Machine learning for quantum and classical photonic devices (Conference Presentation)

Claudio Conti

doi:10.1117/12.2531731

10 September 2019 Machine learning for quantum and classical photonic devices (Conference Presentation)

Claudio Conti

Proceedings Volume 11091, Quantum Nanophotonic Materials, Devices, and Systems 2019; 110910W (2019) https://doi.org/10.1117/12.2531731
Event: SPIE Nanoscience + Engineering, 2019, San Diego, California, United States

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.

Conference Presentation

Citation Download Citation

Claudio Conti "Machine learning for quantum and classical photonic devices (Conference Presentation)", Proc. SPIE 11091, Quantum Nanophotonic Materials, Devices, and Systems 2019, 110910W (10 September 2019); https://doi.org/10.1117/12.2531731

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