Self-Attention for Quantum Entanglement Prediction

Quantum entanglement is a powerful resource in quantum mechanics and quantum information processing. However, its reliable quantification remains challenging due to the exponential growth of the underlying Hilbert space with system size, which renders full state reconstruction infeasible. Moreover, experimentally estimating entanglement typically requires a large number of measurement samples leading to a significant overhead. In this paper, we present two models, a feed-forward neural network and an attention-based model, to accurately predict the entanglement of random states. Our results demonstrate that machine-learning method consistently outperform conventional analytical approaches across a range of qubit numbers, highlighting the advantages of machine learning for the efficient quantification of quantum resources.