Towards Tensor Network Models for Low-Latency Jet Tagging on FPGAs
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866909991988887552 |
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| author | Coppi, Alberto Puljak, Ema Borella, Lorenzo Jaschke, Daniel Rico, Enrique Pierini, Maurizio Pazzini, Jacopo Triossi, Andrea Montangero, Simone |
| author_facet | Coppi, Alberto Puljak, Ema Borella, Lorenzo Jaschke, Daniel Rico, Enrique Pierini, Maurizio Pazzini, Jacopo Triossi, Andrea Montangero, Simone |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2601_10801 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Towards Tensor Network Models for Low-Latency Jet Tagging on FPGAs Coppi, Alberto Puljak, Ema Borella, Lorenzo Jaschke, Daniel Rico, Enrique Pierini, Maurizio Pazzini, Jacopo Triossi, Andrea Montangero, Simone Machine Learning High Energy Physics - Experiment Instrumentation and Detectors Quantum Physics 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. |
| title | Towards Tensor Network Models for Low-Latency Jet Tagging on FPGAs |
| topic | Machine Learning High Energy Physics - Experiment Instrumentation and Detectors Quantum Physics |
| url | https://arxiv.org/abs/2601.10801 |