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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
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Proc. SPIE 11091, Quantum Nanophotonic Materials, Devices, and Systems 2019, 110910W (10 September 2019);
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)

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