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of 593
pro vyhledávání: '"Barbosa, Luís"'
This research explores the trainability of Parameterized Quantum circuit-based policies in Reinforcement Learning, an area that has recently seen a surge in empirical exploration. While some studies suggest improved sample complexity using quantum gr
Externí odkaz:
http://arxiv.org/abs/2406.09614
Discerning between quantum and classical correlations is of great importance. Bell polytopes are well established as a fundamental tool. In this paper, we extend this line of inquiry by applying resource theory within the context of Network scenarios
Externí odkaz:
http://arxiv.org/abs/2406.08073
Publikováno v:
Quantum 8, 1312 (2024)
Several classes of quantum circuits have been shown to provide a quantum computational advantage under certain assumptions. The study of ever more restricted classes of quantum circuits capable of quantum advantage is motivated by possible simplifica
Externí odkaz:
http://arxiv.org/abs/2212.03668
Autor:
Melo, Miguel, Gonçalves, Guilherme, Jorge, Filipa, Losada, Nieves, Barbosa, Luís, Teixeira, Mário Sérgio, Bessa, Maximino
Publikováno v:
Journal of Hospitality and Tourism Technology, 2023, Vol. 15, Issue 1, pp. 18-36.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/JHTT-01-2023-0015
Publikováno v:
EPTCS 358, 2022, pp. 270-284
Modelling complex information systems often entails the need for dealing with scenarios of inconsistency in which several requirements either reinforce or contradict each other. In this kind of scenarios, arising e.g. in knowledge representation, sim
Externí odkaz:
http://arxiv.org/abs/2204.06737
Publikováno v:
Quantum 8, 1242 (2024)
Classical non-perturbative simulations of open quantum systems' dynamics face several scalability problems, namely, exponential scaling of the computational effort as a function of either the time length of the simulation or the size of the open syst
Externí odkaz:
http://arxiv.org/abs/2203.14653
Variational Quantum Circuits are being used as versatile Quantum Machine Learning models. Some empirical results exhibit an advantage in supervised and generative learning tasks. However, when applied to Reinforcement Learning, less is known. In this
Externí odkaz:
http://arxiv.org/abs/2203.10591
Autor:
Bozola, Patrícia Maria, Nunhes, Thais V., Barbosa, Luís César Ferreira Motta, Machado, Marcio C., Oliveira, Otavio José
Publikováno v:
Benchmarking: An International Journal, 2022, Vol. 30, Issue 9, pp. 3699-3724.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/BIJ-04-2022-0215