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    Single-photon indistinguishability of nanowire quantum dots for entanglement swapping
    (University of Waterloo, 2026-09-23) Morales Gutierrez, Catalina Maria
    Entanglement swapping is a crucial technique for realizing quantum networks and the quantum internet. Photons are promising carriers for quantum information processing, and semiconductor quantum dots can act as sources of both single and entangled photons. An ideal quantum dot based entangled photon source should provide on-demand emission, high brightness, suppressed multiphoton emission, high indistinguishability, and high en- tanglement fidelity. In this thesis, nanowire quantum dots (NWQDs) are used to generate entangled photon pairs. Using a Hanbury Brown–Twiss (HBT) measurement, we demon- strate single-photon emission by observing strong suppression of multiphoton events. The NWQDs achieve a brightness of around 10 k counts per second, which is advantageous for quantum communications which often operates over large optical losses of 30dB or higher. The generated entangled states exhibit entanglement fidelities above 95%, indicating that the entanglement produced by the NWQDs is reliable. The main focus of this work is the characterization of the indistinguishability of photons emitted by a NWQD. Indistinguishability is assessed using Hong Ou Mandel (HOM) inter- ference, where the HOM visibility is used as a proxy for photon indistinguishability. The relevant optical transitions correspond to exciton, biexciton, and trion states (positive and negative), which are treated as analogous to atomic emission lines. Previous measurements on the exciton transition reported a HOM visibility of about 40%(on a 0−100% scale), mo- tivating investigation into noise mechanisms, particularly charge noise, that may degrade indistinguishability in the semiconductor environment. For this purpose, the NWQD is placed in a quadrupole gate structure that enables application of a lateral electric field. HOM measurements are performed under different gate voltages to study the effect of the electric field to evaluate whether electrical control of the charged environment improves HOM visibility. Additionally, a wavelength shift of approximately 0.1 nm is observed for both exciton and biexciton transitions when changing from 0 V to 300 V. Indistinguisha- bility is calculated using two complementary methods which are named the area method and the fitting method. While a small systematic change in HOM visibility is observed across the investigated voltage range, any apparent trends are not fully conclusive, as they may be influenced by the fitting procedures employed to extract the HOM visibility.
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    Dynamic Index Management in Log-Structured Merge Storage
    (University of Waterloo, 2026-09-23) Ni, Mingkun
    Many modern key–value stores adopt Log-Structured Merge-trees (LSMs) to support high write throughput. A critical component of an LSM is the memtable, and its internal index structure (memtable type) affects system performance as the memtable serves all writes and a substantial fraction of reads. Existing systems are generally designed with a static memtable index structure. However, such a static design cannot adjust to workload shifts as it is unable to consider workload-dependent characteristics of different memtable types, leading to performance degradation. To address this shortcoming, we propose Dynamic Memtable Index Organization (DIO), a cost-driven framework that dynamically adapts the memtable type according to workload characteristics. DIO models query costs by dissecting queries into primitive memory operations, predicts the workload cost under multiple memtable types, and selects a memtable type that minimizes cost. DIO performs memtable (type) transitions at meaningful memtable lifecycle boundaries, enabling efficient adaptation with minimal disruption to system execution. DIO incorporates mechanisms for early workload-shift detection and on-the-fly memtable conversion, allowing the system to rapidly converge to the most suitable memtable type after workload changes. We implement DIO in RocksDB and show that, across diverse dynamic workloads, it improves post-transition throughput by up to several orders of magnitude over unmodified RocksDB with negligible runtime overhead.
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    Deep Learning-based Identification and Change Detection of Oil/Gas Well Pads Using Satellite Imagery
    (University of Waterloo, 2026-09-22) Xu, Hongzhang
    Global energy extraction and industrial activities have caused widespread land disturbance, making large-scale and accurate environmental monitoring essential for ecosystem protection and land reclamation. Satellite remote sensing provides a powerful tool for monitoring these environmental impacts. In recent years, deep learning algorithms for remote sensing image interpretation have developed rapidly. However, significant gaps remain in bridging algorithmic developments with practical domain applications. In real-world scenarios, existing target extraction algorithms rarely consider the spatial context between targets and their surrounding environments, and optimal training constraints, such as loss functions, remain under-explored. To address these general challenges, this doctoral study develops a series of deep learning methods for target extraction, change detection, and land disturbance and reclamation evaluation, using oil/gas well pad monitoring as a primary application. The research follows a systematic progression: 1. Loss Function Optimization for Road Extraction: Linear feature (Road) extraction plays a foundational role across diverse remote sensing applications. However, how to select a suitable loss function for linear feature extraction is rarely studied. To address this, I conducted a comprehensive comparative study of 12 loss functions for road segmentation, showing that region-based loss formulations (e.g., Log-Cosh Dice and Squared Dice) significantly outperform distribution-based loss functions in maintaining the connectivity of linear features. 2. Well Pad Identification: To overcome the challenge that target extraction algorithms often ignore environmental context, I developed a modified Mask R-CNN network based on the coupled spatial relationship between roads and well pads. By incorporating the road network as a spatial prior, the proposed model effectively solves the spectral similarity problem between abandoned well pads and natural vegetation, improving average precision by over 20% compared with baseline methods. 3. Well Pad Change Detection: To evaluate post-disturbance land recovery and semantic transitions after mining development, I proposed a constrained dual-head HRNet architecture for semantic change detection. By employing a cosine similarity loss to constrain feature structures, the model achieves an 80.05% mIoU on the semantic change detection task. Moreover, to support these methodological advancements and promote benchmark evaluations, we constructed two comprehensive datasets—the Alberta Roads and Wells Dataset and the Alberta Semantic Change Detection dataset—using satellite imagery over oil sands regions in Alberta, Canada. Overall, this research advances the practical capabilities of deep learning in remote sensing by tackling foundational challenges in loss function selection, spatial context modelling, and semantic change analysis, providing a transferable and scalable methodology for broad environmental and geographic applications.
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    Development of a Soft Robotic Arm Compression Sleeve with Tangential Motion for Assisted Lymphedema Management
    (University of Waterloo, 2026-09-22) Kowalski, Jacob
    Lymphedema is a chronic condition affecting over one million Canadians, in which impaired lymphatic function reduces the body’s ability to circulate and process lymphatic fluid. Management typically requires ongoing treatment, often including Manual Lymphatic Drainage (MLD) by trained specialists. However, access to these treatments can be limited, while current at-home compression devices primarily apply normal compression rather than reproducing the directional skin deformation characteristic of MLD. Existing active compression garments can also be bulky and non-portable, and may require users to remain relatively stationary during treatment. These limitations motivate the development of alternative wearable technologies that can improve access to treatment while reducing disruption to daily activities. Soft robotics technologies offer a promising approach to addressing these limitations; however, existing wearable soft robotic systems can be constrained by the cost and bulk of pneumatic control hardware, the inability to generate controlled tangential motion along the skin, and limited options for compliant surface-pressure sensing. One contributor to system cost is the use of high-performance solenoid valves, which can cost tens to hundreds of dollars for precise pneumatic control [1], [2], [3]. In contrast, low-cost miniature on/off solenoid valves under three dollars are widely available and offer an attractive alternative for portable and affordable wearable systems [4]. However, their flow characteristics can vary significantly among valves and operating conditions, and their performance is often insufficiently characterized for direct incorporation into model-based pneumatic control. To address these challenges, this thesis contributes: (i) an experimental framework for characterizing and modelling the flow characteristics of low-cost solenoid valves, identifying their controllable operating regions, and incorporating the resulting models into closed-loop pneumatic control; (ii) a custom flat, flexible capacitive pressure sensor; (iii) a movable Pneumatic Artificial Muscle (PAM)-based compression cuff and cable-driven locomotion system; and (iv) the integration of these components into an active compression sleeve prototype. The developed active compression sleeve integrates the mechanical and pneumatic hardware required to apply compression while enabling controlled tangential motion of the cuff along the limb. The primary mechanical, pneumatic, and sensing subsystems were evaluated independently to characterize their performance and demonstrate their feasibility for integration. Full closed-loop operation of the final integrated prototype remains a subject for future work. Nevertheless, the resulting system establishes a foundation for further calibration, control development, and experimental evaluation of a wearable device intended to supplement existing approaches to lymphedema management.
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    Modeling and Characterization of a Tunable Coupler-Flux Qubit Ultrastrongly Coupled to an Open Transmission Line
    (University of Waterloo, 2026-09-22) Rafati, Parinaz
    Studies in relativistic quantum information have shown that the vacuum state of a quantum field contains pre-existing correlations. Vacuum correlations can be extracted locally by coupling spatially separated probes to a quantum field even without direct light-matter interaction. Despite extensive theoretical work, there has been no experimental demonstration of entanglement harvesting to date. A superconducting circuit design has been proposed as a platform for investigating entanglement harvesting. The device consists of a flux qubit coupled to a one-dimensional transmission line through a tunable coupler. The tunability of the coupler provides control over the qubit-field interaction, allowing the coupling strength to be tuned over a range from the weak regime to the ultrastrong coupling regime at nanosecond time scales. These features make the design a promising platform for investigating entanglement harvesting from vacuum correlations under controlled experimental conditions. However, as the system approaches the ultrastrong-coupling regime, commonly used approximations, such as the rotating-wave approximation (RWA) in the spin-boson model and the two-level approximation for the qubit, may no longer provide an accurate description of the system. In this thesis, we investigate a superconducting qubit design as a potential platform for future studies of entanglement harvesting. We first develop a theoretical model of a tunable coupler–flux qubit galvanically coupled to a transmission line and derive the Hamiltonian of the coupled system. The model is then used to calculate the transverse and longitudinal coupling strengths between the qubit and the transmission-line modes and to investigate their dependence on the coupler flux. We then characterize a proposed three-loop superconducting qubit design through numerical simulations and comparison with experimental spectroscopy data. The system is calibrated to establish the relation between the applied voltages and external fluxes. We then investigate how the junction parameters and junction asymmetry affect the agreement between the simulations and experimental data. Finally, the coupler flux is varied to study its effect on the simulated response. The comparison shows that variations in the junction parameters and junction asymmetry can improve the agreement with the experimental data, while variations in the coupler flux do not lead to a systematic improvement under the assumptions considered.