The rapid advancement of Large Language Models (LLMs) in recent years is not without concerns, such as a lack of privacy, environmental impact, and financial concerns. It might therefore be beneficial to use Small Language Models (SLMs) instead, which are more accessible to be run by individuals or organisations, thus resulting in more control over the model. This research investigates whether we can replace an LLM with an SLM inside an AI hint-generation system, and achieve comparable hint quality, by conducting an expert study to validate generated hints based on a set of criteria and by conducting a student experiment, investigating student satisfaction and trust in the system. The expert results show that the hints generated by the SLM-powered system are slightly less personalised to the situation, are noticeably more misleading and more often suggest the wrong approach. The student experiment shows similar results for these criteria, and shows a slight decrease in the overall perceived helpfulness of the hints, trust in the system and willingness to continue using the system. The most prevalent complaint for the SLM-powered system was its inconsistency in the hint quality, as it generated good and useful hints in some contexts, but also suggested wrong and unusable hints too often. Thus, while replacing the LLM with an SLM has potential, as it is capable of generating useful hints, current SLMs are still too inconsistent.

In the development of any new technology, it is essential to have methods to assess the quality of a system, to compare different systems to one another, and to compare different versions of the same system, to see if changes to the system can actually be classified as improvements. For quantum networks, this is no different. To further develop quantum networks, we need a benchmarking suite to judge the quality of such systems.

The aim of this research is to determine the effectiveness of blind quantum computation - a quantum network algorithm - as a benchmark. We determine what changes to the system affect the results of executing this application, and we use this to determine whether it would be effective to use this application as part of a larger benchmarking suite. We do this by simulating a quantum network, and manually varying system parameters one by one, to see if they have an effect on the results.

What we observed from these experiments is that blind quantum computation is sensitive to almost all system parameters. This means that introducing an imperfection into almost any system parameter will negatively affect the results of the application. This means the application is useful as a full-system benchmark, because it is affected by almost the entire system. However, it also means that the application is less useful as a benchmark for individual parameters.

This means that to make the most useful benchmarking suite, blind quantum computation would have to be combined with other quantum network applications that are more suitable for benchmarking individual system parameters.