The discrepancies between reality and simulation impede the optimisation and scalability of solid-state quantum devices. Disorder induced by the unpredictable distribution of material defects is one of the major contributions to the reality gap. We bridge this gap using physics-aware machine learning, in particular, using an approach combining a physical model, deep learning, Gaussian random field, and Bayesian inference. This approach has enabled us to infer the disorder potential of a nanoscale electronic device from electron transport data. This inference is validated by verifying the algorithm's predictions about the gate voltage values required for a laterally-defined quantum dot device in AlGaAs/GaAs to produce current features corresponding to a double quantum dot regime.

翻译：现实和模拟之间的差异妨碍了固态量子装置的优化和可缩放。物质缺陷的不可预测的分布引起的混乱是造成现实差距的主要原因之一。我们利用物理觉悟机器学习,特别是采用物理模型、深层学习、高斯随机场和贝叶斯推论相结合的方法,弥合了这一差距。这种方法使我们能够从电子传输数据中推断纳米电子装置的混乱潜力。这一推论通过核实AlGaAs/GaAs对横向定义量子点装置所需的门电压值的算法预测来验证。