Leveraging machine learning features for linear optical interferometer control

2025-05-29

AI Summary

Reconfigurable optical interferometers face significant challenges in implementing arbitrary unitary transformations when analytical phase decomposition methods—such as the Clements decomposition—are unavailable, particularly for nonstandard or novel circuit architectures. Method: This paper proposes a data-driven automated calibration and programming framework: first, a device-specific end-to-end response model is constructed via supervised learning, eliminating reliance on architecture-dependent analytical models; second, phase control parameters are jointly optimized to directly approximate the target unitary matrix. Contribution/Results: This work pioneers the tight integration of data-driven modeling with physical-layer control, circumventing conventional analytical decomposition algorithms. It substantially enhances programmability for unconventional interferometric architectures. Experimental results demonstrate high fidelity (>99.5%) and strong robustness even in absence of analytical solutions, establishing a general-purpose calibration paradigm for large-scale programmable photonic integrated circuits.

Technology Category

Machine Learning: Calibration & Uncertainty QuantificationComputer Vision: Learning & Optimization for CVSearch and Optimization: Learning to Search

Application Category

Responsible Web: Machine-in-the-loop, human agency and autonomySearch and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for rankingUser Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

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.

Problem

• Develop algorithm for reconfigurable optical interferometer modeling
• Program unitary transformations without architectural constraints
• Enable interferometer tuning without precise analytical solutions

Innovation

• Machine learning models optical interferometer control
• Analytical and optimization methods for unitary transformations
• Supervised learning aligns model with device data