Comparison of differential evolution, particle swarm optimization, quantum-behaved particle swarm optimization, and quantum evolutionary algorithm for preparation of quantum states

Four intelligent optimization algorithms are compared by searching for control pulses to achieve the preparation of target quantum states for closed and open quantum systems, which include differential evolution (DE), particle swarm optimization (PSO), quantum-behaved particle swarm optimization (QPSO), and quantum evolutionary algorithm (QEA). We compare their control performance and point out their differences. By sampling and learning for uncertain quantum systems, the robustness of control pulses found by these four algorithms is also demonstrated and compared. The resulting research shows that the QPSO nearly outperforms the other three algorithms for all the performance criteria considered. This conclusion provides an important reference for solving complex quantum control problems by optimization algorithms and makes the QPSO be a powerful optimization tool.

Automated summary

Four intelligent optimization algorithms, including DE, PSO, QPSO, and QEA, are compared in searching for control pulses for target quantum state preparation in closed and open quantum systems. Their control performance is compared and their differences are pointed out. The robustness of control pulses found by these four algorithms is also demonstrated and compared for uncertain quantum systems. The research shows that QPSO outperforms the other three algorithms in all the considered performance criteria, making it a powerful optimization tool for solving complex quantum control problems.