AI Framework for Quantum Steering

This paper presents a machine learning-based framework to determine the steerability of entangled quantum states. Steerability is a key concept in quantum information, and this work provides a novel approach to identify it. The use of machine learning to construct local hidden-state models is a significant contribution, potentially offering a more efficient way to analyze complex quantum states compared to traditional analytical methods. The validation on Werner and isotropic states demonstrates the framework's effectiveness and its ability to reproduce known results, while also exploring the advantages of POVMs.