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.

翻译：Pauli 旋转封隔( PSB) 即使在高温下也可以用作旋转当量初始化和读出的巨大资源,但很难识别。 我们展示了一种机器学习算法,能够通过电路传输测量自动识别PSB。 利用模拟数据和交叉装置验证方法对算法进行培训,避免了PSB数据的稀缺性。 我们展示了我们对于硅的现场效果晶体管装置的处理方法,并报告不同测试装置的精确度为96%,从而证明该方法对装置的变异性非常有力。 这种方法有望在所有类型的量子点装置中都能使用。