论文标题
使用机器学习的所有基于RF的调谐算法，用于量子设备
All rf-based tuning algorithm for quantum devices using machine learning
论文作者
论文摘要
射频测量值可以满足Divincenzo的未来大规模固态量子处理器的读数标准，因为它们允许高带宽和频率多路复用。但是，只有使用专门的射频测量值（即不诉诸当前测量值）进行量子设备调整，则只有在执行量子设备调整时才能利用此读出技术的可伸缩性。我们演示了一种算法，该算法仅使用射频反射仪自动调谐双量子点。利用射频测量的高带宽，调整在几分钟内完成，而没有有关设备体系结构的先验知识。我们的结果表明，有可能消除对量子点调整的运输测量的需求，为更可扩展的设备体系结构铺平了道路。
Radio-frequency measurements could satisfy DiVincenzo's readout criterion in future large-scale solid-state quantum processors, as they allow for high bandwidths and frequency multiplexing. However, the scalability potential of this readout technique can only be leveraged if quantum device tuning is performed using exclusively radio-frequency measurements i.e. without resorting to current measurements. We demonstrate an algorithm that automatically tunes double quantum dots using only radio-frequency reflectometry. Exploiting the high bandwidth of radio-frequency measurements, the tuning was completed within a few minutes without prior knowledge about the device architecture. Our results show that it is possible to eliminate the need for transport measurements for quantum dot tuning, paving the way for more scalable device architectures.
