CERN Accelerating science

Article
Report number	arXiv:2110.06933 ; CERN-TH-2021-139 ; TIF-UNIMI-2021-14
Title	Style-based quantum generative adversarial networks for Monte Carlo events
Author(s)	Bravo-Prieto, Carlos (ICC, Barcelona U. ; Technol. Innovation Inst., UAE ) ; Baglio, Julien (CERN) ; Cè, Marco (CERN) ; Francis, Anthony (CERN ; Taiwan, Natl. Chiao Tung U.) ; Grabowska, Dorota M. (CERN) ; Carrazza, Stefano (Technol. Innovation Inst., UAE ; CERN ; Milan U. ; INFN, Milan)
Publication	2022-08-17
Imprint	2021-10-13
Number of pages	15
Note	15 pages, 10 figures, accepted in Quantum, code available in https://github.com/QTI-TH/style-qgan
In:	Quantum 6 (2022) 777
DOI	10.22331/q-2022-08-17-777 (publication)
Subject category	hep-ph ; Particle Physics - Phenomenology ; cs.LG ; Computing and Computers ; quant-ph ; General Theoretical Physics
Accelerator/Facility, Experiment	CERN LHC
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.
Copyright/License	publication: (License: CC-BY-4.0) preprint: (Licenses: arXiv nonexclusive-distrib 1.0, CC BY-NC-ND 4.0)

Corresponding record in: Inspire