Low-cost detection of high-dimensional multipartite entanglement structures

Quantum entanglement detection and characterization are crucial for various quantum information processes. Most existing methods for entanglement detection rely heavily on a complete description of the quantum state, which requires numerous measurements and complex setups. This makes these theoretically sound approaches costly and impractical, as the system size increases. In this work, we propose a multiview neural-network model to generate representations suitable for entanglement structure detection. The number of required quantum measurements shows a significant reduction compared to conventional quantum state tomography approaches as the qubit number increases. This remarkable reduction in resource costs makes it possible to detect specific entanglement structures in large-scale systems. Numerical simulations show that our method achieves over 95% detection accuracy for up to 19 qubit systems. By enabling a universal, flexible, and resource-efficient analysis of entanglement structures, our approach enhances the capability of utilizing quantum states across a wide range of applications.