A machine learning approach for automated fine-tuning of semiconductor spin qubits
A machine learning approach for automated fine-tuning of semiconductor spin qubits
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DOI：
10.1063/1.5088412
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发表时间：
2019-01
影响因子：
4
通讯作者：
J. Teske;S. Humpohl;R. Otten;P. Bethke;Pascal Cerfontaine;Jonas Dedden;A. Ludwig;A. Wieck;H. Bluhm
中科院分区：
文献类型：
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作者：
J. Teske;S. Humpohl;R. Otten;P. Bethke;Pascal Cerfontaine;Jonas Dedden;A. Ludwig;A. Wieck;H. Bluhm
虽然基于栅极定义的量子点的自旋量子位已经证明了量子计算的非常有利的特性，但仍然存在的一个障碍是需要通过调整施加到静电栅极的电压来将它们中的每一个调谐到良好的操作状态。这些调谐过程的自动化是基于门定义的量子点的量子处理器的操作的必要要求，这尚未完全解决。我们提出了一种自动微调量子点的算法，并在GaAs半导体单重态-三重态量子比特上展示了其性能。该算法采用基于贝叶斯统计的卡尔曼滤波器来估计作为门电压的函数的目标参数的梯度，从而学习系统响应。该算法的设计重点是减少所需的测量的数量。我们通过实验证明了在3到5次迭代内改变量子位操作机制的能力，相当于10到15分钟的实验室时间。
While spin qubits based on gate-defined quantum dots have demonstrated very favorable properties for quantum computing, one remaining hurdle is the need to tune each of them into a good operating regime by adjusting the voltages applied to electrostatic gates. The automation of these tuning procedures is a necessary requirement for the operation of a quantum processor based on gate-defined quantum dots, which is yet to be fully addressed. We present an algorithm for the automated fine-tuning of quantum dots, and demonstrate its performance on a semiconductor singlet-triplet qubit in GaAs. The algorithm employs a Kalman filter based on Bayesian statistics to estimate the gradients of the target parameters as function of gate voltages, thus learning the system response. The algorithm's design is focused on the reduction of the number of required measurements. We experimentally demonstrate the ability to change the operation regime of the qubit within 3 to 5 iterations, corresponding to 10 to 15 minutes of lab-time.