  We propose and assess an alternative quantum generator architecture in the
context of generative adversarial learning for Monte Carlo event generation,
used to simulate particle physics processes at the Large Hadron Collider (LHC).
We validate this methodology by implementing the quantum network on artificial
data generated from known underlying distributions. The network is then applied
to Monte Carlo-generated datasets of specific LHC scattering processes. The new
quantum generator architecture leads to a generalization of the
state-of-the-art implementations, achieving smaller Kullback-Leibler
divergences even with shallow-depth networks. Moreover, the quantum generator
successfully learns the underlying distribution functions even if trained with
small training sample sets; this is particularly interesting for data
augmentation applications. We deploy this novel methodology on two different
quantum hardware architectures, trapped-ion and superconducting technologies,
to test its hardware-independent viability.
