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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IOP Publishing
2015-01-01
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| Series: | New Journal of Physics |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/1367-2630/17/2/023006 |
| _version_ | 1797751258102104064 |
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| author | V Dunjko N Friis H J Briegel |
| author_facet | V Dunjko N Friis H J Briegel |
| author_sort | V Dunjko |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-12T16:44:53Z |
| format | Article |
| id | doaj.art-c2bc40505a9a4b8fbf15710c6bf622c5 |
| institution | Directory Open Access Journal |
| issn | 1367-2630 |
| language | English |
| last_indexed | 2024-03-12T16:44:53Z |
| publishDate | 2015-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | New Journal of Physics |
| spelling | doaj.art-c2bc40505a9a4b8fbf15710c6bf622c52023-08-08T14:16:49ZengIOP PublishingNew Journal of Physics1367-26302015-01-0117202300610.1088/1367-2630/17/2/023006Quantum-enhanced deliberation of learning agents using trapped ionsV Dunjko0N Friis1H J Briegel2Institute for Quantum Optics and Quantum Information, Austrian Academy of Sciences , Technikerstraße 21a, A-6020 Innsbruck, Austria; Institute for Theoretical Physics, University of Innsbruck , Technikerstraße 25, A-6020 Innsbruck, Austria; Laboratory of Evolutionary Genetics, Division of Molecular Biology, Ruđer Bošković Institute , Bijenička cesta 54, HR-10000 Zagreb, CroatiaInstitute for Quantum Optics and Quantum Information, Austrian Academy of Sciences , Technikerstraße 21a, A-6020 Innsbruck, AustriaInstitute for Quantum Optics and Quantum Information, Austrian Academy of Sciences , Technikerstraße 21a, A-6020 Innsbruck, Austria; Institute for Theoretical Physics, University of Innsbruck , Technikerstraße 25, A-6020 Innsbruck, AustriaA 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.https://doi.org/10.1088/1367-2630/17/2/023006learning machinesion trappingrandom walks07.05.Mh03.67.Lx37.10.Ty |
| spellingShingle | V Dunjko N Friis H J Briegel Quantum-enhanced deliberation of learning agents using trapped ions New Journal of Physics learning machines ion trapping random walks 07.05.Mh 03.67.Lx 37.10.Ty |
| title | Quantum-enhanced deliberation of learning agents using trapped ions |
| title_full | Quantum-enhanced deliberation of learning agents using trapped ions |
| title_fullStr | Quantum-enhanced deliberation of learning agents using trapped ions |
| title_full_unstemmed | Quantum-enhanced deliberation of learning agents using trapped ions |
| title_short | Quantum-enhanced deliberation of learning agents using trapped ions |
| title_sort | quantum enhanced deliberation of learning agents using trapped ions |
| topic | learning machines ion trapping random walks 07.05.Mh 03.67.Lx 37.10.Ty |
| url | https://doi.org/10.1088/1367-2630/17/2/023006 |
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