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.

贝叶斯网络作为随机变量依存关系图上联合概率分布的紧凑表示形式，以及在存在不确定性的情况下进行建模和推理的工具，对于人工智能而言，结合领域知识，捕获因果关系或从中学习非常重要。不完整的数据集。贝叶斯推理在经典环境中被称为NP难题，它是一类值得在量子框架中探索的算法。本文探索了这样的研究方向，并通过在纠缠的配置中明智地使用效用函数来改进先前的建议。它提出了一种具有公认的计算优势的完全量子力学决策过程。