  Entanglement is one of the key resources of quantum information science which
makes identification of entangled states essential to a wide range of quantum
technologies and phenomena. This problem is however both computationally and
experimentally challenging. Here we use autoencoder neural networks to find
semi-optimal set of incomplete measurements that are most informative for the
detection of entangled states. We show that it is possible to find
high-performance entanglement detectors with as few as three measurements.
Also, with the complete information of the state, we develop a neural network
that can identify all two-qubits entangled states almost perfectly. This result
paves the way for automatic development of efficient entanglement witnesses and
entanglement detection using machine learning techniques.
