An inherent problem with RBS is their great sensitivity to the number of hypotheses, data and rules of the system itself. More specifically, selecting which rules are applicable at each moment can greatly slow down the inferential process. This process, called pattern matching, is an unresolved issue in AI.
It is precisely here that quantum computing, by its intrinsically parallel nature, will be useful for AI in general, and for RBS in particular.
The algorithms and their implementation will be used to build a quantum rule-based system that solves a specific problem: diagnosing and treating a specific type of breast cancer known as Invasive Ductal Carcinoma (IDC).