant No. [2017]7343, the Key laboratory of low dimensional condensed matter physics of higher educational institution of Guizhou province (Grant No.[2016]002) and also by the Fundamental Research Funds for the Central Universities (Grant No. 2018072).

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Authors and Affiliations

  1. Department of Mathematics and Physics, North China Electric Power University, Beijing, 102206, China

    Ye-Qi Zhang, Li-Juan Yang & Liang Chen

  2. School of Physics and Electronics, Guizhou Normal University, Guiyang, 550001, China

    Qi-Liang He

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Correspondence to Ye-Qi Zhang.

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Zhang, YQ., Yang, LJ., He, QL. et al. Machine learning on quantifying quantum steerability. Quantum Inf Process 19, 263 (2020). https://doi.org/10.1007/s11128-020-02769-4

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