Leveraging machine learning features for linear optical interferometer control

TL;DR

This paper introduces a supervised learning algorithm to model and control reconfigurable optical interferometers, enabling effective tuning without relying on analytical solutions, thus facilitating exploration of new circuit architectures.

Contribution

The paper presents a novel machine learning-based method for modeling and controlling optical interferometers, independent of specific architectures, improving flexibility and tunability.

Findings

Successful modeling of interferometers using supervised learning

Efficient determination of phase shifts for desired unitaries

Enabling exploration of new interferometric circuit designs

Abstract

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.