Deep Neural Networks as the Semi-classical Limit of Quantum Neural Networks

Antonino Marciano; Deen Chen; Filippo Fabrocini*; Chris Fields; Enrico Greco*; Niels Gresnigt; Krid Jinklub; Matteo Lulli; Kostas Terzidis; Emanuele Zappala

arXiv · 2007.00142

Deep Neural Networks as the Semi-classical Limit of Quantum Neural Networks

Antonino Marciano·Deen Chen·Filippo Fabrocini*·Chris Fields·Enrico Greco*·Niels Gresnigt·Krid Jinklub·Matteo Lulli·Kostas Terzidis·Emanuele Zappala

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Abstract

Our work intends to show that: (1) Quantum Neural Networks (QNN) can be mapped onto spinnetworks, with the consequence that the level of analysis of their operation can be carried out on the side of Topological Quantum Field Theories (TQFT); (2) Deep Neural Networks (DNN) are a subcase of QNN, in the sense that they emerge as the semiclassical limit of QNN; (3) 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 DNN, relating it to the topological features of the graphs structures involved.

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cond-mat.dis-nn gr-qc hep-th quant-ph

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BibTeX RIS

Antonino Marciano, Deen Chen, Filippo Fabrocini*, Chris Fields, Enrico Greco*, Niels Gresnigt, Krid Jinklub, Matteo Lulli, Kostas Terzidis, Emanuele Zappala. 2020-06-30. Deep Neural Networks as the Semi-classical Limit of Quantum Neural Networks. https://arxiv.org/abs/2007.00142

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