Dual-Parameterized Quantum Circuit GAN Model in High Energy Physics

Abstract

Generative models, and Generative Adversarial Networks (GAN) in particular, are being studied as possible alternatives to Monte Carlo simulations. It has been proposed that, in certain circumstances, simulation using GANs can be sped-up by using quantum GANs (qGANs). We present a new design of qGAN, the dual-Parameterized Quantum Circuit (PQC) GAN, which consists of a classical discriminator and two quantum generators which take the form of PQCs. The first PQC learns a probability distribution over N-pixel images, while the second generates normalized pixel intensities of an individual image for each PQC input. With a view to HEP applications, we evaluated the dual-PQC architecture on the task of imitating calorimeter outputs, translated into pixelated images. The results demonstrate that the model can reproduce a fixed number of images with a reduced size as well as their probability distribution and we anticipate it should allow us to scale up to real calorimeter outputs.

Hybrid quantum classical graph neural networks for particle track reconstruction

“…There are many problems investigated. This include but not limited to physics analysis at LHC using kernel (Wu et al 2021a;Heredge et al 2021) and variational methods (Wu et al 2021b;Terashi et al 2021), simulating parton showers (Jang et al 2021) and imitating calorimeter outputs using Quantum Generative Adversarial Networks (Chang et al 2021).…”

Snowmass White Paper: Quantum Computing Systems and Software for High-energy Physics Research

“…One example includes algorithms to construct physics objects amenable to analysis from the signals generated in a particle detector-i.e., the clustering of detector hits into so-called tracks for reconstructing a particle's trajectory [81][82][83][84][85][86][87][88] or tracks and calorimeter energy depositions into jets [89][90][91]. Furthermore, quantum-assisted algorithms have been explored in unsupervised learning settings to classify jets according to their origin (b-tagging) [92], generative tasks [93][94][95], and the selection of events or interactions along with background suppression [3,13,[96][97][98][99][100][101][102][103][104][105][106][107][108]. In particular, generative models have been explored extensively as an alternative for the simulation of particle interactions and the detector's response to such interactions [4,94].…”

“…Furthermore, quantum-assisted algorithms have been explored in unsupervised learning settings to classify jets according to their origin (b-tagging) [92], generative tasks [93][94][95], and the selection of events or interactions along with background suppression [3,13,[96][97][98][99][100][101][102][103][104][105][106][107][108]. In particular, generative models have been explored extensively as an alternative for the simulation of particle interactions and the detector's response to such interactions [4,94].…”

Style-based quantum generative adversarial networks for Monte Carlo events

“…It is important to highlight that several research groups from the high-energy physics (HEP) community are investigating potential applications of quantum technologies in HEP applications and obtaining interesting results [42][43][44][45][46][47]. Therefore, the study presented in this manuscript should be considered as proof-of-concept, providing a robust and reproducible starting point for future investigations.…”