Quantum Neural Networks and Topological Quantum Field Theories.

Marcianò, Antonino; Chen, Deen; Fabrocini, Filippo; Fields, Chris; Greco, Enrico; Gresnigt, Niels; Jinklub, Krid; Lulli, Matteo et al. · Neural Netw · 2022

basic_science · Level V

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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.

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