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. (c) 2022 Elsevier Ltd. All rights reserved.

Quantum Neural Networks and Topological Quantum Field Theories

Greco, Enrico
;
2022-01-01

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. (c) 2022 Elsevier Ltd. All rights reserved.
2022
set-2022
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https://www.sciencedirect.com/science/article/abs/pii/S0893608022002027
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11368/3043038
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