This thesis investigates the robustness of Gate Set Tomography (GST) under the influence of time-correlated (non-Markovian) noise. GST is a widely used protocol for characterizing quantum gates, yet its efficacy traditionally relies on the assumption of Markovian noise, where errors are memoryless. This research challenges this assumption by introducing non-Markovian noise into simulated GST experiments using a custom-developed Python library. The study com- pares baseline GST performance under Markovian noise with GST results un- der various non-Markovian noise conditions. The findings highlight significant discrepancies in GST’s error reporting and reveal a distinct trade-off between GST’s accuracy and consistency in practical, non-Markovian settings. This work contributes to the broader understanding of error characterization in quantum computing and provides a robust framework for future studies in quantum gate fidelity under realistic noise conditions.

In this research the Metropolis-Hastings algorithmis implemented for the problem of program synthesis and compared with Brute, a best-first search, together with multiple other different search algorithms. The implementation and choices of the Metrolpolis-Hastings algorithm are discussed in detail. The algorithms are tested for three different domains, each with their own associated DSL. Finally, comparisons are drawn between the search algorithms by analyzing the results of these experiments. It is found that the performance of any search algorithm depends very heavily on the specific domain and cost function used and the Metropolis-Hastings algorithm falls short in terms of performance when compared with other conventional methods.