The development of energy-efficient and fast machine learning methods plays an increasingly important role in experimental physics, where data analysis and control tasks often need to operate under strict resource constraints. In these contexts, machine learning models can automate complex calibration and analysis tasks while enabling on-device data processing close to the experimental sensors.One representative application presented on this poster concerns the automated calibration of semiconductor spin qubits, while the outlook highlights extensions toward edge-computing approaches in detector systems. The automated calibration of quantum dots is a key prerequisite for realizing scalable quantum computers. In particular, the analysis of charge stability diagrams, used to detect charge transitions in quantum dots, represents a complex and time-consuming task. Neural networks, especially U-Net architectures, offer the potential to automate this process by reliably recognizing relevant patterns in simulated and experimental measurement data. State-of-the-art networks have already been successfully trained for this purpose. However, there remains significant potential for optimization to enable space- and energy-efficient integration close to the quantum bits within the cryostat. We have investigated the use of quantized neural networks for energy-efficient quantum dot calibration. The goal is to analyze the impact of post-training quantization and quantization-aware training on detection quality, as well as the general effects of quantization on memory requirements and inference speed. Three U-Nets with different architectures, parameter counts, and input dimensions serve as model bases, applied to simulated charge stability diagrams. The results show that appropriate quantization strategies can reduce memory usage without significantly affecting detection quality. The findings of this work contribute to the integration of energy-efficient machine learning methods into experimental quantum computing environments, thereby supporting overall scalability. Building on these results, the approach is extended toward the use of binarized neural networks (BNNs) to push energy efficiency and faster inference even further. Within edge computing applications, current efforts focus on implementing and demonstrating such networks on FPGA hardware, aiming to exploit binary-weight computation and hardware-level parallelism for minimal latency and power consumption. Beyond quantum dot calibration, these methods are also being investigated for other scientific applications, such as the autonomous self-triggering radio detection of extensive air showers, highlighting the broader potential of hardware-embedded AI for resource-constrained experimental environments.
The development of energy-efficient and fast machine learning methods plays an increasingly important role in experimental physics, where data analysis and control tasks often need to operate under strict resource constraints. In these contexts, machine learning models can automate complex calibration and analysis tasks while enabling on-device data processing close to the experimental sensors.One representative application presented on this poster concerns the automated calibration of semiconductor spin qubits, while the outlook highlights extensions toward edge-computing approaches in detector systems. The automated calibration of quantum dots is a key prerequisite for realizing scalable quantum computers. In particular, the analysis of charge stability diagrams, used to detect charge transitions in quantum dots, represents a complex and time-consuming task. Neural networks, especially U-Net architectures, offer the potential to automate this process by reliably recognizing relevant patterns in simulated and experimental measurement data. State-of-the-art networks have already been successfully trained for this purpose. However, there remains significant potential for optimization to enable space- and energy-efficient integration close to the quantum bits within the cryostat. We have investigated the use of quantized neural networks for energy-efficient quantum dot calibration. The goal is to analyze the impact of post-training quantization and quantization-aware training on detection quality, as well as the general effects of quantization on memory requirements and inference speed. Three U-Nets with different architectures, parameter counts, and input dimensions serve as model bases, applied to simulated charge stability diagrams. The results show that appropriate quantization strategies can reduce memory usage without significantly affecting detection quality. The findings of this work contribute to the integration of energy-efficient machine learning methods into experimental quantum computing environments, thereby supporting overall scalability. Building on these results, the approach is extended toward the use of binarized neural networks (BNNs) to push energy efficiency and faster inference even further. Within edge computing applications, current efforts focus on implementing and demonstrating such networks on FPGA hardware, aiming to exploit binary-weight computation and hardware-level parallelism for minimal latency and power consumption. Beyond quantum dot calibration, these methods are also being investigated for other scientific applications, such as the autonomous self-triggering radio detection of extensive air showers, highlighting the broader potential of hardware-embedded AI for resource-constrained experimental environments.
The development of energy-efficient and fast machine learning methods plays an increasingly important role in experimental physics, where data analysis and control tasks often need to operate under strict resource constraints. In these contexts, machine learning models can automate complex calibration and analysis tasks while enabling on-device data processing close to the experimental sensors.One representative application presented on this poster concerns the automated calibration of semiconductor spin qubits, while the outlook highlights extensions toward edge-computing approaches in detector systems.The automated calibration of quantum dots is a key prerequisite for realizing scalable quantum computers. In particular, the analysis of charge stability diagrams, used to detect charge transitions in quantum dots, represents a complex and time-consuming task. Neural networks, especially U-Net architectures, offer the potential to automate this process by reliably recognizing relevant patterns in simulated and experimental measurement data. State-of-the-art networks have already been successfully trained for this purpose.However, there remains significant potential for optimization to enable space- and energy-efficient integration close to the quantum bits within the cryostat.We have investigated the use of quantized neural networks for energy-efficient quantum dot calibration. The goal is to analyze the impact of post-training quantization and quantization-aware training on detection quality, as well as the general effects of quantization on memory requirements and inference speed. Three U-Nets with different architectures, parameter counts, and input dimensions serve as model bases, applied to simulated charge stability diagrams. The results show that appropriate quantization strategies can reduce memory usage without significantly affecting detection quality.The findings of this work contribute to the integration of energy-efficient machine learning methods into experimental quantum computing environments, thereby supporting overall scalability.Building on these results, the approach is extended toward the use of binarized neural networks (BNNs) to push energy efficiency and faster inference even further. Within edge computing applications, current efforts focus on implementing and demonstrating such networks on FPGA hardware, aiming to exploit binary-weight computation and hardware-level parallelism for minimal latency and power consumption. Beyond quantum dot calibration, these methods are also being investigated for other scientific applications, such as the autonomous self-triggering radio detection of extensive air showers, highlighting the broader potential of hardware-embedded AI for resource-constrained experimental environments.
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.
Abstract A key challenge in scaling quantum computers is the calibration and control of multiple qubits. In solid-state quantum dots (QDs), the gate voltages required to stabilize quantized charges are unique for each individual qubit, resulting in a high-dimensional control parameter space that must be tuned automatically. Machine learning techniques are capable of processing high-dimensional data—provided that an appropriate training set is available—and have been successfully used for autotuning in the past. In this paper, we develop extremely small feed-forward neural networks that can be used to detect charge-state transitions in QD stability diagrams. We demonstrate that these neural networks can be trained on synthetic data produced by computer simulations, and robustly transferred to the task of tuning an experimental device into a desired charge state. The neural networks required for this task are sufficiently small as to enable an implementation in existing memristor crossbar arrays in the near future. This opens up the possibility of miniaturizing powerful control elements on low-power hardware, a significant step towards on-chip autotuning in future QD computers.
Spin-based semiconductor qubits hold promise for scalable quantum computing, yet they require reliable autonomous calibration procedures. This study presents an experimental demonstration of online single-dot charge autotuning using a convolutional neural network integrated into a closed-loop calibration system. The autotuning algorithm explores the gates' voltage space to localize charge transition lines, thereby isolating the one-electron regime without human intervention. This exploration leverages the model's uncertainty estimation to find the appropriate gate configuration with minimal measurements while reducing the risk of failures. In 20 experimental runs, our method achieved a success rate of 95% in locating the target electron regime, highlighting the robustness of this approach against noise and distribution shifts from the offline training set. Each tuning run lasted an average of 2 h and 9 min, primarily due to the limited speed of the current measurement. This work validates the feasibility of machine-learning-driven real-time charge autotuning for quantum dot devices, advancing the development toward the control of large qubit arrays.
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.