Speeding-up the decision making of a learning agent using an ion trap quantum processor

We report a proof-of-principle experimental demonstration of a quantum speed-up for learning agents utilizing a small-scale quantum information processor based on radiofrequency-driven trapped ions. The decision-making process of a quantum learning agent within the projective simulation paradigm for machine learning is modeled in a system of two qubits. The latter are realized in the hyperfine levels of two frequency-addressed ions exposed to a static magnetic field gradient. The deliberation algorithm is implemented using single-qubit rotations and two-qubit conditional quantum dynamics. We show that the deliberation time of this quantum learning agent is quadratically improved with respect to comparable classical learning agents. The performance of this quantum-enhanced learning agent highlights the potential of scalable ion trap quantum processors taking advantage of machine learning.