High-Speed Quantum Photonics with Plasmonic Metamaterials Empowered by Machine Learning

Simeon Bogdanov; Zhaxylyk A. Kudyshev; Alexander V. Kildishev; Alexandra Boltasseva; Vladimir M. Shalaev

doi:10.1109/ipcon.2019.8908357

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High-Speed Quantum Photonics with Plasmonic Metamaterials Empowered by Machine Learning

Simeon Bogdanov Zhaxylyk A. Kudyshev Alexander V. Kildishev Alexandra Boltasseva Vladimir M. Shalaev

Year: 2019 Pages: 1-1

DOI: 10.1109/ipcon.2019.8908357

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Abstract

We outline future directions in the development of a platform for high-speed integrated quantum photonics, the use of plasmonics to outpace quantum decoherence and the application of machine-learning techniques for photonics designs and quantum optical measurements.

Keywords:

Photonics Metamaterial Plasmon Quantum decoherence Quantum Quantum optics Computer science Optoelectronics Physics Optics Quantum mechanics

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Topics

Neural Networks and Reservoir Computing

Physical Sciences → Computer Science → Artificial Intelligence

Photonic and Optical Devices

Physical Sciences → Engineering → Electrical and Electronic Engineering

Optical Network Technologies

Physical Sciences → Engineering → Electrical and Electronic Engineering

High-Speed Quantum Photonics with Plasmonic Metamaterials Empowered by Machine Learning

Abstract

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