Quantum Artificial Intelligence Is Closer Than You Think

The deliberate collision of two game-changing technologies has the potential to upend the technology industry and bring about a new era of business disruption and innovation. Few industries will be spared this transformation, and it will create completely new value and risks. Hyperbole? I don’t think so. In the future, artificial intelligence is likely to become supercharged by quantum computing. It’s a partnership that could change the world.

The growing wonder and value of artificial intelligence (AI), triggered by the broad availability of generative AI in late 2022, is understandable. Uses of this software such as the creation of remarkable human-like text and graphics can often exceed the hype. That’s saying something for the technology industry that too often gets ahead of itself. Frequent and mesmerizing new updates to generative AI and the innovation that quickly follows demonstrates that this technology is evolving at breakneck speed.

But, despite the introduction of faster microchips to feed its hunger, AI is ultimately constrained by our ability to continue to squeeze more processing power from silicon-based hardware. It’s just a limitation of physics. That said, let’s recognize that classical computing, based on transistors and electricity, has served us all well and powered the information age to date.

If AI could be powered by a new generation of computing power—quantum processing—then it would leave today’s innovation in the dust.

That day may be coming sooner than you think. It may not be next year, but could we see practical applications within the next decade?

Quantum computing, an entirely new way of processing information, when combined with AI, has the potential, without exaggeration, to bring about a new computing revolution. Transformation may be an understatement.

Today, quantum computing isn’t a term that’s particularly familiar to those outside the technology and physics worlds, but it soon will be. This form of computing, still in its relative infancy, originates out of quantum mechanics, the behavior of nature at the scale of atoms and subatomic particles. Leveraging this science to power a new form of computing began in earnest in the 1970s. By the 1980s, notable scientists such as Paul Benioff, Yuri Manin, Richard Feynman, and David Deutsch, established the core principles. By the late 1990s, the first functioning quantum computers emerged.

Instead of processing information in terms of 1s and 0s—called bits—and doing it serially, which is how classical computing works, quantum computers use qubits which can represent a 1 and a 0 simultaneously. Like a guitar, bits are similar to playing one note at a time, whereas, qubits play several notes together. With enough qubits, quantum computers could theoretically be millions of times faster than the fastest microchip computers today.

But quantum computing isn’t confined anymore to research labs, although technological hurdles must still be overcome. Big tech firms such as IBM, Microsoft, and Google, and many compelling market entrants such as IonQ and D-Wave Systems, provide capabilities that can be used today. That said, usage is still fairly specialized and often experimental in nature. Practical uses today can be found in pharmaceutical development, cybersecurity, financial services, and weather forecasting.

There’s a lot of optimism that the pace of quantum computing innovation will continue to accelerate, and we could see mainstream adoption by the end of the decade. That’s just around the corner.

Independent of artificial intelligence, quantum computing is going to be a big deal. However, coupled with AI, it will be a gamechanger.

Revolutionary innovation is not an exaggeration.

In the interim, work to integrate artificial intelligence with quantum computing will continue. Research is nascent and non-trivial technological hurdles exist.

Quantum Artificial Intelligence (QAI) — and in particular, its subset research area Quantum Machine Learning (QML) — will use new quantum-designed algorithms with superpowers that result in much more powerful AI models. As QAI gradually emerges, we can expect significant improvements in the speed, efficiency, and accuracy of AI. We should also anticipate the emergence of completely new and surprising business capabilities.

At first, the benefits will be reaped by certain industries and needs, particularly those with optimization requirements. But later, as QAI becomes more mainstream, almost every enterprise will need to have a strategy.

An example of work-in-progress today is a partnership between IonQ and Hyundai who are researching the use of QAI to process images such as road signs.

For learning and experimenting, Google currently offers a platform, TensorFlow Quantum (TFQ), for prototyping hybrid quantum-classical AI models.

Rather than playing catch-up when QAI arrives, organizations should have the technology on their radar and follow developments closely. Those lucky enough to have an innovation lab or equivalent, should consider some experimental and research work, if only for the purposes of acquiring skills and knowledge.

The high level of interest from industry and the pace of scientific research clearly suggests that QAI is coming, and the question will be whether you are ready. It’s probably closer than you think and it’s likely going to be a very big deal.