A Quantum Production Model
This work addresses the challenge of enhancing AI problem-solving procedures like hierarchical tree search through quantum computation, but it appears incremental as it builds on existing reversible and quantum models.
The authors tackled the problem of connecting artificial intelligence with quantum computation by proposing a quantum production system model, which they showed can be integrated with Grover's algorithm to achieve a speedup compared to classical methods.
The production system is a theoretical model of computation relevant to the artificial intelligence field allowing for problem solving procedures such as hierarchical tree search. In this work we explore some of the connections between artificial intelligence and quantum computation by presenting a model for a quantum production system. Our approach focuses on initially developing a model for a reversible production system which is a simple mapping of Bennett's reversible Turing machine. We then expand on this result in order to accommodate for the requirements of quantum computation. We present the details of how our proposition can be used alongside Grover's algorithm in order to yield a speedup comparatively to its classical counterpart. We discuss the requirements associated with such a speedup and how it compares against a similar quantum hierarchical search approach.