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arXiv 1305.4672quant-ph
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探索量子控制景观结构

Exploring Quantum Control Landscape Structure

Arun Nanduri, Ashley Donovan, Tak-San Ho, Herschel Rabitz

摘要:

量子控制的一个共同目标是通过应用定制的场来最大化物理可观察性。作为场的函数的可观测值构成了量子控制景观。以前的工作表明,在特定条件下,量子控制景观应该没有次优临界点。这种有利的景观拓扑结构是有助于攀登景观效率的一个因素。另一个补充因素是景观\textit{structure},它构成了所有非拓扑特征。如果景观的结构太复杂,那么攀登者可能会被迫采取低效的迂回路线来寻找最佳控制。本文提供了一个理解控制景观结构的基础上,通过检查的线性梯度为基础的优化轨迹,通过空间的控制领域。对于该评估,度量$R\geq 1$被定义为优化轨迹的路径长度与初始控制场和从景观的底部到顶部获取可观测值的所得最优控制场之间的欧几里得距离的比率。简单模型量子系统的计算分析进行,以确定在优化状态到状态的转移概率时遇到的近直的控制轨迹的相对丰度。收集的结果表明,量子控制景观具有非常简单的结构特征。有利的拓扑结构和互补的简单结构的控制景观提供了一个基础,为理解通常观察到的状态到状态的转移概率优化容易。

英文摘要:

A common goal of quantum control is to maximize a physical observable through the application of a tailored field. The observable value as a function of the field constitutes a quantum control landscape. Previous works have shown, under specified conditions, that the quantum control landscape should be free of suboptimal critical points. This favorable landscape topology is one factor contributing to the efficiency of climbing the landscape. An additional, complementary factor is the landscape \textit{structure}, which constitutes all non-topological features. If the landscape's structure is too complex, then climbs may be forced to take inefficient convoluted routes to finding optimal controls. This paper provides a foundation for understanding control landscape structure by examining the linearity of gradient-based optimization trajectories through the space of control fields. For this assessment, a metric $R\geq 1$ is defined as the ratio of the path length of the optimization trajectory to the Euclidean distance between the initial control field and the resultant optimal control field that takes an observable from the bottom to the top of the landscape. Computational analyses for simple model quantum systems are performed to ascertain the relative abundance of nearly straight control trajectories encountered when optimizing a state-to-state transition probability. The collected results indicate that quantum control landscapes have very simple structural features. The favorable topology and the complementary simple structure of the control landscape provide a basis for understanding the generally observed ease of optimizing a state-to-state transition probability.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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