mlsanityData to 14 Oct 2026

Towards Tensor Network Models for Low-Latency Jet Tagging on FPGAs

arXiv 2601.10801 ↗First posted 15 Jan 2026cs.LG hep-ex physics.ins-detcited by 2

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Abstract (arXiv)

We present a systematic study of Tensor Network (TN) models unicodex2013 Matrix Product States (MPS) and Tree Tensor Networks (TTN) unicodex2013 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.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 15 of its 52 references that are in the map. First posted on arXiv 15 Jan 2026.

Builds on

15 of 52 references.

References · 15

  1. 2018Fast inference of deep neural networks in FPGAs for particle physicscited by 133
  2. 2022Particle Transformer for Jet Taggingcited by 98
  3. 2019ParticleNet: Jet Tagging via Particle Cloudscited by 64
  4. 2017Learning Relevant Features of Data with Multi-scale Tensor Networkscited by 51
  5. 2020Distance-Weighted Graph Neural Networks on FPGAs for Real-Time Particle Reconstruction in High Energy Physicscited by 34
  6. 2020Quantum-inspired Machine Learning on high-energy physics datacited by 31
  7. 2022LL-GNN: Low Latency Graph Neural Networks on FPGAs for High Energy Physicscited by 12
  8. 2024Ultrafast jet classification on FPGAs for the HL-LHCcited by 12
  9. 2025Fast Jet Tagging with MLP-Mixers on FPGAscited by 9
  10. 2024Real-time Anomaly Detection at the L1 Trigger of CMS Experimentcited by 7
  11. 2024Ultra Fast Transformers on FPGAs for Particle Physics Experimentscited by 6
  12. 2025Sub-microsecond Transformers for Jet Tagging on FPGAscited by 6
3 more
  1. 2025tn4ml: Tensor Network Training and Customization for Machine Learningcited by 5
  2. 2024Ultra-low latency quantum-inspired machine learning predictors implemented on FPGAcited by 4
  3. 2025Machine-learning based particle-flow algorithm in CMScited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026Hardware-Aware Tensor Networks for Real-Time Quantum-Inspired Anomaly Detection at Particle Colliderscited by 2
  2. 2026Classical Hardware Acceleration of Quantum Autoencoders for Real-Time Anomaly Detection in Collider Experimentscited by 0

Similar, not cited

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  1. 2025JEDI-linear: Fast and Efficient Graph Neural Networks for Jet Tagging on FPGAs
  2. 2021Fast convolutional neural networks on FPGAs with hls4ml
  3. 2026JetFormer: A Scalable and Efficient Transformer for Jet Tagging from Offline Analysis to FPGA Triggers
  4. 2023End-to-end codesign of Hessian-aware quantized neural networks for FPGAs and ASICs
  5. 2026PQuantML: A Tool for End-to-End Hardware-aware Model Compression
  6. 2023Efficient and Robust Jet Tagging at the LHC with Knowledge Distillation
  7. 20213U-EdgeAI: Ultra-Low Memory Training, Ultra-Low BitwidthQuantization, and Ultra-Low Latency Acceleration
  8. 2026Reconfigurable Computing Challenge: Transformer for Jet Tagging on Versal AI Engines