用量子正交网络提升粒子物理视觉模型的性能与效率
Quantum Attention for Vision Transformers in High Energy Physics
- 将量子正交网络嵌入注意力机制，增强高维空间建模能力
- 在CMS开放数据上实现对夸克与胶子喷注的准确区分
- 适合关注量子机器学习在高能物理中应用的研究者
我们提出一种新型混合量子-经典视觉变换器架构，引入量子正交神经网络（QONNs）以提升高能物理应用中的性能与计算效率。基于量子视觉变换器的进展，该方法克服了以往模型在高维空间中的局限性，利用QONNs在高维空间中参数化高效且稳定的特性。我们在CMS开放数据的多探测器喷注图像上评估该架构，重点解决夸克起源与胶子起源喷注的区分任务。结果表明，在注意力机制中嵌入量子正交变换可实现稳健性能，并为未来高亮度大型强子对撞机面临的机器学习挑战提供良好可扩展性。本工作展示了量子增强模型在下一代粒子物理实验计算需求中的潜力。
原文摘要 · Abstract (English)
We present a novel hybrid quantum-classical vision transformer architecture incorporating quantum orthogonal neural networks (QONNs) to enhance performance and computational efficiency in high-energy physics applications. Building on advancements in quantum vision transformers, our approach addresses limitations of prior models by leveraging the inherent advantages of QONNs, including stability and efficient parameterization in high-dimensional spaces. We evaluate the proposed architecture using multi-detector jet images from CMS Open Data, focusing on the task of distinguishing quark-initiated from gluon-initiated jets. The results indicate that embedding quantum orthogonal transformations within the attention mechanism can provide robust performance while offering promising scalability for machine learning challenges associated with the upcoming High Luminosity Large Hadron Collider. This work highlights the potential of quantum-enhanced models to address the computational demands of next-generation particle physics experiments.
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