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。