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I read some papers on Quantum GANs, for instance this one and this one. I also noticed all the main quantum computing frameworks have a tutorial on quantum GANs, e.g. qiskit.

However I don't really get what are the practical advantages of the quantum version. Is it a matter of speedup of the convergence of the model ? Is it related to the expressiveness of the model ? In general, what can I expect this quantum version to be better at and why should I choose it ?

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Loading Data into quantum circuit is generally a hard problem which by using QGAN we can reduce it's time complexity Quantum Generative Adversarial Networks for learning and loading random distributions. Generally the time complexity of QGAN is $O(ploylog N)$ which exponantialy faster than classical counterpart $O(N)$.

Also there are some use cases in probing the chemical space using thier large hilbert space Quantum Generative Models for Small Molecule Drug Discovery

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  • $\begingroup$ Can you point me out to an application closer to the field of data science where using the qGANs is indeed better ? I'm a computer scientist exploring this field for the kind of topics I usually work with, then unfortunately a usecase related to chemistry is not helpful for me $\endgroup$
    – mpro
    Jul 6, 2022 at 7:50
  • $\begingroup$ Also, can you please clarify the complexity of what is O(N) vs O(polylog N)? Is it the complexity to perform a training step or maybe the complexity of generating an image ? $\endgroup$
    – mpro
    Jul 7, 2022 at 7:09
  • $\begingroup$ Basically the QGAN introduced in that paper can be tested in other areas, it's a hybrid quantum generator and classical discriminator so there is a flexibility in building the model. The complicity in for 'low rank matrix computation' or training and predicting. $\endgroup$
    – Farhad
    Jul 11, 2022 at 22:51
  • $\begingroup$ Hello, thanks for you answer. I fear I did not understand the last part related to (I guess) the computational complexity $\endgroup$
    – mpro
    Jul 12, 2022 at 8:04

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