Optics for AI and AI for Optics

Edited by
June 2020
162 pages
  • ISBN978-3-03936-398-8 (Paperback)
  • ISBN978-3-03936-399-5 (PDF)

This book is a reprint of the Special Issue Optics for AI and AI for Optics that was published in

Biology & Life Sciences
Chemistry & Materials Science
Computer Science & Mathematics
Environmental & Earth Sciences
Physical Sciences
Artificial intelligence is deeply involved in our daily lives via reinforcing the digital transformation of modern economies and infrastructure. It relies on powerful computing clusters, which face bottlenecks of power consumption for both data transmission and intensive computing. Meanwhile, optics (especially optical communications, which underpin today’s telecommunications) is penetrating short-reach connections down to the chip level, thus meeting with AI technology and creating numerous opportunities. This book is about the marriage of optics and AI and how each part can benefit from the other. Optics facilitates on-chip neural networks based on fast optical computing and energy-efficient interconnects and communications. On the other hand, AI enables efficient tools to address the challenges of today’s optical communication networks, which behave in an increasingly complex manner. The book collects contributions from pioneering researchers from both academy and industry to discuss the challenges and solutions in each of the respective fields.
  • Paperback
© 2020 by the authors; CC BY-NC-ND license
light emitting diode; nonlinearity estimation and compensation; probabilistic Bayesian learning; visible light communication; digital signal processing; support vector machines; BCSVM; nonlinear equalization; coherent detection; k-nearest neighbor algorithm; modulation format identification; OSNR monitoring; neural networks; optical communications; optimization; equalizer; tap estimation; optical Fast-OFDM; neural networks; nonlinearity compensation; optical fiber communications; chromatic dispersion; short-reach communication; neural network; hybrid signal processing; fiber optics communications; coherent communications; machine learning; clustering; nonlinearity cancellation; entanglement; charge qubit; position-based semiconductor qubits; cryogenic technologies; semiconductor photon communication; Jaynes–Cummings–Hubbard formalism; deep neural networks; volterra equalization; nonlinear systems; coherent optical communication; passive optical networks; nonlinear compensation; clustering; optical transmission; optical networks; machine learning; artificial intelligence; quality of transmission; optical performance monitoring; failure management; artificial neural networks; deep neural network; image classification; photonic integrated circuits; semiconductor optical amplifiers; photonic neural network; n/a