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Style-based quantum generative adversarial networks for Monte Carlo events
[article]
2022
arXiv
pre-print
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
arXiv:2110.06933v2
fatcat:j4vxx2lkmbbbpgc3m6oo6qtlty