When will we see more mainstream adoption of quantum computers, and what will that look like?

Crowder: Before this year, you could argue that anything you could do with a quantum computer could be simulated classically. There was no point of doing the computation on a computer other than learning about its information science. But that’s changed. This year, for the first time, you actually could run something on a quantum computer that you can’t run on a classical simulator. It doesn’t mean you can run anything on a quantum computer. It’s the first couple of kinds of computations that you can actually get value out of a quantum computer as opposed to trying to simulate it.

Over the next couple of years, the usefulness or utility will continue to expand. Right now, there are limitations of how big a problem we can run because of the quality of the systems. But we’re past the point where there’s value in running a quantum computer. It doesn’t mean there’s business value yet, because problems tend to get bigger, they need to be integrated into your workflows, etc. But we don’t think it’s going to take until 2033 for other people to get business value.

In the 1940s, we weren’t carrying around classical computers in our pocket and doing whatever it is we’re doing on our phones. They were the initial use cases in scheduling. I think that’s going to be true this decade [for quantum computers]. In the next decade when the systems get bigger and bigger and bigger — and better and better and better — you’re going to see more and more use cases.

What will be the first use cases?

There are three kinds of math that quantum computers are getting better at.

One of them is around simulating nature. Materials, properties, physics, chemistry — think of all the industrial as well as healthcare and life sciences chemistry-related things.

The second kind of math that quantum computers will be better at is a certain kind of complex structure in the data. The most famous algorithm, Shor’s algorithm — which all the nation-states are interested in — is that kind of math. It does factoring: A times B equals C. A times B; regular old computers are good at giving you C. But given C, your computer is not good at figuring out what A and B were. Classical computers are not good at that kind of math, which is a good thing. If we don’t have cryptography, we don’t have a digital economy.

This is part of the discussion about quantum. If it falls into the hands of bad actors, we are in deep trouble. But this kind of math is also used in machine learning — things like classification. It can help find fraud, better trial sites for clinical trials and better treatments when it’s given a patient’s health record data. There’s a lot of interest in the industry of leveraging quantum computers in the near term for those kinds of problems.

The last kind of math, which is also interesting — but for the second phase of the journey late this decade or in the next decade — is around optimization. What takes me N tries on a regular old computer will take the square root of N tries on a quantum computer. So N equals 100, squared equals a factor of 10. There might be breakthroughs in that space as well. Examples might be portfolio optimization in financial services, risk management and logistics — a whole bunch of things that people struggle with using regular computers to document today.

Quantum computers run somewhere down near zero degrees Kelvin. How are we going to solve the freezer problem? Put them in space?

Crowder: Unfortunately, space isn’t cold enough.

We need to isolate the computing part of it from the rest of the universe because you’re programmatically entangling these qubits with each other in a specific way. It can’t be perfectly isolated (at absolute zero) because if it is perfectly isolated, we can’t get them to do anything. It needs to be just connected enough to the rest of the universe so you can program it, but the rest of the universe can’t muck with it. That’s why either you need to keep it very, very, very, very cold — like we do with our technology — or you need to shoot laser beams at it [using a light-based approach] to take the entropy out. It’s complicated, and it’s not room temperature, no matter how you do it.

The good news is that there are commercial refrigeration techniques that are stable. They’re low cost, and they’re low energy compared to regular old computers — like compared to a rack of electronics. These things seem extremely efficient. The refrigeration action is not that big of a problem. There are other problems in scaling them and getting the cost down, but the underlying technology is there.

Do you think that quantum computers will ever make it into the average enterprise data center? Or will it be reserved for specialized use only large enterprises will be able to afford?

Crowder: The infrastructure around quantum computers, I know, seems weird and different right now. But we’ve deployed them at Cleveland Clinic; we’ve deployed them in Germany, Japan and Canada. We have large data centers. I think in the near-term, like the next several years, the technology is so rapidly advancing that it probably doesn’t make sense plopping them in enterprise data centers, because you’re going to want the latest technology.

Cloud delivery has definite advantages because the software stack is evolving quickly and allows us to get new capabilities out to everybody at the same time and because the underlying hardware improves year by year by year. You’re going to have quantum computers in enterprise data centers, whether that be [via] cloud provider or on premises. It’s going to happen. It just doesn’t make sense in the next several years.

Explain how quantum computing will intersect with AI. We have heard that quantum is not a match for generative AI.

Crowder: It’s a mix. People usually use the word AI to mean the latest trend in AI.

Thinking of AI in a broader sense [than just generative AI], yes, there is a direct connection in terms of finding data patterns and complex structure problems, through machine learning or other means. Quantum will automatically do a better job of classification, as an example. That’s not generative AI.

Generative AI is the latest stage in AI, and that is now the definition of AI for the next year or two until we come up with something else — the next definition of AI. Generative AI has just a tenuous connection to quantum computing. There are people who are doing research and looking at leveraging quantum on neural networks as opposed to deep neural networks. I don’t think anything has proven that quantum is going to be better in that space. But some researchers think that it might. Over the next couple of years, we’ll find out the answer. But at this point I haven’t seen any data that says definitively “yes.” But I haven’t seen any data that says definitively “no” either.

Don Fluckinger covers digital experience management, end-user computing, CPUs and assorted other topics for TechTarget Editorial. Got a tip? Email him here.