mlsanityData to 7 Oct 2026

Quantum-inspired event reconstruction with Tensor Networks: Matrix Product States

arXiv 2106.08334 ↗First posted 15 Jun 2021hep-ph cs.LG hep-excited by 9

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Abstract (arXiv)

Tensor Networks are non-trivial representations of high-dimensional tensors, originally designed to describe quantum many-body systems. We show that Tensor Networks are ideal vehicles to connect quantum mechanical concepts to machine learning techniques, thereby facilitating an improved interpretability of neural networks. This study presents the discrimination of top quark signal over QCD background processes using a Matrix Product State classifier. We show that entanglement entropy can be used to interpret what a network learns, which can be used to reduce the complexity of the network and feature space without loss of generality or performance. For the optimisation of the network, we compare the Density Matrix Renormalization Group (DMRG) algorithm to stochastic gradient descent (SGD) and propose a joined training algorithm to harness the explainability of DMRG with the efficiency of SGD.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 20 of its 82 references that are in the map. First posted on arXiv 15 Jun 2021.

Builds on

20 of 82 references.

References · 20

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2015Tensorizing Neural Networkscited by 246
  3. 2017Unsupervised Generative Modeling Using Matrix Product Statescited by 87
  4. 2016Learning to Pivot with Adversarial Networkscited by 55
  5. 2016Ultimate tensorization: compressing convolutional and FC layers alikecited by 55
  6. 2016Supervised Learning with Quantum-Inspired Tensor Networkscited by 35
  7. 2019MadMiner: Machine learning-based inference for particle physicscited by 32
  8. 2020Quantum-inspired Machine Learning on high-energy physics datacited by 30
  9. 2020Supervised Learning with Projected Entangled Pair Statescited by 30
  10. 2019TensorNetwork for Machine Learningcited by 29
  11. 2018Constraining Effective Field Theories with Machine Learningcited by 26
  12. 2020Anomaly Detection with Tensor Networkscited by 26
8 more
  1. 2019TensorNetwork: A Library for Physics and Machine Learningcited by 21
  2. 2020Tensor Networks for Medical Image Classificationcited by 20
  3. 2019Modeling Sequences with Quantum States: A Look Under the Hoodcited by 19
  4. 2020Entanglement and Tensor Networks for Supervised Image Classificationcited by 18
  5. 2020A Multi-Scale Tensor Network Architecture for Classification and Regressioncited by 13
  6. 2021Quantum Tensor Network in Machine Learning: An Application to Tiny Object Classificationcited by 9
  7. 2015Matrix Product State for Feature Extraction of Higher-Order Tensorscited by 6
  8. 2021Tensor-Train Recurrent Neural Networks for Interpretable Multi-Way Financial Forecastingcited by 6

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 3; 2024: 2; 2025: 1; 2026: 2.

9 papers.

  1. 2023Tensor networks for quantum machine learningcited by 16
  2. 2023Machine learning with tree tensor networks, CP rank constraints, and tensor dropoutcited by 14
  3. 2023Quantum Machine Learning on Near-Term Quantum Devices: Current State of Supervised and Unsupervised Techniques for Real-World Applicationscited by 14
  4. 2022Classical versus Quantum: comparing Tensor Network-based Quantum Circuits on LHC datacited by 7
  5. 2025Tensor Network for Anomaly Detection in the Latent Space of Proton Collision Events at the LHCcited by 5
  6. 2024Jet Discrimination with Quantum Complete Graph Neural Networkcited by 3
  7. 2024The role of data embedding in quantum autoencoders for improved anomaly detectioncited by 3
  8. 2026Hardware-Aware Tensor Networks for Real-Time Quantum-Inspired Anomaly Detection at Particle Colliderscited by 2
  9. 2026Quantum-Inspired Tensor Network Autoencoders for Anomaly Detection: A MERA-Based Approachcited by 0