  Photonic artificial intelligence has attracted considerable interest in
accelerating machine learning; however, the unique optical properties have not
been fully utilized for achieving higher-order functionalities. Chaotic
itinerancy, with its spontaneous transient dynamics among multiple
quasi-attractors, can be employed to realize brain-like functionalities. In
this paper, we propose a method for controlling the chaotic itinerancy in a
multi-mode semiconductor laser to solve a machine learning task, known as the
multi-armed bandit problem, which is fundamental to reinforcement learning. The
proposed method utilizes ultrafast chaotic itinerant motion in mode competition
dynamics controlled via optical injection. We found that the exploration
mechanism is completely different from a conventional searching algorithm and
is highly scalable, outperforming the conventional approaches for large-scale
bandit problems. This study paves the way to utilize chaotic itinerancy for
effectively solving complex machine learning tasks as photonic hardware
accelerators.
