A supervised learning method linearizes the phase-dependence of a linear optical interferometer, so its unitary response can be modeled and programmed by least squares instead of non-convex optimization.
After performing this local minimization, we proceed to the next layer, conduct tomography, and train a local linear model (4)
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Leveraging machine learning features for linear optical interferometer control
A supervised learning method linearizes the phase-dependence of a linear optical interferometer, so its unitary response can be modeled and programmed by least squares instead of non-convex optimization.