Steerability detection of arbitrary 2-qubit state via machine learning

Abstract

Quantum steering is an important nonclassical resource for quantum information processing. However, even lots of steering criteria exist, it is still very difficult to efficiently determine whether an arbitrary two-qubit state shared by Alice and Bob is steerable or not, because the optimal measurement directions of Alice are unknown. In this work, we provide an efficient quantum steering detection scheme for arbitrary 2-qubit states with the help of machine learning, where Alice and Bob only need to measure in a few fixed measurement directions. In order to prove the validity of this method, we firstly realize a high performance quantum steering classifier with the whole information. Furthermore, a high performance quantum steering classifier with partial information is realized, where Alice and Bob only need to measure in three fixed measurement directions. Our method outperforms the existing methods in generic cases in terms of both speed and accuracy, opening up the avenues to explore quantum steering via the machine learning approach.

arXiv admin note: text overlap with arXiv:1705.01523, arXiv:1604.00501 by other authors

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Cited by in corpus (14)

• Direct Fidelity Estimation of Quantum States using Machine Learning
• Building separable approximations for quantum states via neural networks
• Detecting genuine multipartite entanglement via machine learning
• Einstein-Podolsky-Rosen steering based on semi-supervised machine learning
• Entanglement Verification with Deep Semi-supervised Machine Learning
• Detected the steerability bounds of the generalized Werner states via BackPropagation neural network
• Deep learning the hierarchy of steering measurement settings of qubit-pair states
• Learning entanglement breakdown as a phase transition by confusion
• Genuine multipartite entanglement verification with convolutional neural networks
• Unveiling the nonclassicality within quasi-distribution representations through deep learning
• Quantifying Quantum Steering with Limited Resources: A Semi-supervised Machine Learning Approach
• Parameterized steering criteria via correlation matrices
• Entanglement quantification from collective measurements processed by machine learning
• Detection of quantum information masking via machine learning