Zhang, LeiKaur, Khushdeep2025-02-132025-02-132024-01-0112969http://hdl.handle.net/11603/37640Quantum computers bring in a transformative technology with the potential to revolutionize various sectors, such as healthcare, chemistry, and data science. The functioning of quantum computers relies on both hardware and software. This leads to the emergence of “Quantum Software Engineering” involving numerous opportunities for discovery and research. While the Quantum Software Lifecycle may be similar to that of classical software, it holds unique challenges due to the principles of quantum mechanics involved in quantum computing. This thesis focuses on quantum software quality assurance and quantum education through three key contributions. First, it discusses existing state-of-the-art quantum software testing and debugging strategies and provides our visions and insights into testing and debugging quantum software in terms of challenges and opportunities for improving quantum software quality. Second, it presents a novel machine-learning platform that leverages a combination of multiple machine-learning models (e.g., support vector machine and random forests) to automatically detect flaky tests in quantum programs. Third, it introduces a novel active learning approach and assesses the best practices for teaching post-quantum cryptography to undergraduate and graduate students in the discipline of information systems. These contributions aim to highlight the importance of quantum software quality and quantum education to harness the full potential of quantum computers and help future generations develop the necessary skills in the field.application:pdfThis item may be protected under Title 17 of the U.S. Copyright Law. It is made available by UMBC for non-commercial research and education. For permission to publish or reproduce, please see http://aok.lib.umbc.edu/specoll/repro.php or contact Special Collections at speccoll(at)umbc.edu or contact Special Collections at speccoll(at)umbc.eduText