Detecting genuine multipartite entanglement via machine learning

In recent years, supervised and semisupervised machine learning methods such as neural networks, support vector machines (SVMs), and semisupervised support vector machines (S4VMs) 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 semisupervised 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 S4VM training method, 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.