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In this paper, a combination of a multi-layer quantum neural network (QNN) with the particle swarm optimization (PSO) algorithm is used with the aim of controlling a continuous stirred-tank reactor (CSTR) system. The CSTR process is highly non-linear and its dynamics are significantly sensitive to system parameter values. Normally, conventional controllers with fixed coefficients are applied to control this kind of system. In highly non-linear systems, having fixed controller coefficients in different operational conditions may decrease the performance of controllers. In the proposed scheme, by using a multi-layer QNN, an adaptive structure is designed for a PI-D controller. In order to train the QNN, the PSO algorithm is employed. With the aim of improving accuracy and convergence speed of the training process, some modifications have been applied to the movement of each particle towards the optimal point …