Speeding-up the decision making of a learning agent using an ion trap quantum processor
Sriarunothai, T., Wölk, S., Giri, G., Friis, N., Dunjko, V., Briegel, H., Wunderlich, C. · MPG.PuRe (Max Planck Society) · 2019
We report 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. 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 quantum processors taking advantage of machine learning.