Quantum computing’s potential to revolutionize AI depends on growth of a developer ecosystem in which suitable tools, skills, and platforms are in abundance. To be considered ready for enterprise production deployment, the quantum AI industry would have to, at the very least, reach the following key milestones:

• Find a compelling application for which quantum computing has a clear advantage over classical approaches to building and training AI.
• Converge on a widely adopted open source framework for building, training, and deploying quantum AI.
• Build a substantial, skilled developer ecosystem of quantum AI applications.

[ Also on InfoWorld: The 6 best programming languages for AI development ]

These milestones are all still at least a few years in the future. What follows is an analysis of the quantum AI industry’s maturity at the present time.

Lack of a compelling AI application for which quantum computing has a clear advantage

Quantum AI executes ML (machine learning), DL (deep learning), and other data-driven AI algorithms reasonably well.

As an approach, quantum AI has moved well beyond the proof-of-concept stage. However, that’s not the same as being able to claim that quantum approaches are superior to classical approaches for executing the matrix operations upon which AI’s inferencing and training workloads depend.

#ai