Style-based quantum generative adversarial networks for Monte Carlo events
This is an incremental improvement for particle physics simulation, potentially aiding data augmentation in quantum computing applications.
The paper tackled the problem of simulating particle physics processes at the Large Hadron Collider by proposing a style-based quantum generative adversarial network for Monte Carlo event generation, achieving smaller Kullback-Leibler divergences with shallow-depth networks and learning distributions with small training sets.
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.