Krotov方法在寻找开放量子系统控制中的有效性

Marllos E F Fernandes, Felipe F Fanchini, Emanuel de Lima, Leonardo Kleber Castelano
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引用次数: 1

摘要

摘要:我们将Krotov方法应用于开放和封闭量子系统,以寻找在外部环境存在的情况下操纵量子比特/量子元系统的优化控制。在酉优化的情况下,首先将克罗托夫方法应用于量子系统,忽略其与环境的相互作用。然后用统一优化得到的控制结果来驱动系统和环境噪声。在非酉优化的情况下,Krotov方法已经考虑了优化过程中的噪声。我们考虑两个不同的计算任务:目标态准备和量子门实现。这些任务可以在简单的量子位/量子位系统中执行,也可以在呈现泄漏状态的系统中执行。对于状态准备情况,非统一优化控制优于统一优化控制。然而,正如我们在这里展示的,对于量子门的实现来说,这并不总是正确的。在某些情况下,与非酉优化相比,酉优化执行得同样好。我们验证了这些情况对应于没有泄漏状态或耗散均匀分布在系统上的影响,包括非计算水平。对于这种情况,量子门的实现必须覆盖整个希尔伯特空间,并且没有办法避免耗散。另一方面,如果包含计算层及其补的子空间受耗散的影响不同,则非酉优化是有效的。
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Effectiveness of the Krotov method in finding controls for open quantum systems
Abstract We apply the Krotov method for open and closed quantum systems to find optimized controls to manipulate qubit/qutrit systems in the presence of the external environment. In the case of unitary optimization, the Krotov method is first applied to a quantum system neglecting its interaction with the environment. The resulting controls from the unitary optimization are then used to drive the system along with the environmental noise. In the case of non-unitary optimization, the Krotov method already takes into account the noise during the optimization process. We consider two distinct computational tasks: target-state preparation and quantum gate implementation. These tasks are carried out in simple qubit/qutrit systems and also in systems presenting leakage states. For the state preparation cases, the controls from the non-unitary optimization outperform the controls from the unitary optimization. However, as we show here, this is not always true for the implementation of quantum gates. There are some situations where the unitary optimization performs equally well compared to the non-unitary optimization. We verify that these situations correspond to either the absence of leakage states or to the effects of dissipation being spread uniformly over the system, including non-computational levels. For such cases, the quantum gate implementation must cover the entire Hilbert space and there is no way to dodge dissipation. On the other hand, if the subspace containing the computational levels and its complement are differently affected by dissipation, the non-unitary optimization becomes effective.
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