This paper presents a type-2 quantum fuzzy logic controller (T2QFLC) for robot manipulators with unstructured dynamical uncertainty that is customized using quantum evolutionary algorithm. In order to effectively construct type-2 fuzzy logic controllers, quantum genetic algorithms are used to simultaneously design type-2 fuzzy sets and rule sets. Traditional fuzzy logic controllers (FLCs), also known as type-1 fuzzy logic systems employing type-1 fuzzy sets, struggle to describe and reduce the impact of uncertainties that are present in many real-time applications. As a result, type-2 FLC has recently been suggested. A collection of several embedded type-1 FLCs can be thought of as the type-2 FLC. The type-2 FLC design method now in use, however, is not automated and relies on the heuristic expertise of seasoned operators. Our research is aimed at automating the design process. The type-2 FLCs that have emerged can deal with a lot of uncertainty and perform better for mobile robots. Additionally, it has outperformed both the conventionally constructed type-2 FLCs and their type-1 equivalents.