Quantum simulator for modeling intelligent fuzzy control

When using quantum soft computing and the principles of quantum deep machine learning in problems of robust intelligent / cognitive fuzzy control of real control objects, there problems arise in the implementation of software and hardware. This complicates the development and testing of quantum algorithms, requires more complex equipment. These and many other problems can be solved by creating a simulator of intelligent control. Such a simulator simplifies the development of software and can be used in the development of commercial products and for educational purposes. This article discusses an example of controlling globally unstable system “cart-pole”. For the control of which the algorithm of quantum fuzzy inference is used, which contains in its structure the quantum genetic algorithm - an improved version of the classical genetic algorithm. The use of such an algorithm on a quantum computer solves the main problem - the speed of work, which in the classical version does not allow the system to be trained in on line. In theory, in a real quantum algorithm, a population can be made up of just one chromosome in a state of superposition. Also, the use of various types of quantum genetic algorithms on a quantum computer can solve the problem of supercomputing.