Quantum Neural Networks and Topological Quantum Field Theories. (September 2022)

• Record Type: Journal Article
• Authors: Marcianò, Antonino; Chen, Deen; Fabrocini, Filippo; Fields, Chris; Greco, Enrico; Gresnigt, Niels; Jinklub, Krid; Lulli, Matteo; Terzidis, Kostas; Zappala, Emanuele
• Is Part Of: Neural networks. Volume 153(2022)
• Page Start: 164
• Page End: 178
• Publication Date: 2022-09
• Subjects: Topological quantum field theory -- Topological quantum neural networks -- Graph neural networks -- Quantum amplitude classifiers -- Quantum perceptron; Neural computers -- Periodicals; Neural networks (Computer science) -- Periodicals; Neural networks (Neurobiology) -- Periodicals; Nervous System -- Periodicals; Ordinateurs neuronaux -- Périodiques; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux neuronaux (Neurobiologie) -- Périodiques; Neural computers; Neural networks (Computer science); Neural networks (Neurobiology); Periodicals; 006.32
• DOI: 10.1016/j.neunet.2022.05.028
• Languages: English
• ISSNs: 0893-6080

Abstract

Abstract: 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.