Adams, Ryan P.Miryala, Sushma2026-01-052026-01-052025https://theses-dissertations.princeton.edu/handle/88435/dsp01ng451m99kSuperconducting high-entropy alloys (HEAs) have recently garnered significant attention across numerous fields due to their unique blend of properties such as increased mechanical strength, structural stability, and tunable electronic properties. These attractive features thus position HEAs as a strong candidate for multiple real-world applications, especially as next-generation superconducting qubit materials considering their robust performance under extreme conditions such as low temperatures and high magnetic fields. However, the creation of HEAs consists of a vast compositional space, enabling researchers to choose from a great range of elements in different proportions heated at multiple cycles. In order to navigate this complex field, this study utilizes Bayesian optimization as a data-driven strategy to expedite the process of discovering and optimizing HEAs with high superconducting performance. Due to the high cost and time often associated with carrying out experiments in laboratories, this approach of iteratively updating a probabilistic model with an initial set of training data proves to be beneficial in focusing efforts on only the most promising configurations. It is also crucial to note that this research study is the first in literature to explore and computationally optimize a novel composition of seven specific elements of Gold, Tin, Antimony, Palladium, Silver, Tellurium, and Indium. This combination of Bayesian optimization and superconducting HEAs demonstrates a dynamic convergence between machine learning and materials innovation, broadening research horizons for quantum technology and engineering.en-USPrinceton University Senior Theses