mlsanityData to 14 Oct 2026

Tensor networks for interpretable and efficient quantum-inspired machine learning

arXiv 2311.11258 ↗First posted 19 Nov 2023quant-ph cs.AI cs.LGcited by 17

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Abstract (arXiv)

It is a critical challenge to simultaneously gain high interpretability and efficiency with the current schemes of deep machine learning (ML). Tensor network (TN), which is a well-established mathematical tool originating from quantum mechanics, has shown its unique advantages on developing efficient ``white-box'' ML schemes. Here, we give a brief review on the inspiring progresses made in TN-based ML. On one hand, interpretability of TN ML is accommodated with the solid theoretical foundation based on quantum information and many-body physics. On the other hand, high efficiency can be rendered from the powerful TN representations and the advanced computational techniques developed in quantum many-body physics. With the fast development on quantum computers, TN is expected to conceive novel schemes runnable on quantum hardware, heading towards the ``quantum artificial intelligence'' in the forthcoming future.

Key facts. Cited by 17 papers in the map (data through 14 Oct 2026). Builds on 57 of its 192 references that are in the map. First posted on arXiv 19 Nov 2023.

Builds on

57 of 192 references.

References · 57

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38 more
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50 most cited of 57 All 57 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 3; 2025: 7; 2026: 6.

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  1. 2023Tensor Networks for Explainable Machine Learning in Cybersecuritycited by 14
  2. 2025Interpretable Machine Learning in Physics: A Reviewcited by 14
  3. 2025SHAP Meets Tensor Networks: Provably Tractable Explanations with Parallelismcited by 8
  4. 2024Federated Hierarchical Tensor Networks: a Collaborative Learning Quantum AI-Driven Framework for Healthcarecited by 7
  5. 2025Deep Tree Tensor Networkscited by 5
  6. 2025tn4ml: Tensor Network Training and Customization for Machine Learningcited by 5
  7. 2025Tensor Network for Anomaly Detection in the Latent Space of Proton Collision Events at the LHCcited by 5
  8. 2026Hardware-Aware Tensor Networks for Real-Time Quantum-Inspired Anomaly Detection at Particle Colliderscited by 2
  9. 2024Universal scaling laws in quantum-probabilistic machine learning by tensor network towards interpreting representation and generalization powerscited by 1
  10. 2024Tensor Network Estimation of Distribution Algorithmscited by 1
  11. 2025A Quantum Tensor Network-Based Viewpoint for Modeling and Analysis of Time Series Datacited by 1
  12. 2025Dimension reduction with structure-aware quantum circuits for hybrid machine learningcited by 0
5 more
  1. 2026Structured Unitary Tensor Network Representations for Circuit-Efficient Quantum Data Encodingcited by 0
  2. 2026Quantum-inspired tensor networks in machine learning modelscited by 0
  3. 2026A Hamiltonian-Inspired Local-Operator Ansatz for Slimming Large Language Modelscited by 0
  4. 2026LLMs Interpret, Embeddings Organize, Graphs Emerge: Agent-Driven Compilation of Scientific Knowledgecited by 0
  5. 2026Gaussian Universality and Its Breakdown in Tensor-Network Machine Learningcited by 0