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Deep Neural Networks miss a principled model of their operation. A novelframework for supervised learning based on Topological Quantum Field Theorythat looks particularly well suited for implementation on quantum processorshas been recently explored. We propose the use of this framework forunderstanding the problem of generalization in Deep Neural Networks. Morespecifically, in this approach Deep Neural Networks are viewed as thesemi-classical limit of Topological Quantum Neural Networks. A framework ofthis kind explains easily the overfitting behavior of Deep Neural Networksduring the training step and the corresponding generalization capabilities.

Deep Neural Networks as the Semi-classical Limit of Topological Quantum Neural Networks: The problem of generalisation