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

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.