mlsanityData to 14 Oct 2026

Unsupervised Recognition of Informative Features via Tensor Network Machine Learning and Quantum Entanglement Variations

arXiv 2207.06031 ↗First posted 13 Jul 2022quant-ph cond-mat.str-el cs.LGcited by 7

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Abstract (arXiv)

Given an image of a white shoe drawn on a blackboard, how are the white pixels deemed (say by human minds) to be informative for recognizing the shoe without any labeling information on the pixels? Here we investigate such a ``white shoe'' recognition problem from the perspective of tensor network (TN) machine learning and quantum entanglement. Utilizing a generative TN that captures the probability distribution of the features as quantum amplitudes, we propose an unsupervised recognition scheme of informative features with the variations of entanglement entropy (EE) caused by designed measurements. In this way, a given sample, where the values of its features are statistically meaningless, is mapped to the variations of EE that statistically characterize the gain of information. We show that the EE variations identify the features that are critical to recognize this specific sample, and the EE itself reveals the information distribution of the probabilities represented by the TN model. The signs of the variations further reveal the entanglement structures among the features. We test the validity of our scheme on a toy dataset of strip images, the MNIST dataset of hand-drawn digits, the fashion-MNIST dataset of the pictures of fashion articles, and the images of brain cells. Our scheme opens the avenue to the quantum-inspired and interpreted unsupervised learning, which can be applied to, e.g., image segmentation and object detection.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 9 of its 43 references that are in the map. First posted on arXiv 13 Jul 2022.

Builds on

9 of 43 references.

References · 9

  1. 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,349
  2. 2018Explaining Explanations: An Overview of Interpretability of Machine Learningcited by 508
  3. 2017Unsupervised Generative Modeling Using Matrix Product Statescited by 88
  4. 2019Tree Tensor Networks for Generative Modelingcited by 64
  5. 2017Machine Learning by Unitary Tensor Network of Hierarchical Tree Structurecited by 40
  6. 2020Supervised Learning with Projected Entangled Pair Statescited by 31
  7. 2020Anomaly Detection with Tensor Networkscited by 26
  8. 2019Generative Tensor Network Classification Model for Supervised Machine Learningcited by 22
  9. 2018Entanglement-guided architectures of machine learning by quantum tensor networkcited by 15

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Cited by year

Citing papers per year: 2023: 4; 2024: 1; 2025: 2.

7 papers.

  1. 2023Tensor networks for quantum machine learningcited by 17
  2. 2023Tensor networks for interpretable and efficient quantum-inspired machine learningcited by 17
  3. 2023Machine learning with tree tensor networks, CP rank constraints, and tensor dropoutcited by 15
  4. 2025Interpretable Machine Learning in Physics: A Reviewcited by 14
  5. 2025ATP: Adaptive Threshold Pruning for Efficient Data Encoding in Quantum Neural Networkscited by 5
  6. 2024Universal scaling laws in quantum-probabilistic machine learning by tensor network towards interpreting representation and generalization powerscited by 1
  7. 2023Intelligent diagnostic scheme for lung cancer screening with Raman spectra data by tensor network machine learningcited by 0