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.
Highlights
The techno-economic drivers steering the evolution towards future telecommunications (e.g., 5G) and the internet are boosting a new growing interest for artificial intelligence (AI)
This paper provides a short review of these domains and explores the emerging research field where deep neural networks (DNNs) and nanophotonics are converging
DNN principles seem to be deeply rooted in quantum physics, showing correspondences, for instance, with quantum field theory (QFT) and gauge theory principles
Summary
The techno-economic drivers steering the evolution towards future telecommunications (e.g., 5G) and the internet are boosting a new growing interest for artificial intelligence (AI). In a DNN, each high-level layer learns increasingly abstract higher-level features, providing a useful, and at times reduced, representation of the features to a lower-level layer This similarity, and other analysis described in literature [4], suggests the intriguing possibility that the principles of DNNs are deeply rooted in quantum physics. Common to most of these contexts, there is a formulation of the problems in terms of the variational free energy principle In view of this reasoning, it seems promising to explore how this reasoning could be further extended for the development of future DNNs and more in general AI systems, based on quantum optics. Plans for future works and conclusions and are closing the paper
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