Semiconducting spin quantum devices present a promising avenue for advancing noisy intermediate-scale quantum (NISQ) computers. However, the complexities involved in their tuning and characterisation currently hinder their scaling. This thesis addresses these challenges by harnessing machine-learning-based approaches. Firstly, I used Gaussian processes to develop a fully automatic tuning algorithm that exclusively used radio-frequency measurements taken on millisecond timescales. Secondly, I employed Bayesian optimisation for charge sensor optimisation and automation. Both algorithms were deployed on depletion mode hole-based Ge/SiGe devices; however, they could easily be applied to other architectures. These automated approaches considerably outperformed manual tuning and represent vital building blocks for algorithms to tune larger quantum dot arrays. Next, I developed algorithms to find the ground state charge configuration in the constant capacitance model, which was orders of magnitude faster than its predecessors, permitting the simulation of much larger arrays of sixteen dots. This open-source package can be used to gain insights into the charge stability diagrams of large quantum dot arrays and facilitates further tuning algorithm development. Lastly, I present a novel application of hidden Markov models to identify and separate the state preparation, measurement and spin-flip errors presented by a pair of hot Loss Divincenzo qubits in a Si-MOS device. This approach not only unravels these error sources but also sheds light on their relationship with various parameters, such as electron temperature, thus contributing a vital understanding to the field of quantum computing.
Semiconducting spin quantum devices present a promising avenue for advancing noisy intermediate-scale quantum (NISQ) computers. However, the complexities involved in their tuning and characterisation currently hinder their scaling. This thesis addresses these challenges by harnessing machine-learning-based approaches. Firstly, I used Gaussian processes to develop a fully automatic tuning algorithm that exclusively used radio-frequency measurements taken on millisecond timescales. Secondly, I employed Bayesian optimisation for charge sensor optimisation and automation. Both algorithms were deployed on depletion mode hole-based Ge/SiGe devices; however, they could easily be applied to other architectures. These automated approaches considerably outperformed manual tuning and represent vital building blocks for algorithms to tune larger quantum dot arrays. Next, I developed algorithms to find the ground state charge configuration in the constant capacitance model, which was orders of magnitude faster than its predecessors, permitting the simulation of much larger arrays of sixteen dots. This open-source package can be used to gain insights into the charge stability diagrams of large quantum dot arrays and facilitates further tuning algorithm development. Lastly, I present a novel application of hidden Markov models to identify and separate the state preparation, measurement and spin-flip errors presented by a pair of hot Loss Divincenzo qubits in a Si-MOS device. This approach not only unravels these error sources but also sheds light on their relationship with various parameters, such as electron temperature, thus contributing a vital understanding to the field of quantum computing.
Spanning over two decades, the study of qubits in semiconductors for quantum computing has yielded significant breakthroughs. However, the development of large-scale semiconductor quantum circuits is still limited by challenges in efficiently tuning and operating these circuits. Identifying optimal operating conditions for these qubits is complex, involving the exploration of vast parameter spaces. This presents a real ‘needle in the haystack’ problem, which, until now, has resisted complete automation due to device variability, fabrication imperfections, and limited data availability. In this thesis, I first present a machine learning algorithm capable of automatically identifying Pauli spin blockade (PSB) using charge transport measurements. PSB can be employed as a resource for spin qubit initialisation and readout but it can be difficult to identify. The scarcity of PSB data is circumvented by training the algorithm with simulated data and by using cross-device validation. I demonstrate the approach on a silicon field-effect transistor device and report a high accuracy on test devices, giving evidence that the approach is robust to device variability. Then, I present the first fully autonomous tuning of a semiconductor qubit, from a grounded device to Rabi oscillations, a clear indication of successful qubit operation. This automation, achieved without human intervention, is demonstrated in a Ge/Si core/shell nanowire device and integrates deep learning, Bayesian optimisation, and computer vision techniques. Further, I demonstrate the potential of full automation by characterising how the Rabi frequency and g-factor depend on barrier gate voltages for qubits in four different charge transitions. The data reveals that certain features in charge transport measurements can enhance automated tuning processes by recognising the conditions for successful readout. Finally, I present the autonomous optimisation of two qubits for maximal Rabi frequency. This optimisation extends the tuning algorithm, enabling it to enhance the performance of a qubit. I hope the mass tuning and characterisation of qubits, enabled by the methods in this thesis, will create a productive feedback loop between measurement and fabrication processes. Wafer-scale, high-throughput characterisation of quantum devices can mitigate device variability and contribute to their scalability. The continued evolution of these methods will support the growing demands of the field, ultimately – and hopefully – leading to practical and powerful quantum computers.
Abstract Recent progress in building large-scale quantum devices for exploring quantum computing and simulation has relied upon effective tools for achieving and maintaining good experimental parameters, i.e., tuning up devices. In many cases, including quantum dot-based architectures, the parameter space grows substantially with the number of qubits, and may become a limit to scalability. Fortunately, machine learning techniques for pattern recognition and image classification, using so-called deep neural networks, have shown surprising successes for computer-aided understanding of complex systems. We propose a new paradigm for fully automated experimental initialization through a closed-loop system relying on machine learning and optimization techniques. We use deep convolutional neural networks to characterize states and charge configurations of semiconductor quantum dot arrays when only measurements of a current−voltage characteristic of transport are available. For simplicity, we model a semiconductor nanowire connected to leads and capacitively coupled to depletion gates using the Thomas−Fermi approximation and Coulomb blockade physics. We then generate labeled training data for the neural networks, and find at least 90 % accuracy for charge and state identification for single and double dots. Using these characterization networks, we can then optimize the parameter space to achieve a desired configuration of the array, a technique we call “auto-tuning”. Finally, we show how such techniques can be implemented in an experimental setting by applying our approach to an experimental dataset, and outline further problems in this domain, from using charge sensing data to extensions to full one- and two-dimensional arrays, that can be tackled with machine learning.
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:
Donor spin qubits in silicon offer one- and two-qubit gates with fidelities beyond 99%, coherence times exceeding 30 seconds, and compatibility with industrial manufacturing methods. This motivates the development of large-scale quantum processors using this platform, and the ability to automatically tune and operate such complex devices. In this work, we present the first machine learning algorithm with the ability to automatically locate the charge transitions of an ion-implanted donor in a silicon device, tune single-shot charge readout, and identify the gate voltage parameters where tunnelling rates in and out the donor site are the same. The entire tuning pipeline is completed on the order of minutes. Our results enable both automatic characterisation and tuning of a donor in silicon devices faster than human experts.
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.