论文标题
使用深度学习来识别Pauli自旋封锁
Identifying Pauli spin blockade using deep learning
论文作者
论文摘要
Pauli自旋封锁（PSB）也可以用作自旋量子量子初始化和读数的重要资源，即使在升高的温度下，也可能很难识别。我们提出了一种能够使用电荷传输测量值自动识别PSB的机器学习算法。通过使用模拟数据训练算法并使用跨设备验证，可以规避PSB数据的稀缺性。我们在硅现场效应晶体管设备上演示了我们的方法，并在不同的测试设备上报告了96％的精度，证明该方法对设备可变性是可靠的。预计该方法将在所有类型的量子点设备中使用。
Pauli spin blockade (PSB) can be employed as a great resource for spin qubit initialisation and readout even at elevated temperatures but it can be difficult to identify. We present a machine learning algorithm capable of automatically identifying PSB using charge transport measurements. The scarcity of PSB data is circumvented by training the algorithm with simulated data and by using cross-device validation. We demonstrate our approach on a silicon field-effect transistor device and report an accuracy of 96% on different test devices, giving evidence that the approach is robust to device variability. The approach is expected to be employable across all types of quantum dot devices.
