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dc.creatorPires, Otto Menegasso-
dc.creatorDuzzioni, Eduardo Inacio-
dc.creatorMarchi, Jerusa-
dc.creatorSantiago, Rafael de-
dc.date.accessioned2022-02-15T18:03:32Z-
dc.date.available2022-02-15T18:03:32Z-
dc.date.issued2021-
dc.identifier.citationPIRES, O. M. et al. Quantum circuit synthesis using projective simulation. Inteligência Artificial, [S.l.], v. 24, n. 67, p. 90-101, 2021.pt_BR
dc.identifier.urihttps://journal.iberamia.org/index.php/intartif/article/view/586pt_BR
dc.identifier.urihttp://repositorio.ufla.br/jspui/handle/1/49327-
dc.description.abstractQuantum Computing has been evolving in the last years. Although nowadays quantum algorithms performance has shown superior to their classical counterparts, quantum decoherence and additional auxiliary qubits needed for error tolerance routines have been huge barriers for quantum algorithms efficient use. These restrictions lead us to search for ways to minimize algorithms costs, i.e the number of quantum logical gates and the depth of the circuit. For this, quantum circuit synthesis and quantum circuit optimization techniques are explored. We studied the viability of using Projective Simulation, a reinforcement learning technique, to tackle the problem of quantum circuit synthesis. The agent had the task of creating quantum circuits up to 5 qubits. Our simulations demonstrated that the agent had a good performance but its capacity for learning new circuits decreased as the number of qubits increased.pt_BR
dc.languageen_USpt_BR
dc.rightsacesso abertopt_BR
dc.sourceInteligência Artificialpt_BR
dc.subjectMachine learningpt_BR
dc.subjectReinforcement learningpt_BR
dc.subjectProjective simulationpt_BR
dc.subjectQuantum circuit synthesispt_BR
dc.titleQuantum circuit synthesis using projective simulationpt_BR
dc.typeArtigopt_BR
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