用张量网络模型实现毫微秒级粒子对撞机触发，兼顾速度与精度。
Towards Tensor Network Models for Low-Latency Jet Tagging on FPGAs
- 采用矩阵乘积态和树状张量网络，压缩模型体积提升推理速度。
- 在低延迟条件下达到与先进深度学习相当的分类性能。
- 适合高能物理实时触发系统，硬件部署资源占用极低。
我们系统研究了张量网络模型（矩阵乘积态MPS与树状张量网络TTN）在高能物理实时喷注识别中的应用，聚焦于在现场可编程门阵列（FPGA）上的低延迟部署。针对未来高亮度大型强子对撞机（HL-LHC）一级触发系统的严苛要求，探索张量网络作为紧凑且可解释的深度神经网络替代方案。基于喷注低层次构成特征，模型性能媲美当前最优深度学习分类器。通过后训练量化实现硬件高效部署，不损失分类性能或增加延迟。最佳模型经综合后估算其在FPGA上的资源消耗、延迟与内存占用，显示延迟低于1微秒，验证了其在在线触发系统中实时部署的可行性。本研究凸显了张量网络模型在低延迟环境中快速、高效推理的潜力。
原文摘要 · Abstract (English)
We present a systematic study of Tensor Network (TN) models $\unicode{x2013}$ Matrix Product States (MPS) and Tree Tensor Networks (TTN) $\unicode{x2013}$ for real-time jet tagging in high-energy physics, with a focus on low-latency deployment on Field Programmable Gate Arrays (FPGAs). Motivated by the strict requirements of the HL-LHC Level-1 trigger system, we explore TNs as compact and interpretable alternatives to deep neural networks. Using low-level jet constituent features, our models achieve competitive performance compared to state-of-the-art deep learning classifiers. We investigate post-training quantization to enable hardware-efficient implementations without degrading classification performance or latency. The best-performing models are synthesized to estimate FPGA resource usage, latency, and memory occupancy, demonstrating sub-microsecond latency and supporting the feasibility of online deployment in real-time trigger systems. Overall, this study highlights the potential of TN-based models for fast and resource-efficient inference in low-latency environments.
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