用量子电路实现模糊信念融合，提升信息处理的鲁棒性。
Quantum Information Fusion and Correction under the Transferable Belief Model
- 将信念质量映射为量子态，实现线性复杂度的融合运算
- 在多源分类任务中显著增强对噪声的抗性
- 适合研究量子信息融合与可信推理的学者
转移信念模型（TBM）基于达斯-谢弗理论，通过集合型信念质量表示模糊与部分无知。其经典运算在幂集上存在组合爆炸问题。本文提出使用质量函数量子态（MFQS）在量子电路中实现TBM推理，涵盖信念表示、信任层融合与修正、乘积空间操作及广义贝叶斯定理。对于预准备的MFQS输入，CCR与全部α-交集采用逐元素门模块，逻辑门数量与框架元素数呈线性关系，避免了对全部2^n个焦点集坐标的显式更新。一个含噪声的多领域分类器融合示例表明，该方法具有可解释的源融合能力，并显著提升对门控制噪声的鲁棒性。结果证明，达斯-谢弗结构可作为超越单点概率表示的量子信息处理语义层。
原文摘要 · Abstract (English)
The transferable belief model (TBM), developed within Dempster-Shafer theory, represents ambiguity and partial ignorance through set-valued belief masses. Its classical operations, however, can grow combinatorially over the power set. We formulate TBM reasoning on quantum circuits using the mass function quantum state (MFQS). The resulting framework covers belief representation, credal-level fusion and correction, product-space operations, and the generalized Bayesian theorem. For prepared MFQS inputs, CCR and the entire $α$-junction use element-wise gate modules whose logical counts are linear in the number of frame elements. These circuits avoid explicit operation-stage updates over all $2^n$ focal-set coordinates. A noisy multi-domain classifier-fusion example further demonstrates interpretable source fusion and improved robustness to gate-control noise. The results establish Dempster-Shafer structures as a semantic layer for quantum information processing beyond singleton-probability representations.
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