Entanglement structure detection via computer vision

Quantum entanglement plays a pivotal role in various quantum information processing tasks. However, a universal and effective way to detect entanglement structures is still lacking, especially for high-dimensional and multipartite quantum systems. Noticing the mathematical similarities between the common representations of many-body quantum states and the data structures of images, we are inspired to employ advanced computer vision technologies for data analysis. In this work, we propose a hybrid convolutional neural network-transformer model for both the classification of Greenberger-Horne-Zeilinger and W states and the detection of various entanglement structures. By leveraging the feature-extraction capabilities of convolutional neural networks and the powerful modeling abilities of transformers, we not only can effectively reduce the time and computational resources required for the training process but can also obtain high detection accuracies. Through numerical simulation and physical verification, it is confirmed that our hybrid model is more effective than traditional techniques and thus offers a powerful tool for characterizing multipartite entanglement structures.