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.
Photonic reservoir computing with dimensionally compressed readout
Université de Lorraine, CentraleSupélec（洛林大学、中央苏佩莱克）；Instituto de Física Interdisciplinar y Sistemas Complejos (IFISC), CSIC–UIB（跨学科物理与复杂系统研究所（IFISC），西班牙国家研究委员会-巴利阿里群岛大学）
Institute for Intelligent Systems, University of Johannesburg（约翰内斯堡大学智能系统研究所）；Faculty of Engineering and the Built Environment, University of Johannesburg（约翰内斯堡大学工程与建筑环境学院）；Department of Statistical Sciences, University of Cape Town（开普敦大学统计科学系）
