Dual-Parameterized Quantum Circuit GAN Model in High Energy Physics

TL;DR

This paper introduces a dual-Parameterized Quantum Circuit GAN with two quantum generators and a classical discriminator, designed to efficiently generate high-energy physics simulation data, potentially surpassing classical methods in speed and scalability.

Contribution

The paper proposes a novel dual-PQC GAN architecture with two quantum generators for improved simulation of calorimeter outputs in high-energy physics.

Findings

Successfully reproduces calorimeter image distributions

Reduces data size while maintaining accuracy

Shows potential for scaling to real calorimeter data

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.