AI-Driven Quantum Entanglement Classification: A Novel Noise-Robust Approach

This paper introduces a new method to identify quantum entanglement in diverse quantum states by leveraging artificial intelligence. We establish a robust classifier by training a machine learning model on a synthetically generated dataset, enabling accurate differentiation of entangled states even amidst significant noise. The technique employs classical features derived from quantum state measurements, optimizing for sample efficiency. Numerical evaluations demonstrate this approach achieves high accuracy in classifying entangled multi-qubit states under various noisy environments.