Classification of entanglement distribution using machine learning

Abstract

Abstract A classification of multipartite entanglement is introduced for pure and mixed states. The classification is based on the distribution of entanglement between the qubits of a given system, with a mathematical framework used to characterize fully entangled states. Then we use current machine learning and deep learning techniques to automatically classify a random state of two, three, and four qubits without the need to compute the amount of the different types of entanglement in each run; rather this is done only in the learning process. The technique shows high, near-perfect, accuracy in the case of pure states. As expected, this accuracy drops, more or less, when dealing with mixed states and when increasing the number of parties involved.