Matrix optimization on universal unitary photonic devices

TL;DR

This paper investigates how to optimize universal unitary photonic devices for machine learning, highlighting the importance of proper initialization and architecture modifications to improve convergence and scalability.

Contribution

It introduces a novel initialization method and architectural enhancements that significantly improve the optimization process of photonic devices.

Findings

Proper initialization from the distribution of random unitaries enhances convergence.

Adding extra tunable beamsplitters improves training speed.

Architectural modifications increase scalability of the devices.

Abstract

Universal unitary photonic devices can apply arbitrary unitary transformations to a vector of input modes and provide a promising hardware platform for fast and energy-efficient machine learning using light. We simulate the gradient-based optimization of random unitary matrices on universal photonic devices composed of imperfect tunable interferometers. If device components are initialized uniform-randomly, the locally-interacting nature of the mesh components biases the optimization search space towards banded unitary matrices, limiting convergence to random unitary matrices. We detail a procedure for initializing the device by sampling from the distribution of random unitary matrices and show that this greatly improves convergence speed. We also explore mesh architecture improvements such as adding extra tunable beamsplitters or permuting waveguide layers to further improve the training speed and scalability of these devices.