Quantum AI in the 2020s and Beyond: What IBM Is Doing

By James Kobielus

The most important investments that IBM is making in quantum AI is to build out its developer and partner ecosystem and to provide them with sophisticated tools, libraries, and cloud services.

Quantum computing promises to accelerate artificial intelligence (AI) faster than the speed of light. But first, this futuristic technology must prove its worth as an alternative to more mature, traditional approaches to process data-driven statistical algorithms.

Achieving quantum advantage for AI apps

IBM continues to take a leadership position in quantum computing. Among other efforts, it is evangelizing quantum computing to developers of AI, deep learning, and machine learning applications.

Quantum computing might be capable, in its current form, of performing feats that are practically impossible for computers built on traditional von Neumann architectures. However, that has not been proven, and IBM isn’t making this claim, often known as “quantum supremacy,” pertaining to its own quantum R&D efforts.

In fact, IBM has taken a practical approach that keeps expectations for the technology’s prowess in check. It has also been in the vanguard of debunking claims of this nature by other tech vendors. A recent case in point was Google’s claim in fall 2019 that Sycamore, its 53-qubit quantum hardware platform, had completed a calculation in a few minutes that would have taken 10,000 for the world’s most powerful existing supercomputer, IBM Summit.

Google’s benchmark didn’t fall into any of the core use cases-including AI, optimization, simulation, or even cryptography-for which quantum computing might some day hold an advantage over classical architectures. The proof of the pudding for AI is whether a computer built on quantum principles can do data-driven algorithmic inferencing faster than a classical computer, or optimistically, faster than the fastest supercomputers currently in existence.

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