论文标题
双层石墨烯量子点中结合状态的自动重建
Automated reconstruction of bound states in bilayer graphene quantum dots
论文作者
论文摘要
双层石墨烯是一种纳米材料，允许通过静电门进行定义明确的，分离的量子状态，因此为构造可调量子点提供了一个有吸引力的平台。当施加垂直于石墨烯层的磁场时，将抬高石墨烯谷退化，并观察到点的能量水平。鉴于实验能力设计了这种能量谷分裂的能力，双层石墨烯量子点具有托管强大的Qubits的巨大潜力。尽管最近在实验中已经实现了双层石墨烯量子点，但设计可识别可访问的测量数据的量子状态的强大方法至关重要。在这里，我们开发了一种有效的算法，用于提取完全表征双层石墨烯量子点状态所需的模型参数。我们引入了汉密尔顿指导的随机搜索方法，并在模拟和实验数据上证明了量子状态的强大鉴定。
Bilayer graphene is a nanomaterial that allows for well-defined, separated quantum states to be defined by electrostatic gating and, therefore, provides an attractive platform to construct tunable quantum dots. When a magnetic field perpendicular to the graphene layers is applied, the graphene valley degeneracy is lifted, and splitting of the energy levels of the dot is observed. Given the experimental ability to engineer this energy valley splitting, bilayer graphene quantum dots have a great potential for hosting robust qubits. Although bilayer graphene quantum dots have been recently realized in experiments, it is critically important to devise robust methods that can identify the observed quantum states from accessible measurement data. Here, we develop an efficient algorithm for extracting the model parameters needed to characterize the states of a bilayer graphene quantum dot completely. We introduce a Hamiltonian-guided random search method and demonstrate robust identification of quantum states on both simulated and experimental data.
