Single Photon in Hierarchical Architecture for Physical Decision Making: Photon Intelligence

Understanding and using photonic processes for intelligent functionalities, referred to as photonic intelligence, has recently attracted interest from a variety of fields, including postsilicon computing for artificial intelligence and decision making in the behavioral sciences. In a past study, we successfully used the wave–particle duality of single photons to solve the two-armed bandit problem, which constitutes one of the important foundations of decision making and reinforcement learning. In this paper, we propose and confirm a hierarchical architecture for single-photon-based decision making that verifies the scalability of the principle. Specifically, the four-armed bandit problem is solved given zero prior knowledge in a two-layer hierarchical architecture, where the polarization of single photons is autonomously adapted in order to effect adequate decision making. In the hierarchical structure, the notion of layer-dependent decisions emerges. The optimal solutions in the coarse layer and the fine layer, however, conflict with each other in some contradictory problems. We show that while what we call a tournament strategy resolves such contradictions, the probabilistic nature of single photons allows for the direct location of the optimal solution, even for contradictory problems, hence manifesting the exploration capability of single photons. This study provides insights into photon intelligence in hierarchical architectures for artificial intelligence as well as a novel aspect of photonic processes for intelligent functionalities.