The analysis of simulation results obtained using soft computing technologies has allowed one to establish the following fact important for developing technologies for designing robust intelligent control systems. Designed (in the general form for random conditions) robust fuzzy controllers for dynamic control objects based on the knowledge base optimizer (stage 1 of the technology) with the use of soft computing can operate efficiently only for fixed (or weakly varying) descriptions of the external environment. This is caused by possible loss of the robustness property under a sharp change of the functioning conditions of control objects: the internal structure of control objects, control actions (reference signal), the presence of a time delay in the measurement and control channels, under variation of conditions of functioning in the external environment, and the introduction of other weakly formalized factors in the control strategy. In this paper, a description of the strategy of designing robust structures of an intelligent control system based on the technologies of quantum and soft computing is given. The developed strategy allows one to improve the robustness level of fuzzy controllers under the specified unpredicted or weakly formalized factors for the sake of forming and using new types of processes of self-organization of the robust knowledge base with the help of the methodology of quantum computing. Necessary facts from quantum computing theory, quantum algorithms, and quantum information are presented. A particular solution of a given problem is obtained by introducing a generalization of strategies in models of fuzzy inference on a finite set of fuzzy controllers designed in advance in the form of new quantum fuzzy inference. The fundamental structure of quantum fuzzy inference and its software toolkit in the processes of designing the knowledge base of robust fuzzy controllers in on-line, as well as a system for simulating robust structures of fuzzy controllers, are described. The efficiency of applying quantum fuzzy inference is illustrated by a particular example of simulation of robust control processes by an essentially nonlinear dynamic control object with randomly varying structure.