The differences between T1 and T2 in real-world quantum computing platforms underscore the importance of studying the thresholds of quantum error-correcting codes under biased noise, also spurring active searches for error-correcting codes with thresholds exceeding the hashing bound under biased noise. Recently, new error-correcting codes such as the XZZX code and the holographic seven-qubit tailored code have exhibited a 50% threshold under pure Pauli noise. Notably, the XZZX code achieves a threshold exceeding the hashing bound in cases of high bias.

This work reports on a holographic quantum error-correcting code, the HaPPY code, which also exhibits a 50% threshold under pure Pauli noise and surpasses the hashing bound threshold under high biased noise. Additionally, this work also explores the threshold of the holographic Steane code under biased noise for comparison.

In addition to studying thresholds under biased noise, this work also investigates the thresholds of various codes, including the Hyper-Invariant Tensor-Network code (HTN code), holographic Reed-Muller code, and some heterogeneous holographic codes, under quantum erasure channels and depolarizing channels.

This work has developed an automated quantum tensor network operator push program, which supports the automated generation of stabilizers and complete logical operators for tensor network quantum error-correcting codes. This greatly enhances the research efficiency of holographic codes, and the program is now ready to be made available to the open-source community.

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.

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.

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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.

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.