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Quantum computing x artificial intelligence = a perfect storm?

Artificial intelligence is quite possibly the hottest topic in tech right now (except for maybe the Facebook/Cambridge Analytica scandal by nature of Zuckerberg’s congressional testimony over the last couple of days, but even that touched on Facebook’s future with A.I.). Microsoft’s Satya Nadella reorganized their company in recent weeks and rebranded one of the three new core pillars of the company as ‘Cloud + AI Platform’. IBM’s Watson platform has moved to the forefront of IBM’s corporate offerings and is hailed as the future of the company by many observers and insiders alike. Google and Amazon and Facebook and Apple are in a bidding war for top A.I. talent across the industry, and rightfully so. There’s almost nothing in our digital futures that won’t be touched by artificial intelligence in some way. But — and there’s always a but — the dominant form of A.I. and machine learning requires massive amounts of data and even more powerful supercomputers to really make huge strides forward.

A small company in Berkeley, California might be changing all that. How you might ask? Quantum computing.

Qubits ftw

Quantum computing is a complex subject with little consumer application (yet, anyway). That’s probably why it’s far less discussed than A.I. Furthermore, quantum computing is even harder and more fragile to construct and use than most massive A.I. arrays.

For a great primer on quantum computing, I point you to my co-founder’s brilliant article about it from last year. I also wrote about why we think it’s the technology to watch out for in 2018. Even though a working quantum computer might be years away, the sheer potential of it is so great, it made our #1 in 2018. But, for purposes of this article, if a true quantum computer is constructed and operating, the basic idea is that it can compute and solve far larger and more complex problems faster than any classic (read: silicon microchip) computer can currently manage — and in less physical space, no less. And, the improvements in simultaneous operating qubits result in exponential increases in processing speed. So, the better it gets, the faster it gets in a hurry.

We’ve already written about what the most powerful supercomputers can bring to the world of A.I. research. The great promise of quantum computing is that we could have multiple machines with that level of computing power working simultaneously and continuously on a given problem/process. Rigetti Computing might be on the brink of accomplishing just that.

Rigetti Computing

According to MIT’s Technology Review, Rigetti “used one of its prototype quantum chips—a superconducting device housed within an elaborate super-chilled setup—to run what’s known as a clustering algorithm. Clustering is a machine-learning technique used to organize data into similar groups. Rigetti is also making the new quantum computer—which can handle 19 quantum bits, or qubits—available through its cloud computing platform, called Forest, today.”

Now, that’s not to say a working quantum computer will be teaching A.I. systems tomorrow. They’re still a long way off from a real proof of concept. But, this very well could be a real first step in that direction.

MIT TR offers the sobering conclusion that “Rigetti’s algorithm, for instance, isn’t of any practical use, and it isn’t entirely clear how useful it would be to perform clustering tasks on a quantum machine.” But, Rigetti’s Will Zeng, who’s their head of software and applications, argues they may have taken a major step toward building a quantum machine. “This is a new path toward practical applications for quantum computers,” Zeng says. “Clustering is a really fundamental and foundational mathematical problem. No one has ever shown you can do this.”

I suggest you read the entire MIT article, because it goes into great detail about all the players in the game, what Rigetti is up against, why A.I. & quantum computing together might not be the panacea we all hope it might be. It’s super useful context. But if MIT’s skepticism proves incorrect, and Rigetti or one of its larger rivals figure out how to combine these two technologies in a useful and practical way? Well that, my friends, might just be a perfect storm.


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Jeff Francis

Jeff Francis is a veteran entrepreneur and founder of Dallas-based digital product studio ENO8. Jeff founded ENO8 to empower companies of all sizes to design, develop and deliver innovative, impactful digital products. With more than 18 years working with early-stage startups, Jeff has a passion for creating and growing new businesses from the ground up, and has honed a unique ability to assist companies with aligning their technology product initiatives with real business outcomes.

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