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.

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 isnt making this claim, often known as quantum supremacy, pertaining to its own quantum R&D efforts.

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In fact, IBM has taken a practical approach that keeps expectations for the technologys 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 Googles 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 worlds most powerful existing supercomputer, IBM Summit.

Googles benchmark didnt fall into any of thecore use casesincluding AI, optimization, simulation, or even cryptographyforwhich quantum computing might some day hold an advantage over classicalarchitectures. The proof of the pudding for AI is whether a computerbuilt on quantum principles can do data-driven algorithmic inferencing fasterthan a classical computer, or optimistically, faster than the fastestsupercomputers currently in existence.

For its own R&D efforts in this field, IBM is merely aiming at the more realistic goal of quantum advantage. This refers to any demonstration that a quantum device can solve a problem faster than a classical computer. Considering the range of commercial activity in this field, the likelihood that quantum architecture will soon show a clear performance advantage for core use casesespecially AIgrows by the day.

In that regard, we should the range of recent quantum productannouncements by IBM and other leading tech vendors all focus on AI use cases:

All of these vendors are building developer ecosystemsaround their various quantum computing platforms.

In January, IBM announced the expansion of Q Network, its 3-year-old quantum developer ecosystem. To encourage the development of practical quantum AI applications, IBM provides Q Network participants with Qiskit; IBM Quantum platform, which provides cloud-based software for developers to access IBM quantum computers anytime; and IBM Quantum Experience, a free, publicly available, and cloud-based environment for team exploration of quantum applications. Many of the workloads being run include AI, as well as real-time simulations of quantum computing architectures.

Another key industry milestone came in March when Google launched TensorFlow Quantum. This new software-only framework extends TensorFlow so that it can work with a wide range of quantum computing platforms, not limited to its own hardware, software, and cloud computing services.

As quantum techniques start to prove their practicality on core AI use cases, they will almost certainly be applied to AIs grand challenges.

At the level of pure computer/data science, AIs grandchallenges include neuromorphic cognitive models, adaptive machine learning,reasoning under uncertainty, representation of complex systems, andcollaborative problem solving.

We expect that quantum AI developers in the ecosystems ofIBM and its rivals will tackle all of these grand challenges using theirrespective quantum AI tools, libraries, and platforms.

The most important grand challenges for quantum AI will have compelling practical applications. Chief among these is trying to mitigate a key risk that quantum technology has itself unleashed on the world: the prospect that it might break public-key cryptography as we know it. Fortunately, IBM and others are making progress on developing quantum-resistant cryptographic schemes.

Though its not clear how much IBM is investing in the R&D needed to combat the technologys more malignant misuses, you best believe that they are deeply enmeshed in some fairly secretive projects in these domains.

Developers are everything to the future of quantum AI. Themost important investments that IBM is making in quantum AI is to build out itsdeveloper and partner ecosystem and to provide them with sophisticated butconsumable tools, libraries, and cloud services.

Among commercial solution providers, IBMs quantum developer ecosystem, Q Network, is the most mature and extensive. Lets hope that sometime this year IBM begins to support TensorFlow Quantum within Q Network and integrates it seamlessly into IBM Quantum Experience.

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