Quantum-enhanced deliberation of learning agents using trapped ions

A scheme that successfully employs quantum mechanics in the design of autonomous learning agents has recently been reported in the context of the projective simulation (PS) model for artificial intelligence. In that approach, the key feature of a PS agent, a specific type of memory which is explored...

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Bibliographic Details
Published in:New journal of physics 2015-01, Vol.17 (2), p.23006-17
Main Authors: Dunjko, V, Friis, N, Briegel, H J
Format: Article
Language:English
Subjects:
03.67.Lx
07.05.Mh
37.10.Ty
Agents (artificial intelligence)
Artificial intelligence
Autonomous
Computer simulation
Construction
Design engineering
ion trapping
Ion traps (instrumentation)
Learning
learning machines
Mathematical models
Modular design
Physics
Proposals
Quantum mechanics
Random walk
random walks
Robustness (mathematics)
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Summary:A scheme that successfully employs quantum mechanics in the design of autonomous learning agents has recently been reported in the context of the projective simulation (PS) model for artificial intelligence. In that approach, the key feature of a PS agent, a specific type of memory which is explored via random walks, was shown to be amenable to quantization, allowing for a speed-up. In this work we propose an implementation of such classical and quantum agents in systems of trapped ions. We employ a generic construction by which the classical agents are 'upgraded' to their quantum counterparts by a nested process of adding coherent control, and we outline how this construction can be realized in ion traps. Our results provide a flexible modular architecture for the design of PS agents. Furthermore, we present numerical simulations of simple PS agents which analyze the robustness of our proposal under certain noise models.
ISSN:1367-2630
1367-2630
DOI:10.1088/1367-2630/17/2/023006