论文标题
量子设备使用无监督的嵌入学习微调
Quantum device fine-tuning using unsupervised embedding learning
论文作者
论文摘要
具有大量门电极的量子设备可以精确控制设备参数。由于这些参数对应用门电压的复杂依赖性，因此难以完全利用此能力。我们在实验上展示了一种能够一次微调几个设备参数的算法。该算法获得了测量，并使用各种自动编码器为其分配得分。门电压设置设置为以无监督的方式实时优化此分数。我们报告大约40分钟内双量子点设备的微调时间。
Quantum devices with a large number of gate electrodes allow for precise control of device parameters. This capability is hard to fully exploit due to the complex dependence of these parameters on applied gate voltages. We experimentally demonstrate an algorithm capable of fine-tuning several device parameters at once. The algorithm acquires a measurement and assigns it a score using a variational auto-encoder. Gate voltage settings are set to optimise this score in real-time in an unsupervised fashion. We report fine-tuning times of a double quantum dot device within approximately 40 min.
