Publikation: Speeding-up the decision making of a learning agent using an ion trap quantum processor
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2019
Autor:innen
Sriarunothai, Theeraphot
Wölk, Sabine
Giri, Gouri Shankar
Friis, Nicolai
Dunjko, Vedran
Wunderlich, Christof
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Quantum Science and Technology. 2019, 4(1), 015014. eISSN 2058-9565. Available under: doi: 10.1088/2058-9565/aaef5e
Zusammenfassung
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.
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100 Philosophie
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machine learning, reinforcement learning, quantum computing, trapped ions, quadratic speed-up algorithm
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SRIARUNOTHAI, Theeraphot, Sabine WÖLK, Gouri Shankar GIRI, Nicolai FRIIS, Vedran DUNJKO, Hans J. BRIEGEL, Christof WUNDERLICH, 2019. Speeding-up the decision making of a learning agent using an ion trap quantum processor. In: Quantum Science and Technology. 2019, 4(1), 015014. eISSN 2058-9565. Available under: doi: 10.1088/2058-9565/aaef5eBibTex
@article{Sriarunothai2019-01-01Speed-44516,
year={2019},
doi={10.1088/2058-9565/aaef5e},
title={Speeding-up the decision making of a learning agent using an ion trap quantum processor},
number={1},
volume={4},
journal={Quantum Science and Technology},
author={Sriarunothai, Theeraphot and Wölk, Sabine and Giri, Gouri Shankar and Friis, Nicolai and Dunjko, Vedran and Briegel, Hans J. and Wunderlich, Christof},
note={Article Number: 015014}
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<dcterms:abstract xml:lang="eng">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.</dcterms:abstract>
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