Quantum Neural Networks and Topological Quantum Field Theories

Antonino Marcianò, Deen Chen, Filippo Fabrocini*, Chris Fields, Enrico Greco, Niels Gresnigt, Krid Jinklub, Matteo Lulli, Kostas Terzidis, Emanuele Zappala

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

12 Citations (Scopus)


Our work intends to show that: (1) Quantum Neural Networks (QNNs) can be mapped onto spin-networks, with the consequence that the level of analysis of their operation can be carried out on the side of Topological Quantum Field Theory (TQFT); (2) A number of Machine Learning (ML) key-concepts can be rephrased by using the terminology of TQFT. Our framework provides as well a working hypothesis for understanding the generalization behavior of DNNs, relating it to the topological features of the graph structures involved.

Original languageEnglish
Pages (from-to)164-178
Number of pages15
JournalNeural Networks
Publication statusPublished - Sept 2022


  • Graph neural networks
  • Quantum amplitude classifiers
  • Quantum perceptron
  • Topological quantum field theory
  • Topological quantum neural networks


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