Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/78050
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Campo DC | Valor | Idioma |
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dc.contributor.author | Sequeira, Andre | por |
dc.contributor.author | Santos, Luís Paulo | por |
dc.contributor.author | Barbosa, L. S. | por |
dc.date.accessioned | 2022-05-30T19:53:52Z | - |
dc.date.available | 2022-05-30T19:53:52Z | - |
dc.date.issued | 2021 | - |
dc.identifier.citation | A. Sequeira, L. P. Santos and L. S. Barbosa, "Quantum Tree-Based Planning," in IEEE Access, vol. 9, pp. 125416-125427, 2021, doi: 10.1109/ACCESS.2021.3110652. | por |
dc.identifier.issn | 2169-3536 | por |
dc.identifier.uri | https://hdl.handle.net/1822/78050 | - |
dc.description.abstract | Reinforcement Learning is at the core of a recent revolution in Arti cial Intelligence. Simultaneously, we are witnessing the emergence of a new eld: Quantum Machine Learning. In the context of these two major developments, this work addresses the interplay between Quantum Computing and Reinforcement Learning. Learning by interaction is possible in the quantum setting using the concept of oraculization of environments. The paper extends previous oracular instances to address more general stochastic environments. In this setting, we developed a novel quantum algorithm for near-optimal decision-making based on the Reinforcement Learning paradigm known as Sparse Sampling. The proposed algorithm exhibits a quadratic speedup compared to its classical counterpart. To the best of the authors' knowledge, this is the first quantum planning algorithm exhibiting a time complexity independent of the number of states of the environment, which makes it suitable for large state space environments, where planning is otherwise intractable. | por |
dc.description.sponsorship | This work is financed by National Funds through the Portuguese funding agency, FCT - Fundação para a Ciência e a Tecnologia, within project UIDB/50014/2020. | por |
dc.language.iso | eng | por |
dc.publisher | IEEE | por |
dc.relation | info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F50014%2F2020/PT | por |
dc.rights | openAccess | por |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | por |
dc.subject | Quantum computation | por |
dc.subject | quantum reinforcement learning | por |
dc.subject | sparse sampling | por |
dc.subject | Planning | por |
dc.subject | Heuristic algorithms | por |
dc.subject | Quantum computing | por |
dc.subject | Reinforcement learning | por |
dc.subject | Qubit | por |
dc.subject | Encoding | por |
dc.subject | Quantum algorithm | por |
dc.title | Quantum tree-based planning | por |
dc.type | article | por |
dc.peerreviewed | yes | por |
dc.relation.publisherversion | https://ieeexplore.ieee.org/document/9530390 | por |
oaire.citationStartPage | 125416 | por |
oaire.citationEndPage | 125427 | por |
oaire.citationVolume | 9 | por |
dc.identifier.doi | 10.1109/ACCESS.2021.3110652 | por |
dc.subject.fos | Ciências Naturais::Ciências da Computação e da Informação | por |
dc.subject.wos | Science & Technology | por |
sdum.journal | IEEE Access | por |
oaire.version | VoR | por |
Aparece nas coleções: | HASLab - Artigos em revistas internacionais |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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SSB21b.pdf | 938,47 kB | Adobe PDF | Ver/Abrir |
Este trabalho está licenciado sob uma Licença Creative Commons