Speaker
Description
We conduct a systematic study of quantum-inspired Tensor Network (TN) models—Matrix Product States (MPS) and Tree Tensor Networks (TTN)—for real-time jet tagging in high-energy physics, with a focus on low-latency deployment on FPGAs. Motivated by the strict computational demands of the HL-LHC Level-1 Trigger system, we explore TN architectures as compact and interpretable alternatives to deep neural networks. Our models are trained on jet events represented by low-level features of jet constituents. Benchmarked against state-of-the-art deep learning classifiers, they demonstrated competitive performance in terms of classification accuracy and AUC. We implement quantization-aware training for TTNs and successfully deploy the best-performing models on FPGA hardware, evaluating DSP usage, latency and memory usage. We are currently working on extending the support for the quantization of MPS models and synthesizing their designs for full FPGA deployment, to be able to compare them with TTNs in terms of both performance and hardware cost. This work aims to highlight the potential of TN-based models for fast, resource-efficient inference in low-latency environments such as the LHC.