This article discusses the Dispersive Quantum Particle Swarm Optimization algorithm, which can be used to solve global optimization problems. The algorithm, based on quantum mechanics and the laws of particle swarms, enhances the search for global extrema through probabilistic characteristics of particle position updates. Hamming distances between particles allow for the introduction of dispersion to assess swarm convergence and prevent premature stagnation. Improvements in the balance between global and local search through adaptive changes to the alpha parameter, as well as the use of quantum effects for more effective navigation of local minima, are the main outcomes of this work. The algorithm was tested on the Rosenbrock, Schwefel, Rastrigin, Griewank, Eckart, and sphere functions. The results show that Dispersive Quantum Particle Swarm Optimization algorithm can compete with traditional optimization methods.