In spin based quantum dot arrays, a leading technology for quantum computation applications, material or fabrication imprecisions affect the behaviour of the device, which is compensated via tuning parameters. Automatic tuning of these device parameters constitutes a formidable challenge for machine-learning. Here, we present the first practical algorithm for controlling the transition of electrons in a spin qubit array. We exploit a connection to computational geometry and phrase the task as estimating a convex polytope from measurements. Our proposed algorithm uses active learning, to find the count, shapes and sizes of all facets of a given polytope. We test our algorithm on artifical polytopes as well as a real 2x2 spin qubit array. Our results show that we can reliably find the facets of the polytope, including small facets with sizes on the order of the measurement precision. We discuss the implications of the NP-hardness of the underlying estimation problem and outline design considerations, limitations and tuning strategies for controlling future large-scale spin qubit devices.
翻译:量子点阵列是量子计算应用、材料或制造不精确的主要技术,它影响设备的行为,通过调试参数予以补偿。自动调整这些设备参数是机器学习的艰巨挑战。在这里,我们提出第一个控制旋角数阵列中电子转换的实际算法。我们利用计算几何的连接,并将任务表述为从测量中估计锥形多孔径。我们提议的算法利用积极学习,找到某一多管形方形的数、形状和大小。我们测试我们的算法,以人工多管和真实的 2x2 旋角数阵列为基础。我们的结果显示,我们可以可靠地找到多管的方形,包括测量精度的大小的小方块。我们讨论了基本估计问题NP-硬性的影响,并概述了设计考虑、限制和调整策略,以控制未来的大型旋角装置。