Ready or Not (and You’re Probably Not), Quantum Computing’s Nearing
That spells big money for a relatively few eventual providers of quantum solutions and huge security trouble for everyone else.
As we recently established in a separate post, there’s a lot we don’t know about AI. Same’s true of quantum computing, where experts like David Mooter, a principal analyst at Forrester, didn’t even know with 100% certainty that it would ever come off the drawing board in a meaningful way as recently as a couple years ago.
“The error rates were just way too high. The qubit counts were just too low,” he says.
And now?
“Now I feel like it will be a reality. I’m very confident in that,” Mooter (pictured) says. “When is definitely more of an up-in-the-air question.”
And also an extremely important one, certainly for venture capital firms, which tripled their investments in quantum startups last year to $5.8 billion, and almost certainly for the rest of us too. Before I discuss why, I should make clear that quantum computers are not supercomputers that can just solve everything, because…well:
“One thing I have seen that drives me nuts is journalists who don’t know what they’re talking about will describe quantum computers as supercomputers that can just solve everything,” Mooter says. “The way I like to describe it is that quantum computers are not supercomputers. They’re different computers. They solve problems in a different way.”
That different way, without getting deep into the (quantum) mechanics of the matter, lends itself especially well to combinatorial challenges like the traveling salesman problem in which there are huge volumes of possible solutions, way more for a human to sort through ever and more than even powerful classical computers can handle quickly. Mooter points to modeling molecular interactions for drug discovery as one example and optimizing investment portfolios as another. Optimizing supply chains, energy grids, and chemical manufacturing processes are among others frequently cited.
Good news for anyone who likes the sound of improving all those areas and more: Your wait continues to get shorter and shorter. In fact, quantum computing made such strides last year as “vendors moved beyond theoretical fault‑tolerant architectures into early engineering reality,” per a recent Forrester report, that practical commercial applications of the technology could begin arriving as soon as 2030, a good five years earlier than the analyst considered likely just 15 months ago.
Developments like that have inspired pretty much all my past writing about quantum. Generative AI was supposed to be years away when it upended our entire industry late in 2022. I’m hoping we can avoid repeating that scenario this time, but also increasingly thinking we’re going to have to move a lot faster to do so.
To take one of a steadily arriving series of examples, research published days ago by Caltech and quantum startup Oratomic outlined a new approach to quantum error reduction that could cut the minimum number of qubits required to make a useful quantum computer from millions to as few as 10,000.
Consider as well the progress being made in hybrid quantum computing, which divides processing chores across classical and quantum machines. P&G used the technique last year in an experiment aimed at optimizing a manufacturing process with 10^114 possible arrangements of components and ingredients, more than the number of atoms in the universe.
Acting on its own, a regular computer needed six hours to come up with an answer. SAS’s quantum AI platform needed two minutes, but produced unreliable results. Working together, however, the conventional and quantum computers produced an accurate, actionable result in 12 minutes, or about 96.7% less than what the classical machine needed solo.
Time for partners to start thinking up quantum and hybrid quantum solutions then? Maybe not.
“Don’t view quantum as the next layer that will replace CPUs and GPUs,” said Omdia analyst Jay McBain in a conversation at the Canalys Forum late last year. “It won’t, and it’s going to be interesting for very few partners, maybe in the thousands total.”
But it will be very interesting, or more accurately terrifying, for every partner in the industry with respect to security, and sooner perhaps than any of us would like. Remember that 10,000 qubit breakthrough at Caltech? Well, these guys claim they’ve found a hybrid way to break RSA encryption with just 5,000.
Could be hot air, of course, but gets to the reason we probably need to be thinking harder about quantum computing than most of us are even so. The same Forrester report that said commercial uses of quantum computing could start arriving by the end of the decade said that “Q-Day,” when quantum machines break current public-key cryptography, could arrive then too. Google is getting nervous about timelines on quantum security as well.
And we’re not ready. Governments, financial institutions, utilities, and infrastructure providers are working toward adoption of post-quantum cryptography schemes capable of keeping data safe past Q-Day, says Sandy Carielli, a VP and principal analyst at Forrester. “Firms in other industries are still learning and coming up to speed and are not as far along.”
Meaning they’re late. “I’ve been telling my clients that they have to start now, because full migration will likely take them past that 2030 date,” says Carielli, who also recommends inventorying data and prioritizing it by importance and longevity, along with forming a cross-functional Q-Day team, as good steps toward partial readiness.
And partial readiness is a whole lot better than no readiness. The other similarity between AI and quantum computing, in addition to there being a lot we don’t know about both, is that both are progressing very quickly and appear likely to progress even faster in the near future. At present, only a relative handful of people have access to quantum computers, but that was once true of classical computers as well. Then universities and research institutions started deploying them, sparking a “Cambrian explosion of algorithms,” Mooter notes.
“That took decades,” he continues. “We’ll see the same thing with quantum computers, but it won’t be decades because we’re building on the foundation of classical computer science and we have a lot more funding rolling in than computers did back in the 1950s.”
So years more likely, and I suspect they’ll feel like short ones. Brace yourself now.
Over on the Business of Tech
Host Dave Sobel discusses Treeline, the a16z-backed, AI-native MSP I wrote about last week, with the author of that piece. As in me. I’ll be appearing on the show live this coming Wednesday here.
Also worth noting
Three things not to miss at next week’s Channel Partners Conference & Expo in Las Vegas: This session I’m moderating, this other session I’m moderating, and the Alliance of Channel Women’s opening day networking event.
Modern Threat Protection, ConnectWise’s new security platform, combines managed EDR, SIEM services, and email security. More on this coming next week.
Titan has Silicon Valley AI engineers. Shield has Silicon Valley AI engineers. Treeline has Silicon Valley AI engineers. Looks like Kaseya wants a few too.
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Channel Leaders, a peer group for channel sales professionals, is now a full-blown community for channel sales professionals called the Channel Sales Association.
Alex Hesterberg is the new CEO at Devicie, who you read about here earlier.
Lindsey Westbrook is the new VP of marketing at Coro.
Alysia Vetter is the new director of PR and communications at GTIA.
Sharon Florentine is now manager and research analyst at GTIA.