  Entanglement, which quantifies non-local correlations in quantum mechanics,
is the fascinating concept behind much of aspiration towards quantum
technologies. Nevertheless, directly measuring the entanglement of a
many-particle system is very challenging. Here we show that via supervised
machine learning using a convolutional neural network, we can infer the
entanglement from a measurable observable for a disordered interacting quantum
many-particle system. Several structures of neural networks were tested and a
convolutional neural network akin to structures used for image and speech
recognition performed the best. After training on a set of 500 realizations of
disorder, the network was applied to 200 new realizations and its results for
the entanglement entropy were compared to a direct computation of the
entanglement entropy. Excellent agreement was found, except for several rare
region which in a previous study were identified as belonging to an inclusion
of a Griffiths-like quantum phase. Training the network on a test set with
different parameters (in the same phase) also works quite well.
