The dynamic capability of these AI languages to change while the program is running is superior to languages relying on a batch method, in which the program must be compiled and executed prior to outputs. Plus, these quantum AI programming languages enable both data and code to be written as expressions. Since functions in these frameworks are written like lists, they’re readily processed like data, so specific programs can actually manipulate other programs via metaprogramming — which is key for their underlying flexibility. This advantage also translates into performance benefits in which such languages operate much faster in applications — such as those for bioinformatics involving genomics — aided by various dimensions of AI.
The AI effect
When enabled by flexible programming languages for developing AI, quantum computing allows organizations to perform AI calculations much faster, and at a greater scale, than they otherwise could. These programming languages also underpin both statistical and symbolic AI approaches enhanced by quantum computing. Optimization problems, for example, are traditionally solved in knowledge graph settings supporting intelligent inferences between constraints.
