This article presents a technology being developed for the design of an intelligent robust control system based on quantum and soft computing. The corresponding software tools and stages of creating an embedded control system are presented. In particular, the application of soft computing, fuzzy logic, fuzzy neural networks, and evolutionary and quantum computing using quantum-inspired algorithms implemented on classical processors is considered. As an illustrative example, the problem of controlling refrigerant pressure during testing of superconducting magnets is addressed. The results of applying a coordination control system with an integrated quantum controller are presented. The influence of the control system on the cooling process of superconductive magnets and temperature effects are shown. The efficiency of the quantum-inspired algorithm is demonstrated and the advantages of using the developed intelligent control systems and software are shown. The effectiveness of the system is shown in terms of control quality criteria based on experimental data.