  We use error BackPropagation (BP) neural network to determine whether an
arbitrary two-qubit quantum state is steerable and optimize the steerability
bounds of the generalized Werner state. The results show that no matter how we
choose the features for the quantum states, we can use the BP neural network to
construct several models to realize high-performance quantum steering
classifiers compared with the support vector machine (SVM). In addition, we
predict the steerability bounds of the generalized Werner states by using the
classifiers which are newly constructed by the BP neural network, that is, the
predicted steerability bounds are closer to the theoretical bounds. In
particular, high-performance classifiers with partial information of the
quantum states which we only need to measure in three fixed measurement
directions are obtained.
