Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/78052
Título: | Quantum bayesian decision‑making |
Autor(es): | Oliveira, Michael Barbosa, L. S. |
Palavras-chave: | Bayesian inference Quantum algorithms Quantum decision making |
Data: | 2023 |
Editora: | Springer |
Revista: | Foundations of Science |
Citação: | de Oliveira, M., Barbosa, L.S. Quantum Bayesian Decision-Making. Found Sci 28, 21–41 (2023). https://doi.org/10.1007/s10699-021-09781-6 |
Resumo(s): | As a compact representation of joint probability distributions over a dependence graph of random variables, and a tool for modelling and reasoning in the presence of uncertainty, Bayesian networks are of great importance for artificial intelligence to combine domain knowledge, capture causal relationships, or learn from incomplete datasets. Known as a NP-hard problem in a classical setting, Bayesian inference pops up as a class of algorithms worth to explore in a quantum framework. This paper explores such a research direction and improves on previous proposals by a judicious use of the utility function in an entangled configuration. It proposes a completely quantum mechanical decision-making process with a proven computational advantage. A prototype implementation in Qiskit (a Python based program development kit for the IBM Q machine) is discussed as a proof-of-concept. |
Tipo: | Artigo |
Descrição: | First Published: 20 March 2021 |
URI: | https://hdl.handle.net/1822/78052 |
DOI: | 10.1007/s10699-021-09781-6 |
ISSN: | 1233-1821 |
e-ISSN: | 1572-8471 |
Versão da editora: | https://link.springer.com/article/10.1007/s10699-021-09781-6 |
Arbitragem científica: | yes |
Acesso: | Acesso aberto |
Aparece nas coleções: | HASLab - Artigos em revistas internacionais |