Гібридний класично-квантовий алгоритм та його реалізація на реальному квантовому комп’ютері

V.M. Tkachuk

doi:10.15330/itee.2025.3.03

Authors

V.M. Tkachuk Vasyl Stefanyk Carpathian National University https://orcid.org/0000-0001-7366-1676

DOI:

https://doi.org/10.15330/itee.2025.3.03

Keywords:

quantum genetic algorithm, hybrid algorithm, functional optimization, Qiskit, AerSimulator, quantum tomography, superposition, entanglement

Abstract

In this work a Hybrid Quantum Genetic Algorithm (HQGA) for functional optimization problems is presented. All basic operators are implemented using a quantum computer, while auxiliary computations, such as the fitness function and the search for the best individual in the population, are performed on a classical computer. Quantum state tomography is used to organize the evolutionary process – recovering the probability amplitudes of a qubit state based on the measurement results of its quantum ensemble.

HQGA modeling is conducted using the IBM Qiskit environment with the ideal AerSimulator and models of real quantum computers (FakeMarrakesh, FakeBrisbane, FakeKyoto, etc.). Results demonstrate that it provides fast convergence with small population sizes, outperforming classical genetic algorithms in speed and accuracy, and requires a small number of iterations. The evaluation of the impact of simulation parameters (noise models of real quantum computers) confirmed the method's robustness in overcoming hardware limitations of modern IBM quantum devices and quantum errors.

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