用量子计算加速喷注生成，提升高能物理模拟效率。
Quantum Diffusion Model for Quark and Gluon Jet Generation
- 用随机幺正矩阵替代高斯噪声，构建全量子扩散模型。
- 在大型强子对撞机数据上，生成性能媲美经典模型。
- 适合对量子机器学习与粒子物理交叉研究感兴趣的读者。
扩散模型在图像生成中表现卓越，但训练过程计算量大、耗时长。本文提出一种新型扩散模型，利用量子计算技术缓解计算挑战并提升高能物理数据的生成性能。该全量子扩散模型在前向过程中以随机幺正矩阵替代高斯噪声，并在U-Net的去噪架构中引入变分量子电路。我们在大型强子对撞机（LHC）的复杂喷注结构数据集上进行评估，结果表明，全量子模型与混合量子模型在喷注生成任务上可与同规模经典模型相媲美，验证了量子技术在机器学习问题中的应用潜力。
原文摘要 · Abstract (English)
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.
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