Photon-Atom Hybrid Decision-Framework with Concurrent Exploration Acceleration

Decision-making enables artificial intelligence to dynamically adjust and acquire knowledge from experiences, distinguishing it from traditional computing intelligence based on predetermined and rigid logic rules. However, the hardness of decision-making within the Turing framework increases exponentially with the number of decisions and decision-agents, thereby limiting the speed and scaling for artificial intelligence to process intensity-heavy tasks. Here, by introducing the quantum advantages of both photons and atoms, we report a photon-atom hybrid decision framework, whose decision-making exploration is accomplished through time-correlated atomic excitation within a quantum memory material. We develop a pseudophotonic blockade effect within memory materials to ensure that decision-making conflicts are hardly generated. With exploring a two-agent N-armed bandit in a concurrent manner, an N² times acceleration of decision-making exploration compared to nonconcurrent methods is demonstrated. Furthermore, the experimentally characterized performance in preference satisfaction and conflict avoidance shows an advanced capacity for distributed decision-making on a significant scale. Our research advances scalable distributed frameworks to address future reinforcement learning challenges.