Entanglement Structure Detection via Computer Vision
arxiv(2024)
摘要
Quantum entanglement plays a pivotal role in various quantum information
processing tasks. However, there still lacks a universal and effective way to
detecting entanglement structures, 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 CNN-Transformer model for both
the classification of GHZ and W states and the detection of various
entanglement structures. By leveraging the feature extraction capabilities of
CNNs and the powerful modeling abilities of Transformers, we can not only
effectively reduce the time and computational resources required for the
training process but 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
independent detection of multipartite entanglement.
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