Parallel photonic accelerator for decision making using optical spatiotemporal chaos

Photonic accelerators have attracted increasing attention for use in artificial intelligence applications. The multi-armed bandit problem is a fundamental problem of decision making using reinforcement learning. However, to the best of our knowledge, the scalability of photonic decision making has not yet been demonstrated in experiments because of the technical difficulties in the physical realization. We propose a parallel photonic decision-making system to solve large-scale multi-armed bandit problems using optical spatiotemporal chaos. We solved a 512-armed bandit problem online, which is larger than those in previous experiments by two orders of magnitude. The scaling property for correct decision making is examined as a function of the number of slot machines, evaluated as an exponent of 0.86. This exponent is smaller than that in previous studies, indicating the superiority of the proposed parallel principle. This experimental demonstration facilitates photonic decision making to solve large-scale multi-armed bandit problems for future photonic accelerators. (c) 2023 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement

Automated summary

Photonic accelerators have gained attention for use in AI applications, but the scalability of photonic decision making has not been demonstrated in experiments. A parallel photonic decision-making system using optical spatiotemporal chaos is proposed to solve large-scale multi-armed bandit problems. The experimental demonstration shows the superiority of the proposed parallel principle for correct decision making, with an exponent of 0.86. This facilitates photonic decision making for future photonic accelerators.