I have built a quantum generative adversarial network model, in which the generator and the discriminator, both are quantum based model. The parametrized quantum circuit/ansatz of these two models are almost same as well (using rotation and entangling, CNOT, gates). My question is that I have got a 2D gaussian distribution (remember this is in Euclidean space !) and I want to implement quantum generator for this 2D gaussian distribution. But how should I encode this data into the generator, as simply applying data encoding (angle/amplitude encoding) won't work in this case. Is there any way that I can implement Q-GAN on a classical distribution and the output of that quantum generator should also be near to that classical distribution in the Euclidean space?



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