Machine learning enables completely automatic tuning of a quantum device faster than human experts
Machine learning enables completely automatic tuning of a quantum device faster than human experts
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DOI：
10.1038/s41467-020-17835-9
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发表时间：
2020-08-19
影响因子：
16.6
通讯作者：
Ares, N.
中科院分区：
文献类型：
--
作者：
Moon, H.;Lennon, D. T.;Ares, N.
可变性是半导体量子器件可扩展性的一个问题。参数空间大，操作范围小。我们的统计调谐算法在栅极电压空间高达8维的门定义量子点器件中搜索特定的电子输运特征。从每个栅极电压的全范围开始，我们的机器学习算法可以在不到70分钟的中位数时间内将每个器件调整到最佳性能。这个性能超过了我们最好的人类基准（尽管人类和机器的性能都可以提高）。该算法比参数空间的自动随机搜索快约180倍，适用于不同的材料系统和器件架构。我们的结果产生了一个定量测量设备可变性，从一个设备到另一个和热循环后。我们的机器学习算法可以扩展到更高维度和其他技术。为了优化大规模半导体量子器件的运行条件，必须探索一个大的参数空间。在这里，作者报告了一种机器学习算法，可以导航门定义量子点设备的整个参数空间，比纯随机搜索快180倍。
Variability is a problem for the scalability of semiconductor quantum devices. The parameter space is large, and the operating range is small. Our statistical tuning algorithm searches for specific electron transport features in gate-defined quantum dot devices with a gate voltage space of up to eight dimensions. Starting from the full range of each gate voltage, our machine learning algorithm can tune each device to optimal performance in a median time of under 70 minutes. This performance surpassed our best human benchmark (although both human and machine performance can be improved). The algorithm is approximately 180 times faster than an automated random search of the parameter space, and is suitable for different material systems and device architectures. Our results yield a quantitative measurement of device variability, from one device to another and after thermal cycling. Our machine learning algorithm can be extended to higher dimensions and other technologies. To optimize operating conditions of large scale semiconductor quantum devices, a large parameter space has to be explored. Here, the authors report a machine learning algorithm to navigate the entire parameter space of gate-defined quantum dot devices, showing about 180 times faster than a pure random search.