  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.
