mlsanityData to 7 Oct 2026

TensorKrowch: Smooth integration of tensor networks in machine learning

arXiv 2306.08595 ↗First posted 14 Jun 2023cs.LG cond-mat.stat-mech cond-mat.str-elcited by 5

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Abstract (arXiv)

Tensor networks are factorizations of high-dimensional tensors into networks of smaller tensors. They have applications in physics and mathematics, and recently have been proposed as promising machine learning architectures. To ease the integration of tensor networks in machine learning pipelines, we introduce TensorKrowch, an open source Python library built on top of PyTorch. Providing a user-friendly interface, TensorKrowch allows users to construct any tensor network, train it, and integrate it as a layer in more intricate deep learning models. In this paper, we describe the main functionality and basic usage of TensorKrowch, and provide technical details on its building blocks and the optimizations performed to achieve efficient operation.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 9 of its 42 references that are in the map. First posted on arXiv 14 Jun 2023.

Builds on

9 of 42 references.

References · 9

  1. 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,283
  2. 2014Speeding-up Convolutional Neural Networks Using Fine-tuned CP-Decompositioncited by 199
  3. 2017Machine Learning by Unitary Tensor Network of Hierarchical Tree Structurecited by 39
  4. 2020Anomaly Detection with Tensor Networkscited by 26
  5. 2020Entanglement and Tensor Networks for Supervised Image Classificationcited by 18
  6. 2022Generative modeling with projected entangled-pair statescited by 14
  7. 2021On-FPGA Training with Ultra Memory Reduction: A Low-Precision Tensor Methodcited by 9
  8. 2021Explainable Natural Language Processing with Matrix Product Statescited by 7
  9. 2022The minimal canonical form of a tensor networkcited by 3

Built on by

Cited by year (2026 to 7 Oct)

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

5 papers.

  1. 2025tn4ml: Tensor Network Training and Customization for Machine Learningcited by 4
  2. 2025Tensorization of neural networks for improved privacy and interpretabilitycited by 3
  3. 2024Quantum-inspired Techniques in Tensor Networks for Industrial Contextscited by 2
  4. 2026Private and interpretable clinical prediction with quantum-inspired tensor train modelscited by 0
  5. 2026Quantum-inspired tensor networks in machine learning modelscited by 0