Quantum-enhanced deliberation of learning agents using trapped ions

Abstract: A scheme that successfully employs quantum mechanics in the design of autonomous learning agents has recently been reported in the context of the projective simulation (PS) model for artificial intelligence. In that approach, the key feature of a PS agent, a specific type of memory which is explored via random walks, was shown to be amenable to quantization. In particular, classical random walks were substituted by Szegedy-type quantum walks, allowing for a speed-up. In this work we propose how such classical and quantum agents can be implemented in systems of trapped ions. We employ a generic construction by which the classical agents are `upgraded' to their quantum counterparts by nested coherent controlization, and we outline how this construction can be realized in ion traps. Our results provide a flexible modular architecture for the design of PS agents. Furthermore, we present numerical simulations of simple PS agents which analyze the robustness of our proposal under certain noise models.