School of Physics - Theses

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Start date: 2021 × Type: Masters Coursework thesis ×

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    Investigating Superconductivity in Gallium Nanowires Fabricated On Silicon Substrates Using Focused Ion Beam Technique
    Alsulami, Awsaf Awsaf Awadallah M ( 2023)
    Introducing superconductivity into semiconductor materials marks a significant advancement, offering the potential to combine the best attributes that each can offer in development of device elements. This thesis focuses on exploring superconductivity in Si:Ga to create devices that combine semiconductor and superconducting, SC, elements on a single platform. This approach aims to develop a pathway that may lead to enhanced functionality, including control and switching of the superconducting state (SC) and read-out, and may also lead to development of topological SC devices. Gallium's potential as a superconductor, with its various SC phases and a high critical magnetic field of about 9 T, is particularly noteworthy. In our research, we have induced superconductivity in Si:Ga using focused ion beam (FIB) implantation, examining different ion fluences in the range from 300 to 12000 pC/μm². The FIB system's precision enabled us to create Ga nanowires of various sizes. Our goal is to understand how doping concentration affects superconductivity, particularly whether the concentration of Ga influences superconducting properties like the transition temperature. Demonstrating SC in smaller dimensions (1×50 μm) could expand application possibilities. We have successfully observed complete drop in resistance in nanowires measuring 1μm in width and 50 μm in length at fluences of 900 and 1000 pC/μm², along with a clear SC transition at fluences between 1500 and 2000 pC/μm². These results provide valuable insights for future research, particularly in stabilizing high Tc Ga phases and developing more complex structures like SC loops.
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    Simulating Noisy Quantum Algorithms and Low Depth Quantum State Preparation using Matrix Product States
    Nakhl, Azar Christian ( 2021)
    Since the proposal of Quantum Computation in the 1980s, many Quantum Algorithms have been proposed to solve problems in a wide variety of fields. However, due to the limitations of existing quantum devices, analysing the performance of these algorithms in a controlled manner must be performed classically. The leading technique to simulate quantum computers classically is based on the Matrix Product State (MPS) representation of quantum systems. We used this simulation method to benchmark the noise tolerance of a number of quantum algorithms including Grover’s Algorithm, finding that the algorithm’s ability to discern the marked state is exponentially suppressed under noise. We verified the existence of Noise-Induced Barren Plateaus (NIBPs) in the Quantum Approximate Optimisation Algorithm (QAOA) and found that the recursive QAOA (RQAOA) variation is resilient to NIBPs, a novel result. Also integral to the performance of quantum algorithms is the ability to efficiently prepare their initial states. We developed novel techniques to prepare low-depth circuits for slightly entangled quantum states using MPS. We found that we can reproduce Gaussian and W States with circuits of O(log(n)) depth, improving on current best known results which are of O(n).