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

Abstract

Wereport a proof-of-principle experimental demonstration of the 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 implemented in a system of two qubits. The latter are realized using hyperfine states of two frequency-addressed atomic ions exposed to a static magnetic field gradient.Weshow that the deliberation time of this quantum learning agent is quadratically improved with respect to comparable classical learning agents. The performance of this quantumenhanced learning agent highlights the potential of scalable quantum processors taking advantage of machine learning.