  Entangled states are an important resource for quantum computation,
communication, metrology, and the simulation of many-body systems. However,
noise limits the experimental preparation of such states. Classical data can be
efficiently denoised by autoencoders---neural networks trained in unsupervised
manner. We develop a novel quantum autoencoder that successfully denoises
Greenberger-Horne-Zeilinger states subject to spin-flip errors and random
unitary noise. Various emergent quantum technologies could benefit from the
proposed unsupervised quantum neural networks.
