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Complex Deep Learning with Quantum Optics

TIM, Via Reiss Romoli, 274, 10148 Turin, Italy
Quantum Reports 2019, 1(1), 107-118; https://doi.org/10.3390/quantum1010011
Received: 26 June 2019 / Revised: 8 July 2019 / Accepted: 15 July 2019 / Published: 30 July 2019
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Abstract

The rapid evolution towards future telecommunications infrastructures (e.g., 5G, the fifth generation of mobile networks) and the internet is renewing a strong interest for artificial intelligence (AI) methods, systems, and networks. Processing big data to infer patterns at high speeds and with low power consumption is becoming an increasing central technological challenge. Electronics are facing physically fundamental bottlenecks, whilst nanophotonics technologies are considered promising candidates to overcome the limitations of electronics. Today, there are evidences of an emerging research field, rooted in quantum optics, where the technological trajectories of deep neural networks (DNNs) and nanophotonics are crossing each other. This paper elaborates on these topics and proposes a theoretical architecture for a Complex DNN made from programmable metasurfaces; an example is also provided showing a striking correspondence between the equivariance of convolutional neural networks (CNNs) and the invariance principle of gauge transformations. View Full-Text
Keywords: quantum field theory; gauge theory; artificial intelligence; deepneural networks; convolutional neural networks; nanophotonics; transformation optics; metamaterials; metasurfaces quantum field theory; gauge theory; artificial intelligence; deepneural networks; convolutional neural networks; nanophotonics; transformation optics; metamaterials; metasurfaces
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Manzalini, A. Complex Deep Learning with Quantum Optics. Quantum Reports 2019, 1, 107-118.

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