Quantum Diffusion Model for Quark and Gluon Jet Generation

TL;DR

This paper introduces a quantum diffusion model for generating quark and gluon jets, leveraging quantum computing to improve efficiency and performance in high energy physics data generation.

Contribution

It presents a novel quantum diffusion approach that replaces classical components with quantum techniques, advancing generative modeling in particle physics.

Findings

Quantum models are competitive with classical models for jet generation.

Quantum techniques can potentially reduce computational costs.

Hybrid quantum-classical models show promising results.

Abstract

Diffusion models have demonstrated remarkable success in image generation, but they are computationally intensive and time-consuming to train. In this paper, we introduce a novel diffusion model that benefits from quantum computing techniques in order to mitigate computational challenges and enhance generative performance within high energy physics data. The fully quantum diffusion model replaces Gaussian noise with random unitary matrices in the forward process and incorporates a variational quantum circuit within the U-Net in the denoising architecture. We run evaluations on the structurally complex quark and gluon jets dataset from the Large Hadron Collider. The results demonstrate that the fully quantum and hybrid models are competitive with a similar classical model for jet generation, highlighting the potential of using quantum techniques for machine learning problems.