Entanglement Verification with Deep Semi-supervised Machine Learning

Abstract

Quantum entanglement lies at the heart in quantum information processing tasks. Although many criteria have been proposed, efficient and scalable methods to detect the entanglement of generally given quantum states are still not available yet, particularly for high-dimensional and multipartite quantum systems. Based on FixMatch and Pseudo-Label method, we propose a deep semi-supervised learning model with a small portion of labeled data and a large portion of unlabeled data. The data augmentation strategies are applied in this model by using the convexity of separable states and performing local unitary operations on the training data. We verify that our model has good generalization ability and gives rise to better accuracies compared to traditional supervised learning models by detailed examples.

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• Entanglement detection
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Cited by in corpus (9)

• Detecting genuine multipartite entanglement via machine learning
• Entanglement Structure Detection via Computer Vision
• Genuine multipartite entanglement verification with convolutional neural networks
• High-dimentional Multipartite Entanglement Structure Detection with Low Cost
• Tightening the entropic uncertainty relations with quantum memory in a multipartite scenario
• QuFeX: Quantum feature extraction module for hybrid quantum-classical deep neural networks
• Quantifying Quantum Steering with Limited Resources: A Semi-supervised Machine Learning Approach
• Variable Hyperparameterized Gaussian Kernel using Displaced Squeezed Vacuum State
• Separability criteria based on the correlation tensor moments for arbitrary dimensional states