Bridging the reality gap in quantum devices with physics-aware machine learning
Bridging the reality gap in quantum devices with physics-aware machine learning
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DOI：
10.1103/physrevx.14.011001
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发表时间：
2021-11
期刊：
影响因子：
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通讯作者：
D. L. Craig;H. Moon;F. Fedele;D. Lennon;B. V. Straaten;F. Vigneau;L. Camenzind;D. Zumbuhl;G. Briggs;Michael A. Osborne;D. Sejdinovic;N. Ares
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文献类型：
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作者：
D. L. Craig;H. Moon;F. Fedele;D. Lennon;B. V. Straaten;F. Vigneau;L. Camenzind;D. Zumbuhl;G. Briggs;Michael A. Osborne;D. Sejdinovic;N. Ares
现实与模拟之间的差异阻碍了固态量子器件的优化和可扩展性。由不可预测的材料缺陷分布引起的无序是造成现实差距的主要原因之一。我们使用物理感知机器学习来弥补这一差距，特别是使用结合物理模型、深度学习、高斯随机场和贝叶斯推理的方法。这种方法使我们能够从电子传输数据推断纳米级电子设备的无序潜力。通过验证算法对 AlGaAs/GaAs 中横向定义的量子点器件所需的栅极电压值的预测来验证该推论，以产生与双量子点体系相对应的电流特征。
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.