Listen: Next in Tech | Episode 98: Quantum is not boring

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Eric Hanselman

Welcome to Next in Tech, an S&P Global Market Intelligence podcast with a world of emerging tech leads. I'm your host, Eric Hanselman, Chief Analyst for Technology, Media and Telecom at S&P Global Market Intelligence. And today, we're going to be kicking off the new year with a discussion of quantum computing with our guest, analyst Gabriella Brown. Ellie, it's great to have you on the podcast. Thanks for joining us.

Gabriella Brown

Thanks for having me. 

Question and Answer

Eric Hanselman

And I wanted to get things going. I guess we've started this year with a bit more of a forward-looking aspect. We've talked about the trends and previews. When I start thinking about some of the technology that's undergone a lot of change in the last year, quantum computing has had some interesting innovations. It's also an area that's an extension from your regular coverage on the cloud computing side. And there are some interesting connections between the 2 of them. But I want to start off taking a look at really what happened in this last year.

I guess it felt like there was less hype. Maybe the year before, there -- we were all in the quantum bubble and there was a lot of thought about what's happening. But this year, there was a lot of progress, but it didn't seem to be quite as urgent or as buzzy. What are your thoughts about really sort of what happened this year and where we're headed?

Gabriella Brown

Yes, for sure. So I agree. I think there was a lot of progress that was made this past year in the realm of quantum computing. And -- but yes, as you pointed out, perhaps with maybe less hype than we've seen previously. So in the past, we got the benefits of a brand new sort of technology. We were experimenting. Every new thing that came out was just so exciting because it was very new. You could prove something experimentally that hadn't been proven before. We had a lot of that and that was awesome.

And when you started to move through that experimental phase into a real research and modeling phase, so there's a little bit more work that happens there, but it's really a head down, and we're just going to work on this sort of thing. There's a lot less hype that goes around with that. There's a lot of small progress that's made and add that to really, really big implications.

Eric Hanselman

So is quantum computing getting boring?

Gabriella Brown

No. So I wouldn't say quantum computing is getting boring rather...

Eric Hanselman

Hopefully, we're a ways away from that.

Gabriella Brown

Yes. But it is becoming more of a business, right? It's less of the scientific research that you read about in the news. And definitely, that's still going on, but it's not weird, crazy technology that Einstein talked about in big terms. Now we're turning it into real technology that has business implications. And so the lens changes a little bit, and it moves away from that hype from reading about it in a scientific journal into how can we actually make this something useful on a broad scale.

Eric Hanselman

Well, and I think it's interesting to bring up the business pieces because we're now starting to see that next stage of investment that organizations are really starting to look at the business pieces of it. But there's also something that was an interesting point you brought up in our earlier discussions was the fact that we're now starting to see a lot of specialization. We're starting to see some level of regionalization. And in fact, we're starting to get areas that are starting to have to be centers of quantum computing research.

Gabriella Brown

Yes. So -- and just as we saw with Silicon Valley, we're kind of seeing something similar with quantum computing. And the interesting thing is rather than being centered around technology hubs that have been traditional technology hubs, yes, we're seeing quantum computing in places such as Berkeley. But we're also seeing a really big concentration of quantum companies setting up shop near their alma maters, their universities, these academic institutions, that have had really big progress on the fields of physics, quantum physics, entanglement theory.

So they're moving towards where these academic institutions are. And so we're seeing places such as Boulder, Colorado, which has done a lot of work academically in these fields, and we're having a lot of companies moving to those areas and partnering with some of the professors that still work there. We're having companies that are founded by former PhD candidates, former professors. And then they're really trying to tap into this pipeline of quantum trend workers that are coming out of these universities, they're trying to really tap into that talent pool. And so that's where they're choosing to set up shop is near these academic hubs rather than some of these other locations that maybe we might have seen previously.

Eric Hanselman

Well, it seems like it's one of the things we see often in technology, which is especially when we're not really distant from the actual research efforts that are backing this up. And especially with Quantum, where there is the actual -- the equipment aspect of this that you need to have people who understand, not only what are the basic principles, but you need people who actually understand how to construct the equipment because we're still in that stage of requiring relatively specialized environments, which to make all this work, a lot of different capabilities that are coming into play.

You've got, depending upon which of the quantum technologies you're working with, you've still got either high vacuum, low temperature systems or really sensitive electronics. A lot of things that you actually need to have the people who've got the experience really building this because the -- I guess there is still, while we're getting more business focused, still a lot more of the science lab aspect of it that still is involved.

Gabriella Brown

Yes. Certainly, we're bridging that gap between the science and the business, and we haven't shifted entirely. And given the technical nature, we may never shift entirely away from it. We still see a lot of technical skill needed in technology that we're familiar with that's been around for decades now. You need to have a trained workforce. And so partnering with academics that is kind of an important aspect of technology for sure.

Eric Hanselman

Well, it's one of those things that we're also in that stage of being able to provide remote access to computing impairments. IBM has had computing capabilities you could leverage remotely, Azure's partnership with Honeywell. I mean we're getting to that stage of getting at least mediated access to quantum capabilities, so.

I'm curious about what your thoughts are about increasing size. We've talked historically about the size of quantum computers in qubits, the how many actual quantum elements do you have at your disposal as part of that computing environment. And of course, we've got some big announcements from IBM and a number of others over the course of the end of this last year. What do you think about where we're headed in sort of what some of the practical aspects are of what we can do with quantum computers now?

Gabriella Brown

Yes. So as you mentioned, IBM announced their big qubit record with the Osprey and I believe that was back in November. It's at 433 qubits. So that is currently the biggest system that we're seeing out there right now and that's really exciting. Even a couple of years ago, we were working in the tens of qubits. And they still seem like very small numbers compared to the classical bits that you might be more familiar with. But as the size increases, the ability of these computers to actually work on problems, we need to be working in the thousands, tens of thousands, and up to be working on legitimate problems with these quantum computers, so scaling is definitely still needed.

And a lot of these companies that are working on qubit size, they have a really good road map. And I've been very impressed with how all of these companies generally are hitting their road maps. So they have a really good, solid trajectory. I do think, especially with such a hyped-up topic as quantum computing, people want to be very careful about not overpromising.

And so I think these goals are very realistic. And obviously, we can't ever predict the future, but it's exciting to look at these road maps and think, yes, this is doable. They're putting out these road maps for public consumption because these companies believe that they are doable. And so that's exciting.

We also, in terms of qubit size, we're dealing with some interesting sort of side effects of working with a new technology, and that's how to measure whether that new technology is effective. So qubit is broadly a measure of size of these systems, but there's also lots of other units that different companies are kind of focusing on.

One you might hear about is quantum volume and IBM deals with quantum volume quite a bit. And that record is actually being held. It was up in September of last year. It's a quantum volume of a little over 8,000 and that's just the measure of the capability and error rates of a quantum computer. And -- but then we also have other measurements like quantum coherence, which is like an indicator of how deep the quantum circuit can be. And we also have something known as CLOPS, which is circuit layer ops per second. It's kind of a measure of speed and so...

Eric Hanselman

So it's something like megaflop traditionally in classical computing.

Gabriella Brown

Yes. Yes, exactly. And so we're kind of trying to map on these classical units of measurement on to quantum computers and sometimes we're making up brand-new units of measurement. And there's no real way right now to tell what a good benchmark will be. We have some universally accepted ones, qubits are pretty popular. But we have governments that are actually working on this benchmarking problem so that we can kind of get a scale of how things are moving and working on these quantum benchmarking programs that [ Triant ] developed, what makes a quantum computer work and what we should be measuring for that quantum computer.

Eric Hanselman

Well, it sounds like one of these problems that we get into in classical computing, and it's interesting to see you drawing these parallels between trying to do classical quantification in quantum computing. Even today, we've got ongoing debates about the best way to benchmark and what you're benchmarking, and is it floating point performance, is it computational operations, is it -- so maybe this really is an indication that if we're in the middle of these debates of benchmarking, in fact, this is something, it's saying that we are maturing, the quantum technology is getting better.

Fundamentally, we still need to figure out coherence, how long can our environment hold together to actually finish a computation and then what that interconnectedness of that capability is in quantum volumes. You were talking about sort of some of the practical limits. There are still -- you were talking about legitimate applications and still the thing that always gets brought up is cracking encryption technologies with shared keys and Grover's and Shor's algorithms and things like that, which, in theory, we're not at a level of qubits or quantum volume to do that yet, who knows.

But yet, there are things that are practical today, the thing that's -- one of the aspects near and dear to my heart, there's a lot of chemical simulation that's being done. It doesn't need a ton of qubits, but it's one of these things that -- I mean, it seems like we're finding applications that can prove out some of the capabilities. Are there practical aspects there that you think are important and impactful?

Gabriella Brown

Yes, for sure. And you're right, proving out is a great way to describe this. So a lot of the quantum companies that I talk with, they are partnering with customers. And the first step is always to prove out these possible applications of these quantum computers. Some of them were at a qubit level, a capability level that they can start tackling already. Some of them, it's just how can quantum computing help your business, help tackle problems and there's a lot of thought that goes into that right now.

You mentioned chemistry and chemical modeling. Modeling is definitely a really great use case of this technology and simulation. There's a lot of exciting things. Health care is one of the big ones that's talked about often, which ties in pretty tightly towards the chemical modeling.

But the example talked about often is the human genome sequence, right? It took forever to get the human genome sequence. But with the added computing power that quantum computers can bring, what other problems can we tackle like that, that tackle quicker, what problems could we tackle that just can't be handled by classical computing.

There's a lot of work being done actually in the automotive industry. So they've been looking at that from a quantum perspective for modeling, for simulations, how can we design things in a more efficient way. And these are really just the starts as with any new technology. I don't think anyone could have predicted all the uses of the standard Internet that we use now.

Like I don't think the original classical computers could have -- the users could have predicted everything we've chosen to do, found to do with these technologies that we have now. So I'm really excited looking forward the things that we can't even imagine right now that may come about because of this new technology in the next couple of decades.

Eric Hanselman

That's always the way with new tech, right? We've got a set of things we think we can do with it today. We apply a lot of our previous knowledge and then get into solving a lot of those new and interesting problems. But we do have this sort of tied today between quantum and classical computing. And I mean so much today, you still -- you were talking about proving out. In fact, getting -- ensuring that if we run a problem on a quantum computer, we can prove that we got the same result on a classical computer to prove that, in fact, we're actually doing the same kinds of things. But where do you see that relationship heading?

Gabriella Brown

Yes. So I think the consensus seems to be that Quantum is inherently different than classical computing. It will tackle different sorts of problems. It likely won't replace the classical computing that we know. And I think the current goal is for them to supplement one another. Even within a single problem, maybe a classical computer runs part of it, they shift that over to a quantum computer to run another part of the problem, and then they can shift it back to classical computing and finish it up.

And I know there's a lot of lines of research being done on this intersection, this kind of hybrid of quantum and classical. And so with that, it's likely that they'll develop together in kind of the symbiotic relationship rather than quantum coming in and just negating any benefits that classical computing had. They'll probably grow together and allow us to do different things in a more maybe energy-efficient way, time-efficient, product-efficient way as well.

Eric Hanselman

Kind of like the relationship we have with accelerators today like FPGAs and GPUs, and solving those same problems of being able to split out the part we want accelerated and the part that the traditional CPU can actually handle. So what should organizations be planning for around quantum computing?

Gabriella Brown

So what we've been seeing a lot, there's still plenty of investment that's happening in the technology. And so there's obviously continued interest. Over the next couple of years, again, looking back to those company road maps, we're still in the let's-make-this-thing-bigger phase for the next couple of years. And with each new iteration, with each bigger system, we're going to see kind of incremental increased uses in these computers and in this technology.

And then we'll hit this point that's kind of generally known as quantum advantage, which is where quantum is going to really just take off where the capability of these computers is going to exceed what classical computing can do. And so that's kind of the tipping point that businesses are kind of on edge for, waiting for. And again, no one knows exactly when it's going to hit. But these road maps are really exciting to see, the qubit numbers go up, to see these current capabilities go up.

One thing we've also been seeing is a lot of kind of quantum handholding. So quantum computing companies will work with their customer's really guided process as they develop applications, use cases for technologies, and that's probably likely to continue for some time. It's still a new technology and not very many people are trained in how to build these technologies, how to implement them or even aware of what they can do exactly.

So there's a lot of kind of PR going on between the quantum world and the general business world trying to start easing these bigger companies into this process of working with quantum computers, seeing how they might be useful ideating and figuring out ideas for what they can be used for. And so we'll probably see some of that for a while as well with a shift towards kind of this more independent approach to quantum computing and quantum technology.

We do have some companies that are out there working on developing interaction sorts of points between regular coding technologies that people are more familiar with, say, Python, and some of these classical computing programs that people are used to using and then how to apply those skills to quantum and they're trying to create products to bridge that gap so that we don't need humans to do that, right?

It's kind of embedded in the technology. So technology is to make quantum technology easier to use. So that might be worth looking at as well. It is products that use that approach and especially where it can be difficult to try and find people who are trained in quantum, and most of those are taken up by quantum computing companies, right? And so it would be hard for a non-quantum company to go off and hire a quantum expert right now because most of the quantum experts work at quantum companies. So those technologies are really doing a good job bridging that gap.

And then over the next year, probably expecting to see some new records set and it's slated to be a pretty big year, assuming all targets are being hit. So record set on the hardware side, new stuff coming out from the software side. And then kind of this emergence of the idea of a quantum Internet, so this connected capability of quantum computers to talk to one another. There should be some new progress being made on that over the next year as well.

Eric Hanselman

Yes. There's some interesting shifts in technology. But I think the point you made about a lot of these onboarding engagement, starter kit kinds of things are interesting parallel to what we've done in artificial intelligence and machine learning because those are things that, originally, you needed a lot of specialized knowledge, now, you've got environments that are much easier to use.

You've got developer tools so that, that end of the consumption economy can start to actually onboard these capabilities. It's interesting to hear that -- identifying that there are the Python tools to be able to leverage quantum capabilities. It seems like that aspect is really looking to help to get quantum as accessible in as many different environments as possible.

Gabriella Brown

Yes, I would say so. I mean, one of the big PR issues that quantum faces is one of the things that makes it so exciting, but it does seem very complex, right? And for a good reason, right? When you talk to people about physics and then you bring up the word quantum, everyone kind of just gets a glazed over look on their face often. And it is a very complex scientific principle.

The technology is very complex. You see the pictures of these quantum computers and they look very complex, and it can be a little intimidating to even computer engineers, people who are used to working with very complex technology. But then they look at quantum computing, and they say, "Oh, no, that's too science-y. That's too much physics. I'm an engineer, I like computers."

Eric Hanselman

Still in the realm of exotics in terms of where the technology is.

Gabriella Brown

Yes, exactly. And so helping to bridge that gap, bring it more down to earth, and that ties back into our original discussion about hype. One of the things with maybe the less hype that we've been hearing about is to try and, in some ways, normalize quantum computing in a good way, right? You want to try and make it maybe less wild, less whacky and make it seem more real so that people feel more comfortable using it and implementing it.

Eric Hanselman

All right. Getting to practical use, which, it sounds like we're getting environments that can start to do that, but not quite boring yet, right?

Gabriella Brown

No, not quite boring yet.

Eric Hanselman

Well, this has been great. Thanks, Ellie. I appreciate all the perspectives. We are, unfortunately, at time. There's so much more that we can be talking around this. Thanks very much for all the updates. And I will point our listeners to your research on the research stream.

Gabriella Brown

Awesome. Thank you.

Eric Hanselman

And that is it for this episode of Next in Tech. Thanks for staying with us. And thanks to our production team, including Carolyn Wright, Ethan Zimman and Syed Wajih Abbas on the marketing and events teams, and our studio lead, Kyle Cangialosi.

I hope you'll join us for our next episode where we're going to get the latest and greatest from the Consumer Electronics Show with a couple of the team that are just going to be back from Las Vegas and all of the craziness that is CES. So I hope you'll join us then because there is always something Next in Tech.

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