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Machine learning on quantifying quantum steerability
Ist Teil von
Quantum information processing, 2020-08, Vol.19 (8), Article 263
Ort / Verlag
New York: Springer US
Erscheinungsjahr
2020
Quelle
Springer LINK 全文期刊数据库
Beschreibungen/Notizen
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.