mlsanityData to 7 Oct 2026

Entangled q-Convolutional Neural Nets

arXiv 2103.11785 ↗First posted 6 Mar 2021cs.LG quant-ph stat.MLcited by 4

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Abstract (arXiv)

We introduce a machine learning model, the q-CNN model, sharing key features with convolutional neural networks and admitting a tensor network description. As examples, we apply q-CNN to the MNIST and Fashion MNIST classification tasks. We explain how the network associates a quantum state to each classification label, and study the entanglement structure of these network states. In both our experiments on the MNIST and Fashion-MNIST datasets, we observe a distinct increase in both the left/right as well as the up/down bipartition entanglement entropy during training as the network learns the fine features of the data. More generally, we observe a universal negative correlation between the value of the entanglement entropy and the value of the cost function, suggesting that the network needs to learn the entanglement structure in order the perform the task accurately. This supports the possibility of exploiting the entanglement structure as a guide to design the machine learning algorithm suitable for given tasks.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 19 of its 39 references that are in the map. First posted on arXiv 6 Mar 2021.

Builds on

19 of 39 references.

References · 19

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7 more
  1. 2019TensorNetwork for Machine Learningcited by 29
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Cited by year

Citing papers per year: 2021: 1; 2022: 0; 2023: 2; 2024: 0; 2025: 1.

4 papers.

  1. 2023Machine Learning Regularization for the Minimum Volume Formula of Toric Calabi-Yau 3-foldscited by 5
  2. 2025ATP: Adaptive Threshold Pruning for Efficient Data Encoding in Quantum Neural Networkscited by 5
  3. 2021Tensor network to learn the wavefunction of datacited by 4
  4. 2023What Makes Data Suitable for a Locally Connected Neural Network? A Necessary and Sufficient Condition Based on Quantum Entanglementcited by 4