Quantum Diffusion Model for Quark and Gluon Jet Generation

2024-12-30

AI Summary

To address the high computational cost and the inability of classical generative models to capture quantum dynamical features in quark/gluon jet generation for high-energy physics, this paper proposes the first end-to-end fully quantized diffusion model. Methodologically, it replaces Gaussian noise with stochastic unitary matrices to define a quantum forward process and integrates variational quantum circuits (VQCs) into a U-Net denoising architecture to realize quantum-classical hybrid inversion. Its key contribution lies in the first unified application of a quantum diffusion framework to particle physics generative modeling—combining physical interpretability with architectural novelty. Experiments on LHC jet data demonstrate that the model achieves fidelity comparable to classical diffusion models, as measured by Fréchet Inception Distance (FID) and Jensen–Shannon Divergence (JSD), thereby validating the feasibility and competitiveness of quantum generative paradigms for high-dimensional, high-fidelity physics data modeling.

Technology Category

• Machine Learning: Quantum Machine Learning
• Computer Vision: Diffusion Models for Vision
• Natural Language Processing: Generation

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization

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.

Problem

• Quantum Physics
• Quark-Gluon Dynamics
• High-Energy Physics Data Analysis

Innovation

• Quantum Computing
• Machine Learning
• Quark-Gluon Jet Image Generation