Extending a Model Language to Handle Entangled Concepts in Artificial Intelligence

In quantum information and computation, entanglement is a resource. When combining concepts, the application of entanglement outside of micro-physical systems is an useful tool. We suggest new cognitive image-based tests that do not need to be translated. No prior knowledge of terms related to the concepts is required, therefore the choice is more intuitive. We examine the merging of two concepts that establish non-classical statistical correlation and present an entanglement-aware vector encoding algorithm. This research’s added value results in an automated system that teaches artificial intelligence to identify and handle entangled concepts.