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Projective Simulation was introduced as a novel approach to ArtificialIntelligence. It involves a deliberation procedure that consists of a randomwalk on a graph of clips and allows for the learning agent to project itselfinto the future before committing to an action. Here we study and analyze aquantum mechanical version in which the random walk is performed by two kindsof Hamiltonians. The first kind is implemented by naively embedding theclassical model in a quantum model by turning the clips into qubits. The otherallows for storing clips in superpositions of qubits allowing for a potentiallypurely quantum mechanical learning procedure in which the perception of theenvironment is purely quantum mechanical but the action is classical. We lastlyintroduce the concept of interacting projective agents for both the classicaland quantum mechanical case.

Quantum Projective Simulation with Hamiltonian Evolution: A study in reinforcement learning