Function Optimization Based on Higher-Order Quantum Genetic Algorithm

V.M. Tkachuk, Ph.D. (Phys.-Math.), M.I. Kozlenko, Ph.D (Eng.),
M.V. Kuz Dr.Sc. (Eng.), I.M. Lazarovych Ph.D. (Eng.), M.C. Dutchak
Vasyl Stefanyk Precarpathian National University
(57 Shevchenko str., Ivano-Frankivsk, 76018, Ukraine,

Èlektron. model. 2019, 41(3):43-58


Quantum genetic algorithms (QGA) are typically built using the traditional representation of theQuantum genetic algorithms (QGA) are typically built using the traditional representation of thequantum chromosome in the form of system of independent qubits. This makes it impossible touse a very powerful quantum calculations mechanism, namely quantum state entanglement. Inthis paper we implement a higher-order QGA and illustrate efficiency of the algorithm on the basisof example of optimization problem solved for a test functions set. An adaptive quantum gateoperator, which does not require a lookup table is also proposed. In comparison to traditionalQGA, the transition to higher (more than two) orders in the algorithm implementation showsmuch better results in terms of the running time, convergence speed and solution precision.


function optimization, quantum state entanglement, quantum genetic algorithm,function optimization, quantum state entanglement, quantum genetic algorithm,quantum computation, quantum register.


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