Einstein-Podolsky-Rosen steering based on semi-supervised machine learning

Abstract

Einstein-Podolsky-Rosen(EPR)steering is a kind of powerful nonlocal quantum resource in quantum information processing such as quantum cryptography and quantum communication. Many criteria have been proposed in the past few years to detect the steerability both analytically and numerically. Supervised machine learning such as support vector machines and neural networks have also been trained to detect the EPR steerability. To implement supervised machine learning, one needs a lot of labeled quantum states by using the semidefinite programming, which is very time consuming. We present a semi-supervised support vector machine method which only uses a small portion of labeled quantum states in detecting quantum steering. We show that our approach can significantly improve the accuracies by detailed examples.

References in corpus (6)

• Steering, Entanglement, Nonlocality, and the EPR Paradox
• Experimental criteria for steering and the Einstein-Podolsky-Rosen paradox
• Einstein-Podolsky-Rosen steering provides the advantage in entanglement-assisted subchannel discrimination with one-way measurements
• Fine-grained EPR-steering inequalities
• Geometric Bell-like inequalities for steering
• Temporal Steering in Four Dimensions with applications to coupled qubits and magnetoreception

Cited by in corpus (6)

• Detecting genuine multipartite entanglement via machine learning
• Entanglement Verification with Deep Semi-supervised Machine Learning
• Genuine multipartite entanglement verification with convolutional 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