Photo by Claudia on Unsplash
This week: Complex Deep Learning with Quantum Optics by Antonio Manzalini
Date of publication: 30/07/2019
Links: Article in HTML: https://www.mdpi.com/2624-960X/1/1/11/htm Article in PDF: https://www.mdpi.com/2624-960X/1/1/11/pdf
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.
To cite this paper: Manzalini, A. Complex Deep Learning with Quantum Optics. Quantum Reports 2019, 1, 107-118.
Date of publication: 30/07/2019
Links: Article in HTML: https://www.mdpi.com/2624-960X/1/1/11/htm Article in PDF: https://www.mdpi.com/2624-960X/1/1/11/pdf
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.
To cite this paper: Manzalini, A. Complex Deep Learning with Quantum Optics. Quantum Reports 2019, 1, 107-118.