1Quantum Research Centre, Technology Innovation Institute, Abu Dhabi, UAE
2Departament de Física Quàntica i Astrofísica and Institut de Ciències del Cosmos (ICCUB), Universitat de Barcelona, Barcelona, Spain.
3Theoretical Physics Department, CERN, CH-1211 Geneva 23, Switzerland.
4Institute of Physics, National Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan.
5TIF Lab, Dipartimento di Fisica, Università degli Studi di Milano and INFN Sezione di Milano, Milan, Italy.
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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.
Featured image: On the left, schematic steps involved in the style-qGAN training. On the right, marginal samples distributions for the physical observables, s, t, y in pp -> ttbar production at the LHC for the style-qGAN model.
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