We build on our prior quantum machine learning work to design and train a quantum GAN. We first demonstrate that it is a truly quantum network by showing that it can successfully classify the entanglement of input quantum states, an open NP-hard problem that is purely quantum mechanical and has no classical analog. We then apply the method of our quantum discriminator in our QGAN to image classification, and demonstrate both efficient encoding of images into quantum states and successful discrimination of database images.