Detected the steerability bounds of the generalized Werner states via BackPropagation neural network

Abstract

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.

References in corpus (10)

• Entanglement detection
• Steering, Entanglement, Nonlocality, and the EPR Paradox
• Experimental criteria for steering and the Einstein-Podolsky-Rosen paradox
• Entanglement, EPR-correlations, Bell-nonlocality, and Steering
• Einstein-Podolsky-Rosen steering provides the advantage in entanglement-assisted subchannel discrimination with one-way measurements
• Quantification of Gaussian quantum steering
• Fine-grained EPR-steering inequalities
• Analog of the Clauser-Horne-Shimony-Holt inequality for steering
• Neural network quantum state tomography in a two-qubit experiment
• Estimating the degree of non-Markovianity using machine learning

Cited by in corpus (5)

• Deep learning the hierarchy of steering measurement settings of qubit-pair states
• Measuring network quantum steerability utilizing artificial neural networks
• Quantifying Quantum Steering with Limited Resources: A Semi-supervised Machine Learning Approach
• Parameterized steering criteria via correlation matrices
• Detection of quantum information masking via machine learning