QZNs: Quantum Z-numbers
This work addresses the need for quantum-compatible fuzzy modeling in decision-making, particularly for medical diagnosis, but appears incremental as it extends existing Z-number concepts to a quantum setting.
The authors tackled the problem of Z-numbers being unable to handle quantum information by proposing quantum Z-numbers (QZNs), a quantum generalization, and developed a quantum multi-attributes decision making algorithm for medical diagnosis, which they claim makes diagnoses correctly and efficiently.
Because of the efficiency of modeling fuzziness and vagueness, Z-number plays an important role in real practice. However, Z-numbers, defined in the real number field, lack the ability to process the quantum information in quantum environment. It is reasonable to generalize Z-number into its quantum counterpart. In this paper, we propose quantum Z-numbers (QZNs), which are the quantum generalization of Z-numbers. In addition, seven basic quantum fuzzy operations of QZNs and their corresponding quantum circuits are presented and illustrated by numerical examples. Moreover, based on QZNs, a novel quantum multi-attributes decision making (MADM) algorithm is proposed and applied in medical diagnosis. The results show that, with the help of quantum computation, the proposed algorithm can make diagnoses correctly and efficiently.