The transferable belief model, as a semantic interpretation of Dempster-Shafer theory, enables agents to perform reasoning and decision making in imprecise and incomplete environments. The model offers distinct semantics for handling unreliable testimonies, allowing for a more reasonable and general process of belief transfer compared to the Bayesian approach. However, because both the belief masses and the structure of focal sets must be considered when updating belief functions-leading to extra computational complexity during reasoning-the transferable belief model has gradually lost favor among researchers in recent developments. In this paper, we implement the transferable belief model on quantum circuits and demonstrate that belief functions offer a more concise and effective alternative to Bayesian approaches within the quantum computing framework. Furthermore, leveraging the unique characteristics of quantum computing, we propose several novel belief transfer approaches. More broadly, this paper introduces a new perspective on basic information representation for quantum AI models, suggesting that belief functions are more suitable than Bayesian approach for handling uncertainty on quantum circuits.

翻译：可迁移信念模型作为Dempster-Shafer理论的语义解释，使智能体能够在不精确和不完整环境中进行推理与决策。该模型为处理不可靠证言提供了独特的语义框架，相比贝叶斯方法实现了更合理、更通用的信念迁移过程。然而，由于更新信念函数时需同时考虑信度质量和焦元结构——这导致推理过程中产生额外的计算复杂度——可迁移信念模型在近年发展中逐渐失去研究者的青睐。本文在量子电路上实现了可迁移信念模型，并证明在量子计算框架内信念函数为贝叶斯方法提供了更简洁有效的替代方案。此外，利用量子计算的独特特性，我们提出了若干新型信念迁移方法。更广泛而言，本文为量子AI模型的基础信息表征引入了新视角，表明信念函数比贝叶斯方法更适合处理量子电路上的不确定性问题。