Details

Autor(en) / Beteiligte

• Zhang, Ye-Qi
• Yang, Li-Juan
• He, Qi-Liang
• Chen, Liang

Titel

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.