Abstract

This work provides a generalized view of the current state of quantum genetic algorithms (QGAs), showing the advances made in this research field over the last 24 years. QGAs combine concepts from quantum computing and classical genetic algorithms (CGAs), allowing them to address complex search and optimization problems efficiently. The main findings and contributions of these quantum algorithms are presented, highlighting the most promising trends and approaches, as well as the challenges and limitations that need to be overcome. New approaches and implementation techniques for QGAs are presented, including quantum genetic operators and efficient coding schemes that contribute to improving the performance and convergence of the algorithms. QGAs and other similar approaches, such as CGAs and pure quantum algorithms, are compared, highlighting the relative advantages and disadvantages of QGAs compared to their classical versions. An implementation of QGA using the Qiskit library is also shown. The selection of the methods used for the generation of the initial population, the crossing and the mutation of the different populations of the quantum circuits simulated in the experiments carried out are presented, exemplifying the significant advantages that these can bring in comparison with classical approaches.