Constructing and operating a large-scale, fault-tolerant quantum computer remains one of the most arduous challenges for the field of quantum information science, inasmuch from the theoretical and practical standpoints. Much progress is still required in the development of new, high-rate quantum error correction codes, without which the dream of commercially-viable quantum computing is not achievable. On the other hand, the specific details of implementation with regards to novel quantum codes remain equally as important and challenging. This dissertation presents an extensive practical study for a relatively new class of quantum error correction codes, known as holographic quantum codes. First studied exclusively in the context of toy-model simulations for the famous AdS/CFT correspondence, one of the leading theoretical proposals for the emergence of gravity in the quantum regime, we study the error-correction properties of holographic quantum codes for real-world quantum computing, as well as addressing specific implementation issues.

Firstly, we present a new subclass of holographic quantum codes that has been discovered, which we name Evenbly codes. The particular code construction presented demonstrates many aspects of the gauge-gravity correspondence that previous tensor-network models of holography lacked. Among these are: state-dependent operator reconstruction, hailing from a novel gauge-fixing picture; quantum corrections to the Ryu-Takayanagi formula, which are expected in the finite-𝑁 regime of AdS/CFT; and an analytical derivation of bona fide scaling dimensions from a conformal field theory defined on the boundary of AdS, in agreement with what is known from holographic renormalization group theory. Additionally, the gauge-fixing picture allows for Evenbly codes to be viewed as novel holographic subsystem codes, in which the gauge degree of freedom chosen permits markedly different quantum error correction properties to emerge, ranging from very high thresholds to low-weight transversal logical operations, to distance scaling of logical qubits that bests the most well-studied mainstream quantum error correction codes, such as topological codes. Finally, we show that asymptotically zero-rate versions of these codes not only attain and exceed the zero-rate hashing bound at various bias points, but that several holographic codes supersede the current state-of-the-art record, beating the hashing bound as we move towards the 2-Pauli noise regime.

Secondly, we consider the practical usability of holographic codes for universal quantum computation. Utilizing a novel connection to code concatenation, we construct heterogeneous holographic codes which allow for universal fault-tolerant logic, thus circumventing the Eastin-Knill theorem. We pinpoint the thresholds of these codes under the quantum erasure channel, showing that they exceed the thresholds given by traditional code concatenation, all while conferring significant savings in space overhead. Also, we consider fault-tolerant syndrome extraction for precursor seed codes of holographic codes, showing that, by considering the entire stabilizer group of 2𝑛-𝑘 elements, such syndrome extraction protocols are amenable to large gate reductions if the flag fault tolerance protocol is utilized.

Finally, we investigate engineering-level dilemmas associated with executing quantum algorithms and error-correction codes on real devices. Penultimately, we derive and demonstrate a lower bound for the number of SWAP gates needed to realize an algorithm on a finite-connectivity quantum device, permitting future algorithmic strategies to be fairly compared. This lower bound is derived using insights from quantum information theory, graph theory, and quantum circuit complexity theory. In particular, we show that the use of entropic divergences allows us to lower-bound the number of SWAP gates needed via a relationship with the quantum Fisher information metric. Lastly, we investigate several examples of near-term spin-qubit architectures, and utilize the multipartite maximally-entangled states as benchmark measures, thus aiding in the design of future quantum devices. By utilizing benchmarks known for characterizing multipartite quantum entanglement, we establish a framework for efficiently diagnosing and differentiating architectural connectivity features under realistic noise models and compilation features. Our results include a trade-off evaluation regarding the utility of advanced local connectivity for a spin-qubit device versus the amount of crosstalk present. Our study shows that limitations exist in spin-qubit architectures concerning the relative amount of local connectivity.

At the end of this dissertation, we provide concluding comments, as well as ideas for future directions in the field of holographic quantum error correction. ... Read more

Large-scale construction projects are characterised by uncertainty, multidisciplinary dependencies, and frequent cost and schedule overruns. Structural engineering and project planning increasingly use probabilistic methods, but within separate workflows, so uncertainty information is often lost when results are transferred manually between disciplines. As a result, projects experience fragmented workflows, which negatively impact project performance. To address this, the thesis develops and evaluates an AI-supported decision framework that integrates probabilistic structural reliability analysis with probabilistic project planning.

The framework consists of three components. The first one is a Probabilistic Surrogate Module (PSM), which combines principal component analysis and Gaussian process regression to approximate the structural response of an immersed-tunnel cross-section, achieving a coefficient of determination of 0.991 at approximately three orders of magnitude lower computational cost compared to the reference model. Combined with Monte Carlo simulation, it estimates the probability of failure and reliability index while accounting for aleatoric and epistemic uncertainty. Secondly, a Probabilistic Planning Module (PPM) is integrated into the framework, which is based on the Mitigation Controller software. This module propagates uncertainty in durations, costs, risks, and mitigation measures into the project schedule. A coupling mechanism is used to translate structural reliability information into the occurrence probability of a planning risk. Lastly, a human-controlled AI agent was developed that functions as an orchestration layer between the mathematical modules and the human decision-maker. All three of these components are implemented into an interactive dashboard with which the human decision-maker can freely interact.

The framework was applied to the Fehmarnbelt Tunnel through four validation scenarios and evaluated in sessions with six practitioners. The results demonstrate functional feasibility and professional plausibility. The coupling mechanism, however, is not yet empirically calibrated, and measurable improvements in decision quality have not yet been established.

The main contribution lies in system-level integration of novel methods within an uncertainty-aware, human-controlled decision environment.
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This MSc thesis investigates the potential of two-dimensional trailing edge airfoil morphing to compensate for temporary sectional power losses caused by inflow variations occurring faster than the global controller response of a wind turbine. A quasi-steady inverse-tracking optimization framework is developed for the FFA-W3-241 airfoil at a representative section of the IEA 15 MW reference wind turbine. The airfoil is represented using a cubic B-spline and modified through two coordinated trailing edge morphing modes controlling camber with a flap like deformation and thickness variations while preserving a protected wingbox region. Candidate geometries are evaluated using XFOIL coupled to a single annulus Blade Element Momentum model and optimized using Particle Swarm Optimization. The objective is to reproduce the sectional power of the baseline airfoil operating at the optimal tip speed ratio while the candidate rotor speed remains fixed. An offline database of 36 optimized geometries is generated for inflow velocities from 7.0 to 10.5m\s, staying in region 2 below rated conditions. The optimized airfoils closely track the target power throughout this range, with a maximum deviation of approximately 0.63kW\m. When tested against an OpenFAST, TurbSim turbulent inflow signal, the database increases the mean sectional power by 0.683 relative to the fixed speed baseline and recovers 74.51 of the mean sectional power deficit with respect to the ideal, optimal tip speed ratio response. The results demonstrate the aerodynamic potential of offline airfoil morphing for local power recovery. ... Read more

Across Europe, cities are actively pursuing transitions toward sustainable urban mobility. The cargo bike has emerged as a viable substitute for the (second) car, particularly in dense urban areas, a finding supported by this study, in which 46% of cargo bike owners reported that owning a cargo bike replaced or prevented a car purchase. Dutch municipalities are increasingly seeking to facilitate this modal shift. However, an adoption barrier remains: residents do not purchase a cargo bike due to the absence of secure near-home parking, particularly among those without access to private storage such as a garage. Within this group, 33% indicated that the availability of a suitable parking facility in their neighbourhood would increase their likelihood of considering a cargo bike purchase.
This paper presents the research and development of a Cargo Bike Parking Solution (CBPS), a facility accommodating cargo bikes within the dimensions of a single car parking space. The development process is informed by quantitative survey data (n=465) collected among both current cargo bike owners and non-owners in the Netherlands, as well as by the requirements of multiple stakeholders including municipalities, residents, and production partners. ... Read more

ICAO projects a near-tripling of passenger volumes to 12.4 billion by 2050, yet the resulting bottleneck sits not in the sky but at the runway. Reinforcement learning agents can cut mid-air separation violations by over $99\%$, but treat the airport as a single infinite-capacity sink: traffic concentrates on one runway while others idle, a failure mode termed Runway Overload. This work closes that gap with a Goal-Conditioned Reinforcement Learning hierarchical framework coupling spatial and temporal arrival management. A high-level Manager uses Constrained Position Shifting to allocate runways and assign Required Times of Arrival. A low-level 4D-Worker, trained with Soft Actor-Critic, treats time as a state variable and learns path-stretching manoeuvres to meet arrival times without predefined templates. An Extra Trees regressor bridges the layers with fast arrival-time estimates, avoiding full trajectory rollouts. Evaluated in BlueSky-gym across two training phases and a coordination-evaluation phase, the selected policy reaches success rates above 99.8\%, on-time rates near 99.9\% and tracking error below 0.2 minutes. A heading-augmented observation, intended to preserve the Markov property during holding patterns, instead causes catastrophic failure on closely spaced parallel runways when trained without hindsight relabelling, possibly reflecting a spatial-reachability constraint; the selected policy omits both heading augmentation and hindsight experience relabelling without performance loss. Coupling the Manager with this policy no longer reproduces the runway-overload signature of prior single-sink models under either runway-assignment mode: static assignment splits traffic evenly by construction, whereas dynamic assignment leaves a small residual load imbalance and a separation-compliance cost that widens sharply at low $k$ before plateauing, without a measured benefit in this uniform-demand setting. Across 600,000 simulated aircraft, stalling is effectively absent and no delay propagation between arrivals is observed; under homogeneous-fleet, single-airport conditions the framework therefore addresses the spatio-temporal integration gap in trajectory-based arrival management. ... Read more