  Deep Neural Networks miss a principled model of their operation. A novel
framework for supervised learning based on Topological Quantum Field Theory
that looks particularly well suited for implementation on quantum processors
has been recently explored. We propose the use of this framework for
understanding the problem of generalization in Deep Neural Networks. More
specifically, in this approach Deep Neural Networks are viewed as the
semi-classical limit of Topological Quantum Neural Networks. A framework of
this kind explains easily the overfitting behavior of Deep Neural Networks
during the training step and the corresponding generalization capabilities.
