Abstract
We report an algorithm designed to perform computer-automated tuning of a single quantum dot with a charge sensor. The algorithm performs an adaptive measurement sequence of sub-sized stability diagrams until the single-electron regime is identified and reached. For each measurement, the signal processing module removes the physical background of the charge sensor to generate a binary image of charge transitions. Then, the image analysis module identifies the position and number of lines using two line detection schemes that are robust to noise and missing data.
Highlights
Spin qubits in quantum dots are among the frontrunner architectures for the implementation of a small-scale quantum computer [1,2] due to their high potential for scalability [3,4,5,6]
Software has been developed to address this issue by automatizing tedious parts of this process for double dots using image analysis or machine learning tools to adjust the interdot tunnel coupling [9], detect triple points in stability diagrams [10,11], and perform state recognition [12,13,14,15]
This has been recognized as challenging for the following reasons: (i) Tuning a single dot requires line detection, which proves to be less robust than the detection of triple points [10]. (ii) The number of transitions in a measurement is a priori unknown. (iii) The detection of transition lines with possible curvature and in the presence of noise and missing data points is computationally expensive [16]. (iv) The charge sensor couples to all charges at proximity, measuring several unwanted features, giving rise to a physical background in the resulting signal
Summary
Spin qubits in quantum dots are among the frontrunner architectures for the implementation of a small-scale quantum computer [1,2] due to their high potential for scalability [3,4,5,6]. We report an algorithm designed to perform automated tuning of a single quantum dot tunnel coupled to a reservoir of electrons using only charge sensing This has been recognized as challenging for the following reasons: (i) Tuning a single dot requires line detection, which proves to be less robust than the detection of triple points [10]. The signal processing module removes the physical background of the charge sensor to generate a map of detected charge transitions, and the image analysis module reconstructs the transition lines from that map The latter was implemented using two different approaches, namely, a modified Hough transform [17] and the EDLines algorithm [18], each of which have different advantages regarding computation time and detection of curved transitions. This sequence is based on a heuristic that aims to find the quantum dot regime, empty it, and add one electron
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