  We develop the learning algorithm to build the architecture agnostic model of
the reconfigurable optical interferometer. Programming the unitary
transformation on the optical modes of the interferometer either follows the
analytical expression yielding the unitary matrix given the set of phaseshifts
or requires the optimization routine if the analytic decomposition does not
exist. Our algorithm adopts the supervised learning strategy which matches the
model of the interferometer to the training set populated by the samples
produced by the device under study. The simple optimization routine uses the
trained model to output the phaseshifts of the interferometer with the given
architecture corresponding to the desired unitary transformation. Our result
provides the recipe for efficient tuning of the interferometers even without
rigorous analytical description which opens opportunity to explore new
architectures of the interferometric circuits.
