Throughout the work, the goal was to develop the physical layer of a quantum network stack. The layer of the network stack connected to the physical layer is the link layer. For entanglement-based quantum networks, we demonstrated the successful operation of a link layer and a physical layer. The physical layer’s entanglement generation procedure—implemented here using two NV center-based quantum network nodes—is abstracted by the link layer into a robust platform-independent service that can be utilized to run quantum networking applications. Other quantum network platforms using the approaches provided here (which are not unique to our diamond devices) will accelerate the development of large-scale and heterogeneous quantum networks. ---------------- A vision is developed on how successful university-industry collaborations can be established in the field of quantum technology development. By looking at the stakeholders, purpose and type of collaboration, and influencing factors, a three-step model is developed to move from awareness to acceptance to collaboration. By overcoming the existing barriers, university-industry collaborations can be successfully realized.

Scientific computing and applied mathematics enable the exploration of and, sometimes even, the simplification of complex systems through various optimized modeling and simulation methods. These fields create and utilize computational resources to do so. Many problems, however, are still unsolvable or very difficult to solve with current well-established methods – be it algorithms or computers. Quantum computers were theorized and are now being realized to extend computational abilities. Though commercially available and widely researched, quantum computers today are still more proof-of-concept than ”universally” applicable tools. A major part of the problem is these systems are riddled with noise, hence the phrase noisy intermediate scale quantum (NISQ) devices is commonly used to refer to current day devices. There are many approaches to mitigating the effects of noise in quantum systems from both hardware and software perspectives. This work focuses on optimizing the device hardware by combining aspects of two already well-explored systems – superconducting quantum integrated circuits and topological insulators. The former are relatively easy to fabricate and have become one of the main focuses of today’s industry. The heart of superconducting quantum circuits is found in two circuit elements: the quantum bit (qubit) and the resonator. The qubit manages quantum information processing. The resonator is a simple inductor and capacitor (LC) in parallel and is a well understood classical circuit element. Resonators play a key role in this research. In contrast, topological quantum systems are incredibly difficult to fabricate. In theory, however, topological insulators promise a strong shield against noise across the device. By leveraging the simplicity and ease of simulating classical LC resonators and combining this with the noise-protection introduced in topological systems, this project lays the groundwork for developing a model supporting how such a hybrid hardware could process and protect quantum information. This thesis presents the characterization of how a two-dimensional array of LC resonators could behave similarly to a qubit when demonstrating edge modes, which are typically found in topological quantum systems. An approach for how to model such systems is proposed from the perspective of architecture, optimal control, and metrics.

For NP-hard optimisation problems no polynomial-time algorithms exist for finding a solution. Therefore, heuristic methods are often used, especially when approximate solutions can be satisfactory. One such method is quantum annealing, a method where some initial Hamiltonian is slowly perturbed to anneal towards a problem Hamiltonian. The annealing schedule should follow the adiabatic theorem of quantum mechanics and should therefore be slow to yield the most accurate results. Finding a schedule that is both fast and still accurate has been referred to as a 'black art' and is usually just guessed. In this thesis reinforcement learning (RL) is explored to see if it can help with finding the optimal quantum annealing (QA) schedules. The Hamiltonians used are of the form of a transverse field Ising model. These are well studied Hamiltonians that can map to many NP-complete problems [1]. Finding a good balance between going both fast and being precise proved to be difficult and needs further research. Therefore this thesis mainly dealt with training an RL agent to find the most accurate QA schedule. The results show that the agent learns to find the most accurate (slowest) annealing schedule after 300,000 learning steps for a problem Hamiltonian with a single coupling constant J and no reward shaping. Annealing in an environment with a spin glass problem Hamiltonian proved to be difficult and has not shown good results in this thesis This needs further investigation. Nonetheless, the result on the single coupling constant Hamiltonian shows the potential of an RL agent for learning in a quantum annealing environment with very sparse rewards. This paves the way for further research into an RL agent that can find a schedule that is both fast and accurate on a wide range of problem Hamiltonians.

Radiotherapy treatment planning is a complex and time consuming process prone to differences as result of choices of individual planners. Autoplanning systems have been introduced to both reduce the time consumption and to counteract the influence of individual planning choices. Although autoplanning generally increases performance of the treatment plans, the plans still need to be checked manually to ensure plan accuracy. Increasing plan consistency with knowledge based planning (KBP) or automatic plan evaluation would be clear ways to improve upon this problem. For both improvements, deep learning can be an important tool to produce accurate and fast 3D dose distributions. In this study such a deep learning tool is developed in two parts to accommodate this. In the first part a structure based deep learning model, using a U-Net architecture is developed, used for dose distribution prediction for prostate patients treated with volumetric modulated arc therapy (VMAT) plans, generated by autoplanning. Different loss functions have been tested to see which achieve the best results. In the second part physics information is included in the neural network prediction by using a hybrid physics-data approach. For this, an approximate dose distribution, the segment dose, is used as extra input for the dose prediction network. This segment dose is created by predicting multi leaf collimator (MLC) positions and reconstructing the corresponding dose with a simple dose engine. The predictions are evaluated using several dose characteristics, dose volume histogram (DVH) curves and other clinically relevant metrics. The U-Net architecture proved able to accurately predict the dose of patients in the test set. The best performing loss function for accurate dose characteristics prediction was found to be the weighted mean squared error (WMSE) loss, which predicted several PTV DVH statistics within a 1.5 % error and the rectum statistics within 3.5 % error. Furthermore, the model predicts the DVH points of the PTV within 0.84 ± 0.40 Gy average absolute dose difference and the rectum in 0.91 ± 0.72 Gy average dose difference. The hybrid physics-data model did not improve the prediction accuracy of the dose prediction model in terms of DVH statistics and DVH prediction, as the MLC positions could not be predicted accurately enough. The performance of the structure based prediction is similar to the performance of state-of-the-art prediction models. Although the hybrid model with the predicted segment doses did not improve the prediction accuracy, a significant effect could be seen from using the correct MLC positions instead. The bottleneck of the prediction is therefore identified to be the segment prediction. As such, it would be interesting to focus on segment prediction in follow up research.

In this thesis, gate set tomography (GST) has been conducted on the nitrogen vacancy center (NV). Gate set tomography is a protocol for characterization of logic operations (gates) on quantum computing processors. The NV’s electron served as a qubit. The quantum circuits were run both experimentally as well as on an NV-simulator. GST is different from its predecessors in the sense that it estimates all aspects of the processor simultaneously, without assuming any of its parts to be ideal. However, it does assume that the gates are Markovian. This allows to analyse the gate errors more precisely via their error generators. In addition to this, the diamond norm and a measure of the amount of model violation were used to examine the results. The electron qubit can couple to the nearby nitrogen nucleus, which can cause non-Markovian dynamics. The nitrogen nucleus (𝑆 = 1) can be initialised using nuclear spin polarization. The effects of different initialisation procedures on GST’s results were researched. Furthermore, a dynamical decoupling XY-4 echo sequence could be employed to protect the quantum state of the qubit. How the presence of the echo affected the estimated gates and their errors was probed. It was discovered that the echo was extremely good at decreasing the amount of model violation, most likely by preventing the electron from coupling to the nitrogen, which can lead to non-Markovian dynamics. Without the echo, GST’s estimates were satisfactory only if the nitrogen nucleus was initialised with a high enough fidelity, at least 0.95. Another topic for further research would be to perform GST on the electron and the nitrogen system, by modelling the nitrogen nucleus as a qutrit.