# Matrix optimization on universal unitary photonic devices

arXiv: https://arxiv.org/abs/1808.00458 · HTML: https://mlsanity.com/paper/1808.00458/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Aug 2018.

## Built on by (most cited first, 5 of 5)

- [Dual adaptive training of photonic neural networks](https://mlsanity.com/paper/2212.06141/) (2022)
- [Exact gradients for linear optics with single photons](https://mlsanity.com/paper/2409.16369/) (2024)
- [Local learning for stable backpropagation-free neural network training towards physical learning](https://mlsanity.com/paper/2603.24790/) (2026)
- [Acceleration Method for Learning Fine-Layered Optical Neural Networks](https://mlsanity.com/paper/2109.01731/) (2021)
- [LuxIA: A Lightweight Unitary matriX-based Framework Built on an Iterative Algorithm for Photonic Neural Network Training](https://mlsanity.com/paper/2512.22264/) (2025)

## Abstract (arXiv)

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.