# Quantum-inspired Machine Learning on high-energy physics data

arXiv: https://arxiv.org/abs/2004.13747 · HTML: https://mlsanity.com/paper/2004.13747/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Apr 2020.

## Built on by (most cited first, 30 of 31)

- [Implicit Regularization in Hierarchical Tensor Factorization and Deep Convolutional Neural Networks](https://mlsanity.com/paper/2201.11729/) (2022)
- [Hybrid Quantum-Classical Graph Convolutional Network](https://mlsanity.com/paper/2101.06189/) (2021)
- [Tensor networks for quantum machine learning](https://mlsanity.com/paper/2303.11735/) (2023)
- [Tensor networks for interpretable and efficient quantum-inspired machine learning](https://mlsanity.com/paper/2311.11258/) (2023)
- [On the Ability of Graph Neural Networks to Model Interactions Between Vertices](https://mlsanity.com/paper/2211.16494/) (2022)
- [Alternating Local Enumeration (TnALE): Solving Tensor Network Structure Search with Fewer Evaluations](https://mlsanity.com/paper/2304.12875/) (2023)
- [Quantum-inspired event reconstruction with Tensor Networks: Matrix Product States](https://mlsanity.com/paper/2106.08334/) (2021)
- [Classical versus Quantum: comparing Tensor Network-based Quantum Circuits on LHC data](https://mlsanity.com/paper/2202.10471/) (2022)
- [Domain-Aware Tensor Network Structure Search](https://mlsanity.com/paper/2505.23537/) (2025)
- [Tensor Network for Anomaly Detection in the Latent Space of Proton Collision Events at the LHC](https://mlsanity.com/paper/2506.00102/) (2025)
- [What Makes Data Suitable for a Locally Connected Neural Network? A Necessary and Sufficient Condition Based on Quantum Entanglement](https://mlsanity.com/paper/2303.11249/) (2023)
- [Ultra-low latency quantum-inspired machine learning predictors implemented on FPGA](https://mlsanity.com/paper/2409.16075/) (2024)
- [Towards LLM-guided Efficient and Interpretable Multi-linear Tensor Network Rank Selection](https://mlsanity.com/paper/2410.10728/) (2024)
- [Riemannian Optimization on Tree Tensor Networks with Application in Machine Learning](https://mlsanity.com/paper/2507.21726/) (2025)
- [Deep tensor networks with matrix product operators](https://mlsanity.com/paper/2209.09098/) (2022)
- [Using matrix-product states for time-series machine learning](https://mlsanity.com/paper/2412.15826/) (2024)
- [Plastic tensor networks for interpretable generative modeling](https://mlsanity.com/paper/2504.06722/) (2025)
- [Towards Tensor Network Models for Low-Latency Jet Tagging on FPGAs](https://mlsanity.com/paper/2601.10801/) (2026)
- [Hardware-Aware Tensor Networks for Real-Time Quantum-Inspired Anomaly Detection at Particle Colliders](https://mlsanity.com/paper/2603.26604/) (2026)
- [A Quantum Tensor Network-Based Viewpoint for Modeling and Analysis of Time Series Data](https://mlsanity.com/paper/2511.13514/) (2025)
- [Quantum-Inspired Robust and Scalable SAR Object Classification](https://mlsanity.com/paper/2604.25755/) (2026)
- [Quantum-classical simulation of quantum field theory by quantum circuit learning](https://mlsanity.com/paper/2311.16297/) (2023)
- [A Hybrid Classical-Quantum Fine Tuned BERT for Text Classification](https://mlsanity.com/paper/2511.17677/) (2025)
- [Quantum-inspired tensor networks in machine learning models](https://mlsanity.com/paper/2604.14287/) (2026)
- [Federated Learning with Quantum Enhanced LSTM for Applications in High Energy Physics](https://mlsanity.com/paper/2604.15775/) (2026)
- [Quantum inspired qubit qutrit neural networks for real time financial forecasting](https://mlsanity.com/paper/2604.18838/) (2026)
- [Classical Hardware Acceleration of Quantum Autoencoders for Real-Time Anomaly Detection in Collider Experiments](https://mlsanity.com/paper/2607.20302/) (2026)
- [Classical and Hybrid Quantum Machine Learning for Trigger-Like Event Selection on CMS Open Data: An Eight-Qubit, PCA-Constrained Benchmark](https://mlsanity.com/paper/2608.26224/) (2026)
- [Stochastic Optimization of Tree Tensor Networks](https://mlsanity.com/paper/2609.00870/) (2026)
- ["Transforming" LHCb: self-supervised maps of heavy-flavour decays](https://mlsanity.com/paper/2609.09275/) (2026)

## Abstract (arXiv)

Tensor Networks, a numerical tool originally designed for simulating quantum many-body systems, have recently been applied to solve Machine Learning problems. Exploiting a tree tensor network, we apply a quantum-inspired machine learning technique to a very important and challenging big data problem in high energy physics: the analysis and classification of data produced by the Large Hadron Collider at CERN. In particular, we present how to effectively classify so-called b-jets, jets originating from b-quarks from proton-proton collisions in the LHCb experiment, and how to interpret the classification results. We exploit the Tensor Network approach to select important features and adapt the network geometry based on information acquired in the learning process. Finally, we show how to adapt the tree tensor network to achieve optimal precision or fast response in time without the need of repeating the learning process. These results pave the way to the implementation of high-frequency real-time applications, a key ingredient needed among others for current and future LHCb event classification able to trigger events at the tens of MHz scale.