12.3 Quantum GANs in Qiskit

An early proposal of a QGAN was introduced by IBM researchers Zoufal, Lucchi, and Woerner [101] to learn a probability distribution using a QGAN with a quantum generator and a classical discriminator. In this section, we will discuss how to implement this kind of QGAN with Qiskit, so let’s put everything in more precise terms.

This type of quantum GAN is given a dataset of real numbers that follow a certain probability distribution. This distribution may potentially be continuous, but it could be discretized to take some values with ; this will usually be done by fixing the values and , rounding the samples and ignoring those that are smaller than or bigger than . Each of the resulting labels will have a certain probability of appearing in the dataset. That is the distribution that we want the generator in our QGAN to learn.

And what does the generator of these QGANs look like? It is a quantum generator that is dependent on some classical parameters...