Quantum Diffusion Model for Quark and Gluon Jet Generation
This work addresses computational challenges in generative models for high-energy physics researchers, but it is incremental as it builds on existing diffusion models with quantum enhancements.
The paper tackles the computational intensity of diffusion models by introducing a quantum diffusion model that uses random unitary matrices and variational quantum circuits, applied to quark and gluon jet generation from the Large Hadron Collider, with results showing competitive performance against classical models.
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.