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arXiv · 1709.01366

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

Theeraphot Sriarunothai·Sabine Wölk·Gouri Shankar Giri·Nicolai Friis·Vedran Dunjko·Hans J. Briegel·Christof Wunderlich
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Abstract

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.

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Theeraphot Sriarunothai, Sabine Wölk, Gouri Shankar Giri, Nicolai Friis, Vedran Dunjko, Hans J. Briegel, Christof Wunderlich. 2018-12-19. Speeding-up the decision making of a learning agent using an ion trap quantum processor. https://doi.org/10.1088/2058-9565%2Faaef5e

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