# Bayesian optimisation of large-scale photonic reservoir computers

arXiv: https://arxiv.org/abs/2004.02535 · HTML: https://mlsanity.com/paper/2004.02535/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Apr 2020.

## Built on by (most cited first, 7 of 7)

- [Next Generation Reservoir Computing](https://mlsanity.com/paper/2106.07688/) (2021)
- [Hyperparameter Optimization for Randomized Algorithms: A Case Study on Random Features](https://mlsanity.com/paper/2407.00584/) (2024)
- [High Speed Human Action Recognition using a Photonic Reservoir Computer](https://mlsanity.com/paper/2305.15283/) (2023)
- [Efficient Optimisation of Physical Reservoir Computers using only a Delayed Input](https://mlsanity.com/paper/2401.14371/) (2024)
- [Optical Computing for Deep Neural Network Acceleration: Foundations, Recent Developments, and Emerging Directions](https://mlsanity.com/paper/2407.21184/) (2024)
- [Boosting Reservoir Computing with Brain-inspired Adaptive Dynamics](https://mlsanity.com/paper/2504.12480/) (2025)
- [Bayesian Optimization of Multi-Bit Pulse Encoding in In2O3/Al2O3 Thin-film Transistors for Temporal Data Processing](https://mlsanity.com/paper/2510.07421/) (2025)

## Abstract (arXiv)

Introduction. Reservoir computing is a growing paradigm for simplified training of recurrent neural networks, with a high potential for hardware implementations. Numerous experiments in optics and electronics yield comparable performance to digital state-of-the-art algorithms. Many of the most recent works in the field focus on large-scale photonic systems, with tens of thousands of physical nodes and arbitrary interconnections. While this trend significantly expands the potential applications of photonic reservoir computing, it also complicates the optimisation of the high number of hyper-parameters of the system. Methods. In this work, we propose the use of Bayesian optimisation for efficient exploration of the hyper-parameter space in a minimum number of iteration. Results. We test this approach on a previously reported large-scale experimental system, compare it to the commonly used grid search, and report notable improvements in performance and the number of experimental iterations required to optimise the hyper-parameters. Conclusion. Bayesian optimisation thus has the potential to become the standard method for tuning the hyper-parameters in photonic reservoir computing.