mlsanityData to 14 Oct 2026

tn4ml: Tensor Network Training and Customization for Machine Learning

arXiv 2502.13090 ↗First posted 18 Feb 2025cs.LG cs.MS quant-phcited by 5

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Abstract (arXiv)

Tensor Networks have emerged as a prominent alternative to neural networks for addressing Machine Learning challenges in foundational sciences, paving the way for their applications to real-life problems. This paper introduces tn4ml, a novel library designed to seamlessly integrate Tensor Networks into optimization pipelines for Machine Learning tasks. Inspired by existing Machine Learning frameworks, the library offers a user-friendly structure with modules for data embedding, objective function definition, and model training using diverse optimization strategies. We demonstrate its versatility through two examples: supervised learning on tabular data and unsupervised learning on an image dataset. Additionally, we analyze how customizing the parts of the Machine Learning pipeline for Tensor Networks influences performance metrics.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 15 of its 46 references that are in the map. First posted on arXiv 18 Feb 2025.

Builds on

15 of 46 references.

References · 15

  1. 2023Cross-Entropy Loss Functions: Theoretical Analysis and Applicationscited by 210
  2. 2017Unsupervised Generative Modeling Using Matrix Product Statescited by 88
  3. 2016Supervised Learning with Quantum-Inspired Tensor Networkscited by 36
  4. 2023Tensor Networks Meet Neural Networks: A Survey and Future Perspectivescited by 32
  5. 2020Anomaly Detection with Tensor Networkscited by 26
  6. 2019TensorNetwork: A Library for Physics and Machine Learningcited by 21
  7. 2021Tensor networks for unsupervised machine learningcited by 21
  8. 2020Entanglement and Tensor Networks for Supervised Image Classificationcited by 18
  9. 2023Tensor networks for quantum machine learningcited by 17
  10. 2023Tensor networks for interpretable and efficient quantum-inspired machine learningcited by 17
  11. 2023Tensor Networks for Explainable Machine Learning in Cybersecuritycited by 14
  12. 2017Tensor Regression Networks with various Low-Rank Tensor Approximationscited by 8
3 more
  1. 2020Tangent-Space Gradient Optimization of Tensor Network for Machine Learningcited by 7
  2. 2022tntorch: Tensor Network Learning with PyTorchcited by 7
  3. 2023TensorKrowch: Smooth integration of tensor networks in machine learningcited by 5

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 4.

5 papers.

  1. 2025Tensor Network for Anomaly Detection in the Latent Space of Proton Collision Events at the LHCcited by 5
  2. 2026Towards Tensor Network Models for Low-Latency Jet Tagging on FPGAscited by 2
  3. 2026Hardware-Aware Tensor Networks for Real-Time Quantum-Inspired Anomaly Detection at Particle Colliderscited by 2
  4. 2026Tensor Methods for Language Models: From Token Representation to Training, Adaptation, Inference, Compression, and Interpretabilitycited by 1
  5. 2026Tensor Network Kernel Machines: A JAX Framework for Machine Learning and Nonlinear System Identificationcited by 0