This paper explores the use of laboratory closed loop learning control to supppress the effects of decoherence in quantum dynamics. Simulations of the process are performed in multilevel quantum systems strongly interacting with the environment. A genetic algorithm is used to find an optimal control field which seeks out tansition pathways to achieve a minimum influence of decoherence upon the system at a target time. The simulations suggest that decoherence may be optimally managed in thelaboratory throughclose dloop operations with a suitable cost that is sensitive to the coherence of the dynamics. The case studies of dimentison N=4 and N=10 withstrong system-environment coupling indicate that the additional complexity with increasing system dimension can make it more difficult to manage decoherence.
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机译：本文探索了使用实验室闭环学习控制来抑制退相干在量子动力学中的作用。在与环境有强烈相互作用的多级量子系统中执行该过程的仿真。使用遗传算法来找到最佳控制场，该控制场找出过渡路径，以在目标时间对系统退相干的影响达到最小。仿真结果表明，在实验室中可以通过闭环操作以最佳方式管理去相干性，并且对动态的相干性敏感。具有强烈的系统-环境耦合的Dimentison N = 4和N = 10的案例研究表明，随着系统尺寸的增加，额外的复杂性会使管理去相干性变得更加困难。
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