Automated Tuning of Gate-defined Quantum Dots

Abstract

Building a quantum computer able to solve real-world problems is facing several challenges, both in software and hardware. Some of these challenges are the physical implementation of a large number of qubits, as well as qubit quality and control. This thesis addresses one of the outstanding challenges - the automated initialisation of solid-state qubits. These qubit types, which encode information in charge, spin or topologically protected quasiparticle states, are based on electrostatically defined quantum dots. In order to define quantum dots, suitable gate voltage combinations need to be determined and several parameters such as the inter-dot tunnel coupling adjusted to desired values. With growing device complexity and an increasing number of devices required for measurements, a manual approach to tuning is impractical. Considering the success of machine learning in many disciplines over recent years, it is a promising tool whose suitability for qubit tuning is explored here. We first implement a two-stage device characterisation and dot-tuning process by automating well-established, manual tuning procedures, where the experimenter's decisions is replaced by supervised machine learning. A demonstration on several previously unmeasured devices shows that such an approach is sufficient to tune devices autonomously and without pre-measured input. Next, we address charge-state detection based on charge stability diagrams, an essential component of automated tuning. Beside our use of simple binary classifiers trained on experimental data, deep learning models trained on synthetic data have been previously used to perform this task. While the use of synthetic data has the advantage of providing the large training datasets required for deep learning without the need to hand label experimental data, it also trains classifiers on data coming from different distributions than the data encountered during tuning. We evaluate the classification accuracy of a range of machine learning models trained on simulated and experimental data, assessing their ability to generalise to experimental charge stability diagrams. We find that experimental training data as well as realistic quantum dot simulations and noise models are essential in charge state detection using supervised machine learning. Furthermore, we review other existing approaches to both coarse and fine tuning of gate-defined quantum dots. These efforts show that several machine learning and image processing methods can be used to define and fine tune quantum dots, however difficulties due to material defects, impurities and fabrication variances remain.