Gate-defined semiconductor quantum dots require an appropriate number of electrons to function as qubits. The number of electrons is usually tuned by analyzing charge stability diagrams, in which charge transitions manifest as edges. Therefore, to fully automate qubit tuning, it is necessary to recognize these edges automatically and reliably. This paper investigates possible detection methods, describes their training with simulated data from the SimCATS framework, and performs a quantitative comparison with a future hardware implementation in mind. Furthermore, we investigated the quality of the optimized approaches on experimentally measured data from a GaAs and a SiGe qubit sample.
Gate-defined semiconductor quantum dots require an appropriate number of electrons to function as qubits. The number of electrons is usually tuned by analyzing charge stability diagrams, in which charge transitions manifest as edges. Therefore, to fully automate qubit tuning, it is necessary to recognize these edges automatically and reliably. This paper investigates possible detection methods, describes their training with simulated data from the SimCATS framework, and performs a quantitative comparison with a future hardware implementation in mind. Furthermore, we investigated the quality of the optimized approaches on experimentally measured data from a GaAs and a SiGe qubit sample.
Reliable detection of charge transitions in charge stability diagrams (CSDs) is a key requirement for the full automation of quantum dot device control. Performing this task directly at the cryogenic stage reduces data transfer and supports scalability. To provide the large labeled datasets required for developing and evaluating detection methods, we introduced SimCATS [1], a simulator that generates realistic CSDs including sensor responses and distortions. We optimize both traditional and machine-learning-based detection methods using simulated data and benchmark them on simulated and experimental measurements from GaAs and SiGe qubit devices. We also investigate the potential of model compression and find its performance closely tied to task complexity, which can be alleviated by sensor dot compensation. In fact, we find that sensor compensation allows machine-learning approaches to be reduced in size by up to two orders of magnitude while maintaining, or even improving, detection quality. Together with high-quality measurements, this enables robust and scalable (ray-based) charge transition detection. Finally, we estimate the cryogenic power budget for applying this approach to large-scale systems with up to one million qubits. [1] F. Hader et al., "Simulation of Charge Stability Diagrams for Automated Tuning Solutions (SimCATS)", IEEE Transactions on Quantum Engineering, DOI: 10.1109/TQE.2024.3445967 (2024).
Quantum dots (QDs) must be tuned precisely to provide a suitable basis for quantum computation. A scalable platform for quantum computing can only be achieved by fully automating the tuning process. One crucial step is to trap the appropriate number of electrons in the QDs, typically accomplished by analyzing charge stability diagrams (CSDs). Training and testing automation algorithms require large amounts of data, which can be either measured and manually labeled in an experiment or simulated. This article introduces a new approach to the realistic simulation of such measurements. Our flexible framework enables the simulation of ideal CSD data complemented with appropriate sensor responses and distortions. We suggest using this simulation to benchmark published algorithms. Also, we encourage the extension by custom models and parameter sets to drive the development of robust technology-independent algorithms.
To realize practical quantum computers, a large number of quantum bits (qubits) will be required. Semiconductor spin qubits offer advantages such as high scalability and compatibility with existing semiconductor technologies. However, as the number of qubits increases, manual qubit tuning becomes infeasible, motivating automated tuning approaches. In this study, we use U-Net, a neural network method for object detection, to identify charge transition lines in experimental charge stability diagrams. The extracted charge transition lines are analyzed using the Hough transform to determine their positions and angles. Based on this analysis, we obtain the transformation matrix to virtual gates. Furthermore, we identify the single-electron regime by clustering the Hough transform outputs. We also show the single-electron regime within the virtual gate space. These sequential processes are performed automatically. This approach will advance automated control technologies for large-scale quantum devices.
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 iii Suominen, these two dragged me out of the lab on a regular basis and made sure all doubts and misery were drowned in bubbles.Their input on how to navigate the first period of new beginnings and changes was priceless.Beside the usual PhD challenges and as a result of living in two places at one, my adventure also included some overhead paperwork.Overwhelmed by it at times, it was more than reassuring to have a friend like Mario Könz.He has always been keen on sharing some of his abundant knowledge, be it in programming or in how to deal with Swiss authorities.My endeavour would have failed without his readiness to help me out on the Swiss paperwork front.Similarly, Anïs Mairena, whose presence already got me through my Master's in Lausanne, also saved me from a lot of admit headache.I am also immensely grateful for crossing the paths of John Hornibrook and Xanthe Croot.As the kindest and most generous people I have ever met, these two made me feel at home far away at home.I will always remember those 30km bush walks with Xanthe, during which I not only got to know some of the QNL lab rats better, but was also able to recharge on positivity, happiness and sunshine.Back in the lab, it was John who patiently listened to my complains and answered all my dumb questions.From my very first steps in python and QCoDeS to the implementation of my tuning package, I had the privilege to be able to count on William H. P Nielsen and Jens H. Nielsen for support.They not only helped me get through writing drivers for instruments I didn't how to use, but their patience, comprehensive feedback and technical expertise made me the programmer I am today.Despite the rather atypical working arrangement, I was blessed to have someone who could comprehend my situation: Natalie Pearson.I was fortunate not to be the only one packing my bags for Copenhagen early in their PhD.We embarked on the Danish journey together and were able to support each other in a new country.In German one would say "geteiltes Leid ist halbes Leid".Furthermore, I would like to mention those who always had a time for a spontaneous chat, ultimately getting me through those days that didn't seem to end:
Semiconductor quantum computing leverages quantum dots as qubits, where achieving high qubit fidelity is one essential requirement for scalability. The formation of qubits and associated sensor dots necessitates meticulous iterative voltage adjustments, a tuning process that is ordinarily executed manually by researchers. While various automated approaches have been proposed, only a few address scalability in the context of a system with a million qubits or consider shifting key data analysis tasks into the cryostat. The latter is necessary to alleviate wiring constraints and to minimize data transmission to room temperature electronics. This thesis explores scalable solutions for automated in-cryostat data analysis, identifying charge transition detection as a pivotal task. A simulation model is developed to generate realistic charge stability diagrams with known ground truth. The model’s output is benchmarked against experimental data to verify fidelity and diversity. Utilizing this model, a comprehensive evaluation of charge transition detection methods is conducted, comparing classical algorithms and a range of machine learning approaches. In consideration of energy-efficient integration, this study includes an initial investigation into reducing the complexity of convolutional neural networks. Subsequent to their optimization using simulated data, the approaches are benchmarked on both simulated and experimental data from diverse qubit samples. When applied to contemporary data qualities, evidence is presented that classical approaches are inadequate in terms of robustness and accuracy while numerous machine learning models demonstrate compelling performance. Among the latter, convolutional neural networks like U-Net are identified to be particularly eligible due to their advantageous structure for efficient hardware integration. Although the U-Net architecture already demonstrates a strong potential for network size reduction under current data quality constraints, a cryogenic hardware integration remains a significant challenge. Consequently, a subsequent study explores if enhanced sensor dot tuning and sensor compensation reduce the complexity of charge transition analysis and if they enable the utilization of ray-based detection methodologies. The findings indicate that particularly sensor compensation has a substantial impact on the data quality and allows a streamlining of the analytical process. However, achieving robust ray-based analysis remains feasible only if consistently reaching the highest observed measurement quality and utilizing machine learning. Even in this case, classical methods and a proposed averaging and thresholding circuit fall short of the requisite detection accuracy. Only with further improvements toward optimal data quality and additional enhancements to the proposed circuit, an integrated solution without machine learning appears attainable. Finally, the energy efficiency of scaled-down neural networks is assessed using state-of-the art hardware accelerators. The findings suggest that the cryogenic integration of machine learning-based charge transition detection is feasible, thus propelling the development of scalable and energy-efficient quantum control architectures.
Semiconductor quantum computing leverages quantum dots as qubits, where achieving high qubit fidelity is one essential requirement for scalability. The formation of qubits and associated sensor dots necessitates meticulous iterative voltage adjustments, a tuning process that is ordinarily executed manually by researchers. While various automated approaches have been proposed, only a few address scalability in the context of a system with a million qubits or consider shifting key data analysis tasks into the cryostat. The latter is necessary to alleviate wiring constraints and to minimize data transmission to room temperature electronics. This thesis explores scalable solutions for automated in-cryostat data analysis, identifying charge transition detection as a pivotal task. A simulation model is developed to generate realistic charge stability diagrams with known ground truth. The model’s output is benchmarked against experimental data to verify fidelity and diversity. Utilizing this model, a comprehensive evaluation of charge transition detection methods is conducted, comparing classical algorithms and a range of machine learning approaches. In consideration of energy-efficient integration, this study includes an initial investigation into reducing the complexity of convolutional neural networks. Subsequent to their optimization using simulated data, the approaches are benchmarked on both simulated and experimental data from diverse qubit samples. When applied to contemporary data qualities, evidence is presented that classical approaches are inadequate in terms of robustness and accuracy while numerous machine learning models demonstrate compelling performance. Among the latter, convolutional neural networks like U-Net are identified to be particularly eligible due to their advantageous structure for efficient hardware integration. Although the U-Net architecture already demonstrates a strong potential for network size reduction under current data quality constraints, a cryogenic hardware integration remains a significant challenge. Consequently, a subsequent study explores if enhanced sensor dot tuning and sensor compensation reduce the complexity of charge transition analysis and if they enable the utilization of ray-based detection methodologies. The findings indicate that particularly sensor compensation has a substantial impact on the data quality and allows a streamlining of the analytical process. However, achieving robust ray-based analysis remains feasible only if consistently reaching the highest observed measurement quality and utilizing machine learning. Even in this case, classical methods and a proposed averaging and thresholding circuit fall short of the requisite detection accuracy. Only with further improvements toward optimal data quality and additional enhancements to the proposed circuit, an integrated solution without machine learning appears attainable. Finally, the energy efficiency of scaled-down neural networks is assessed using state-of-the art hardware accelerators. The findings suggest that the cryogenic integration of machine learning-based charge transition detection is feasible, thus propelling the development of scalable and energy-efficient quantum control architectures.