Parallel photonic accelerator for decision making using optical spatiotemporal chaos

TL;DR

This paper demonstrates a parallel photonic decision-making system using optical spatiotemporal chaos, successfully solving a 512-armed bandit problem and showing improved scalability over previous methods.

Contribution

The authors introduce a novel parallel photonic accelerator leveraging optical chaos, enabling large-scale decision making in multi-armed bandit problems with superior scalability.

Findings

Solved a 512-armed bandit problem online

Scaling exponent of 0.86 indicates improved scalability

Demonstrated feasibility of photonic decision making for large problems

Abstract

Photonic accelerators have attracted increasing attention in artificial intelligence applications. The multi-armed bandit problem is a fundamental problem of decision making using reinforcement learning. However, the scalability of photonic decision making has not yet been demonstrated in experiments, owing to technical difficulties in physical realization. We propose a parallel photonic decision-making system for solving large-scale multi-armed bandit problems using optical spatiotemporal chaos. We solve a 512-armed bandit problem online, which is much larger than 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 work, 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.