# Optimal Control of SOAs with Artificial Intelligence for Sub-Nanosecond Optical Switching

arXiv: https://arxiv.org/abs/2006.12165 · HTML: https://mlsanity.com/paper/2006.12165/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jun 2020.

## Built on by (most cited first, 3 of 3)

- [RAMP: A Flat Nanosecond Optical Network and MPI Operations for Distributed Deep Learning Systems](https://mlsanity.com/paper/2211.15226/) (2022)
- [One-shot, Offline and Production-Scalable PID Optimisation with Deep Reinforcement Learning](https://mlsanity.com/paper/2210.13906/) (2022)
- [Partitioning Distributed Compute Jobs with Reinforcement Learning and Graph Neural Networks](https://mlsanity.com/paper/2301.13799/) (2023)

## Abstract (arXiv)

Novel approaches to switching ultra-fast semiconductor optical amplifiers using artificial intelligence algorithms (particle swarm optimisation, ant colony optimisation, and a genetic algorithm) are developed and applied both in simulation and experiment. Effective off-on switching (settling) times of 542 ps are demonstrated with just 4.8% overshoot, achieving an order of magnitude improvement over previous attempts described in the literature and standard dampening techniques from control theory.