Erlangen 2018 – scientific programme
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Q: Fachverband Quantenoptik und Photonik
Q 7: Quantum Information (Quantum Computing)
Q 7.1: Group Report
Monday, March 5, 2018, 10:30–11:00, K 1.020
Speeding-up the decision making of a learning agent using an ion trap quantum processor — •Theeraphot Sriarunothai1, Sabine Wölk2,1, Gouri Shankar Giri1, Nicolai Friis3,2, Vedran Dunjko2,4, Hans Briegel2,5, and Christof Wunderlich1 — 1Department Physik, Naturwissenschaftlich-Technische Fakultät, Universität Siegen, Walter-Flex-Str. 3, 57068 Siegen, Germany — 2Institute for Theoretical Physics, University of Innsbruck, Technikerstraße 21a, 6020 Innsbruck, Austria — 3Institute for Quantum Optics and Quantum Information, Austrian Academy of Sciences, Boltzmanngasse 3, 1090 Vienna, Austria — 4Max Planck Institute of Quantum Optics Garching 85748, Germany — 5Department of Philosophy, University of Konstanz, 78457 Konstanz, Germany
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 [1]. 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.
[1] Th. Sriarunothai et al., arXiv: 1709.01366 (2017)