Efficiently measuring a quantum device using machine learning
This addresses the challenge of scalable quantum technologies by automating measurement processes, which is incremental as it builds on existing machine learning methods for quantum devices.
The paper tackled the problem of time-consuming characterization and tuning of quantum devices by using a machine learning algorithm to automate measurements, reducing the number of measurements by up to 4 times and measurement time by 3.7 times compared to standard grid scans.
Scalable quantum technologies will present challenges for characterizing and tuning quantum devices. This is a time-consuming activity, and as the size of quantum systems increases, this task will become intractable without the aid of automation. We present measurements on a quantum dot device performed by a machine learning algorithm. The algorithm selects the most informative measurements to perform next using information theory and a probabilistic deep-generative model, the latter capable of generating multiple full-resolution reconstructions from scattered partial measurements. We demonstrate, for two different measurement configurations, that the algorithm outperforms standard grid scan techniques, reducing the number of measurements required by up to 4 times and the measurement time by 3.7 times. Our contribution goes beyond the use of machine learning for data search and analysis, and instead presents the use of algorithms to automate measurement. This work lays the foundation for automated control of large quantum circuits.