Solving the Ising Problem by Noisy Quantum Genetic Algorithms

Quantum Genetic Algorithms (QGAs) are emerging as a new class of algorithms that exploit the peculiarities of quantum computers to outperform classical evolutionary approaches in solving complex optimization problems. Despite the theoretical promises, their applicability has been strongly affected by the limitation of current noise-prone quantum hardware. It has only recently been demonstrated that acceptable levels of quantum noise can enhance the exploration capability of QGA in constrained optimization problems. The research corroborates the initial findings, providing further evidence of the efficacy of these algorithms by successfully resolving the NP-hard Ising Problem through the utilization of a QGA equipped with a quantum mating operator. The experimental results show that noisy simulations of QGA are capable of identifying better solutions than genetic algorithms that are equipped with state-of-the-art classical mating operators.