# Leveraging machine learning features for linear optical interferometer control

arXiv: https://arxiv.org/abs/2505.24032 · HTML: https://mlsanity.com/paper/2505.24032/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
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- First posted on arXiv 29 May 2025.

## Abstract (arXiv)

We have developed an algorithm that constructs a model of a reconfigurable optical interferometer, independent of specific architectural constraints. The programming of unitary transformations on the interferometer's optical modes relies on either an analytical method for deriving the unitary matrix from a set of phase shifts or an optimization routine when such decomposition is not available. Our algorithm employs a supervised learning approach, aligning the interferometer model with a training set derived from the device being studied. A straightforward optimization procedure leverages this trained model to determine the phase shifts of the interferometer with a specific architecture, obtaining the required unitary transformation. This approach enables the effective tuning of interferometers without requiring a precise analytical solution, paving the way for the exploration of new interferometric circuit architectures.