Style-based quantum generative adversarial networks for Monte Carlo events

TL;DR

This paper introduces a novel quantum generator architecture for GANs to simulate particle physics events, demonstrating improved performance and hardware independence on LHC data.

Contribution

The paper presents a new quantum generator architecture for GANs that outperforms existing models and is adaptable to different quantum hardware platforms.

Findings

Achieves smaller Kullback-Leibler divergences with shallow networks

Learns underlying distributions with small training samples

Demonstrates hardware-independent viability on trapped-ion and superconducting devices

Abstract

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.