Quantum Bayesian Decision‑Making
de Oliveira, Michael and Soares Barbosa, Luís, (2021). Quantum Bayesian Decision‑Making. Foundations of Science, 1-21
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Article
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Sub-type Journal article Author de Oliveira, Michael
Soares Barbosa, LuísTitle Quantum Bayesian Decision‑Making Appearing in Foundations of Science Publication Date 2021-03 Place of Publication Cham Publisher Springer Start page 1 End page 21 Language eng Abstract 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. Keyword Bayesian inference
Quantum algorithms
Quantum decision-makingCopyright Holder The Authors Copyright Year 2021 Copyright type Creative commons ISSN 1572-8471 DOI 10.1007/s10699-021-09781-6 -
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