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- W3100358183 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." @default.
- W3100358183 created "2020-11-23" @default.
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- W3100358183 date "2018-12-19" @default.
- W3100358183 modified "2023-10-13" @default.
- W3100358183 title "Speeding-up the decision making of a learning agent using an ion trap quantum processor" @default.
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- W3100358183 doi "https://doi.org/10.1088/2058-9565/aaef5e" @default.
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