I didn’t find any model, paper or code of quantum GAN with RGB images posted on network. Our model is the patch quantum GAN and it is a hybrid model, some papers that I linked in the previous mail talk about this but they are applied to greyscale images. I think our implementation for RGB is correct becuase the training works, the problems of the wrong output may be: the network with RGB requires a more performant hardware otherwise we need to change something into the quantum generator. I noticed that in the standard dcgan pytorch the normal generator takes in input 4 dimension tensor (batch size, latent dim, 1, 1), instead the quantum generator takes 2 dimension tensor (batch size, n-
quibits). Our images are packed as (batch size, 64, 64, 3) so maybe we need to change something in the quantum generator. Can we work together in order to solve the problem and obtain the correct output ? Is there someone on Pennylane team who can help us?