Share Email Print

Proceedings Paper

Machine learning for quantum and classical photonic devices (Conference Presentation)
Author(s): Claudio Conti

Paper Abstract

We apply concepts from machine learning to design topological one-dimensional systems. We also use tensorflow and related tools for designing quantum gates for multilevel qdits with random and unknown media. We report on experiments concerning the realization of a large-scale Ising machine and the use of an optical neural network for detecting cancer morphodynamics in in-vitro tumor models.

Paper Details

Date Published: 10 September 2019
Proc. SPIE 11091, Quantum Nanophotonic Materials, Devices, and Systems 2019, 110910W (10 September 2019); doi: 10.1117/12.2531731
Show Author Affiliations
Claudio Conti, Istituto dei Sistemi Complessi (Italy)

Published in SPIE Proceedings Vol. 11091:
Quantum Nanophotonic Materials, Devices, and Systems 2019
Cesare Soci; Matthew T. Sheldon; Mario Agio, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?