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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  • Sub-type Journal article
    Author de Oliveira, Michael
    Soares Barbosa, Luís
    Title 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-making
    Copyright Holder The Authors
    Copyright Year 2021
    Copyright type Creative commons
    ISSN 1572-8471
    DOI 10.1007/s10699-021-09781-6
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    Created: Tue, 10 May 2022, 01:36:59 JST by Mario Peixoto on behalf of UNU EGOV