Leveraging machine learning features for linear optical interferometer control
This work addresses the challenge of controlling optical interferometers for researchers in photonics and quantum computing, though it appears incremental as it builds on existing optimization and learning methods.
The researchers tackled the problem of programming reconfigurable optical interferometers by developing a supervised learning algorithm that constructs a model from device data and optimizes phase shifts to achieve desired unitary transformations, enabling effective tuning without precise analytical solutions.
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.