IMPROVED QUANTUM GENETIC ALGORITHM WITH QUTRITE REPRESENTATION IN FUNCTIONAL OPTIMIZATION PROBLEMS

Tyryshkin Sergey Yurievich

Quantum optimization algorithms have the potential to revolutionize the application of brute-force methods in decision making. It is widely believed that for certain classes of optimization problems, quantum algorithms can achieve significant performance gains over current state-of-the-art solutions. Taking into account the fact that the latest achievements in the field of quantum computers are reaching the stage of industrialization, optimization algorithms based on quantum technologies become more and more relevant. Taking into account the noted, the paper considers the possibilities of the improved quantum genetic algorithm with cutrite representation in functional optimization problems. The developed scheme of the algorithm is based on decomposition of the generalized Hadamard gate without the use of anksill. The considered solution of the MaxCut problem shows that the algorithm has a higher probability of sampling the correct solution, and can do so with fewer layers.

Keywords:quantum algorithm, kurtrit, optimization, transition, gate