论文标题
机器学习优化Majorana混合动力纳米线
Machine learning optimization of Majorana hybrid nanowires
论文作者
论文摘要
随着量子系统（例如量子位阵列）的复杂性增加，自动化昂贵调整的努力越来越值得。我们使用CMA-ES算法研究基于机器学习的栅极阵列的调整，以进行强大障碍的Majorana电线的案例研究。我们发现该算法能够有效地改善拓扑特征，学习固有的疾病概况并完全消除障碍效应。例如，只有20个门，就可以通过优化栅极电压来完全恢复被无序破坏的Majorana零模式。
As the complexity of quantum systems such as quantum bit arrays increases, efforts to automate expensive tuning are increasingly worthwhile. We investigate machine learning based tuning of gate arrays using the CMA-ES algorithm for the case study of Majorana wires with strong disorder. We find that the algorithm is able to efficiently improve the topological signatures, learn intrinsic disorder profiles, and completely eliminate disorder effects. For example, with only 20 gates, it is possible to fully recover Majorana zero modes destroyed by disorder by optimizing gate voltages.
