Detecting genuine multipartite entanglement via machine learning

Abstract

In recent years, supervised and semi-supervised machine learning methods such as neural networks, support vector machines (SVM), and semi-supervised support vector machines (S4VM) have been widely used in quantum entanglement and quantum steering verification problems. However, few studies have focused on detecting genuine multipartite entanglement based on machine learning. Here, we investigate supervised and semi-supervised machine learning for detecting genuine multipartite entanglement of three-qubit states. We randomly generate three-qubit density matrices, and train an SVM for the detection of genuine multipartite entangled states. Moreover, we improve the training method of S4VM, which optimizes the grouping of prediction samples and then performs iterative predictions. Through numerical simulation, it is confirmed that this method can significantly improve the prediction accuracy.

9 pages, 8 figures

References in corpus (9)

• Entanglement detection
• Scalable multi-particle entanglement of trapped ions
• Entanglement Certification $-$ From Theory to Experiment
• Taming multiparticle entanglement
• Characterizing the entanglement of symmetric many-particle spin-1/2 systems
• Witnessing genuine multipartite entanglement with positive maps
• Cost of quantum entanglement simplified
• Quantum phase recognition via unsupervised machine learning
• Entanglement Verification with Deep Semi-supervised Machine Learning

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• Variable Hyperparameterized Gaussian Kernel using Displaced Squeezed Vacuum State
• Threetangle in the XY-model class with a non-integrable field background