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Senast publicerade

A Comparison of Quantum Gate Optimization Techniques
(2026) Lindgren, Pontus
Better quantum gates are likely key to enabling fault-tolerant, useful quantum computers. This thesis compares gate optimization techniques by simulating single-qubit and two-qubit gates for superconducting qubits. The primary focus is deep reinforcement learning. For single-qubit gates, the task is to optimize a π-pulse, while for two-qubit gates, the task is to optimize the Controlled-Z gate. The results indicate that using an ansatz for the gate’s pulse shape can enhance the performance of deep reinforcement learning, both for single-qubit and two-qubit gates, but only significantly for single-qubit gates. A simple square-pulse ansatz approximately halves the simulation time needed to reach the coherence limit for the single-qubit gate studied. The speed-up in simulation should translate to a speed-up in experiments as well. The thesis does not find evidence that the implemented deep reinforcement learning algorithm yields better quantum gates than a state-of-the-art black-box optimizer, despite the black-box optimizer being easier to implement experimentally. For a quantum gate defined by piece-wise constant controls, a low-pass filter seems to enhance the performance, at least if the filter is considered when optimizing. This indicates that piece-wise constant controls, for example, generated with deep reinforcement learning, are not hindered by the limited bandwidth of control electronics. Finally, the study highlights the importance of ZZ coupling to understanding Controlled-Z gates.
Data Driven Turbine Control in Tidal Power Generation: Development and comparison of data driven turbine control algorithms in a simulated underwater kite environment
(2026) Burenius, Hampus; Mårdberg, Rasmus
Minesto’s underwater kite extracts energy from ocean and tidal flows through a cross-flow motion and aims to convert as much energy as possible from the surrounding water-flow. To achieve this, designing a generator control signal that applies optimal torque on the turbine shaft is essential. This project aims to develop data-driven control systems for the kites generator that increases the power output relative to a baseline. ODE-based dynamical models of the kite and generator are derived to create a full simulation environment. The simulation is used to generate sensor data, as well as test and validate new control strategies. Two different approaches are investigated, a supervised predictive controller and a deep reinforcement learning controller where the control signal is generated by the learned policy. The supervised predictive controller approach involves creating a supervised dataset from the simulation and training a recurrent neural network to forecast future inflow water velocities. The knowledge of future inflow is then incorporated in the design of two new control methods, one that is built on the existing controller and another that is developed as a standalone control method. The deep reinforcement learning controller instead utilizes the simulation directly by iteratively stepping through the simulated environment and learning an optimal controller from the outcomes. This is achieved through the use of the state-of-the-art deep reinforcement learning algorithm Soft Actor-Critic. The solution also incorporates pre-training on generated data to validate a possible simulation-to-reality adaptation. Within the adopted simulation setting, the main finding is that predictive turbine control based on forecasted inflow can outperform a reactive baseline controller. The recurrent predictive controllers consistently improved generated power across unseen evaluation environments, indicating that short-term prediction and the periodicity of the kite motion are useful for control. The Soft Actor-Critic approach demonstrated learning capability but was more sensitive to reward design, partial observability, and tuning. Because the study relies on a simplified simulation model, the results should be interpreted as proof-of-concept and comparative evidence rather than as direct claims about real-system performance.
Accelerating Operational Excellence through Industry 4.0 Technologies
(2026) Mårdh, Albin; Malmqvist, Jonathan
While Industry 4.0 (I4.0) primarily focuses on technological advancements, Operational Excellence (OpEx) harnesses human potential. However, a critical challenge arises from the lack of integration between the two, posing a barrier to realizing the full potential of I4.0. This thesis aims to explore how to integrate I4.0 with OpEx effectively—referred to as OpEx 4.0—and to create a framework capable of measuring the maturity level of OpEx 4.0 across various organizations. Utilizing an iterative process, the framework was developed in seven steps through a multi-method approach: a thematic analysis of three existing frameworks, a semistructured literature review, and ten case studies using a Structured-case research cycle. The framework was evaluated through survey responses and maturity level analysis from six assessments across diverse organizations. Findings indicate the framework’s user-friendliness, efficiency, and reliability in generating actionable insights applicable to organizations of varying sizes, industries, and maturity levels. It enables the creation of tailored road-maps and strategic positioning relative to industry benchmarks within a quick, three-hour format per company visit, showcasing its practical applicability. Subsequently, the results informed the identification of primary challenges, opportunities, and ambitions within the Nordic manufacturing industry. The findings indicate an average OpEx 4.0 maturity rating of 2.8 out of 5, highlighting areas for improvement. Analysis reveals that companies in this sector must focus on enhancing structured improvement processes, technology utilization, comprehensive understanding of all organizational dimensions, and active employee engagement in OpEx 4.0 principles. While most organizations acknowledge the potential for improvement, there appears to be a hesitancy to actively pursue the highest maturity level of OpEx 4.0 among the vast majority of organizations
GPU-accelerated Optical Sensor Simulation - Simulating a Network of Optical Sensors Utilizing GPU-acceleration
(2026) Forsberg, Joar
Laser triangulation sensors are widely used in industrial measurement systems, where multiple sensors continuously acquire geometric data and transmit it to a host for calibration and analysis. Prototyping such systems is costly and time-consuming, as physical sensors require specialized hardware, precise alignment, and dedicated network infrastructure. This thesis presents a proof-of-concept framework for virtual laser triangulation sensors that can be used as a substitute for physical prototypes during system development and testing. The work consists of two main components: a mathematical simulation for generating sensor-like measurements, and a network layer that enables the virtual sensors to communicate with the host software exactly as real devices do. The simulation computes raypolygon intersections to emulate the measurement process of a triangulation sensor. A naïve CPU version and a GPU-accelerated version were implemented, followed by a custom CUDA kernel based on Cramers rule for solving large batches of independent 2 × 2 systems. Profiling and roofline analysis show that the custom kernel achieves several orders of magnitude higher performance compared to both the CPU implementation and high-level GPU libraries such as cuSOLVER. The network interface is implemented using UDP communication and a virtual Wire- Guard network, allowing each virtual sensor to appear indistinguishable from a physical one to the existing configuration software. This enables seamless hardware-in-the-loop style testing without modifications to the host system. The results demonstrate that virtual laser triangulation sensors can generate realistic measurements at rates significantly higher than required for real-time operation, creating room for future improvements in physical accuracy and noise modeling. The framework establishes a foundation for scalable virtual prototyping of optical measurement systems and shows that highly specialized GPU kernels can dramatically accelerate small-matrix computations commonly found in geometric simulation workloads.
Physics-Informed Two-Stage Learning Framework for Engine Ignition Frequency and RPM Estimation
(2026) KIm, Nuree
Accurate estimation of the ignition frequency (f0) of internal combustion engines is essential for non-invasive rotational speed (RPM) monitoring and condition diagnosis. In practice, domain shifts caused by sensor placement, vehicle-specific resonances, and environmental noise can distort the harmonic structure of f0, leading to multiple plausible spectral peaks within short analysis windows. A physics-informed, two-stage machine-learning framework is developed to estimate the ignition frequency of four-stroke engines using synchronized sound and vibration measurements. A nonlinear product signal (xprod(t) = xsound(t) xvib(t)) is introduced to emphasize ignition events jointly detected by both sensors, providing an additional representation for joint analysis of ignition-related components. Features are extracted from multiple signal representations, including the FFT, Envelope FFT, Cepstrum, Autocorrelation (ACF), and Envelope–ACF, and combined into a unified feature space capturing harmonic consistency, periodicity, and peak morphology. The framework consists of two learning stages. Stage 1 classifies global engine characteristics, including the cylinder count and ignition-frequency class. Stage 2 ranks local frequency candidates using a LightGBM-based LambdaMART ranker to identify the ignition frequency f0. Generalization performance is evaluated using Leave-One-Source-Out (LOSO) and Leave- One-Vehicle-Out (LOVO) protocols. Results show a mean LOSO accuracy of 95.5% for cylinder classification and a Top-1 accuracy of 84% within a ±5 Hz tolerance for candidate ranking, with mean frequency errors below 2 Hz (≈60 rpm). The model demonstrates consistent generalization behavior on unseen vehicles, indicating robustness to domain shifts across sensors and operating conditions. The proposed approach therefore provides an interpretable and practically applicable solution for non-invasive RPM estimation and establishes a foundation for real-time diagnostic applications on embedded automotive systems.