However, not everyone agrees with Huang that quantum computing is so far away. In fact, one fellow tech leader has an extremely different perspective.
As he sees it, this technology isn't just growing quickly - it is here already.
Quantum computing may initially inspire visions of highly complex algorithms in those who aren’t overly familiar with it. But at its core, quantum computing simply refers to an advanced type of computing that can perform tasks at a significantly higher rate than the current classical computing systems.
"The laws of quantum mechanics allow qubits to encode exponentially more information than bits," the U.S. Department of Energy said on its website. "By manipulating information stored in these qubits, scientists can quickly produce high-quality solutions to difficult problems."
Quantum machines leverage quantum mechanics principles, utilizing quantum bits of information known as qubits, to solve problems at speeds unattainable by traditional computers.
As the current AI revolution accelerates, the need for more and faster computational power is rising, increasing interest in quantum computing technology.
Many companies are working on quantum computing, including smaller, pure-play, publicly traded stocks like D-Wave Quantum, Quantum Computing Inc. (QUBT) , IonQ Inc. (IONQ) , and Rigetti Computing, Inc. (RGTI) .
We recently spoke at length with D-Wave Quantum CEO Alan Baratz.
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When he says there is no quantum computer that will be able to do anything useful for 15 to 30 years, he is clearly out of touch with reality.
Now, the more nuanced response is that there is more than one approach to quantum computing. I think the problem we have is that Jensen is only really familiar with one approach, and his comments, while maybe on a longer timeline than others might say, isn't totally wrong for the approach he has in mind.
But D-Wave took a different approach and as a result, we are commercial today. So how can you say that there's any truth in the statement that quantum computing is 15 to 30 years away from useful applications when we have them in production today.
TheStreet: I was about to ask how you respond to the assertion that quantum computers are not yet ready for mainstream application, but you just touched on that.
Alan Baratz: Yes, I can give you some examples. NTT Docomo is using our quantum computers today to optimize cell tower resources. With this approach, they are able to support up to 15% more smartphones per cell tower.
Ford Otosan, a joint venture of Ford Motor Company (F) and Koc Holdings (KHOLY) in Turkey, has been working with our system to optimize how they build automotive bodies, reducing a computation that used to take up to a half hour down to seconds.
Pattison Food Group is using us for workforce scheduling, an 80% reduction in the time to schedule the workforce. These are all real business applications that have been built and are being used based on our quantum computers today, and there are many more that are in process.
TheStreet: Now, just quickly, before we move on, can you elaborate more on why someone like Jensen Huang would have a view of this industry? You’ve provided a lot of evidence against it. So why would he be saying things like this if there's so much evidence to the contrary?
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Alan Baratz: First of all, Jensen said he's talked to all the quantum computing companies. It's not true, obviously. What likely happened is that he's talked to companies like IBM (IBM) or Google (GOOGL) or Amazon (AMZN) , and he's gotten their perspective on quantum, and they're all working on a form of quantum that still faces many, many challenges before they can become commercial.
So he just hasn't taken the time to dig into the quantum industry broadly to understand the different approaches. Or he's just assuming ‘Oh, well, what IBM says must be true,’ right?
Well, excuse me. They have a vested and biased interest as well in their approach to quantum. But that's just not the entire quantum industry.
TheStreet: Overall, how far would you say that we really are from practical, large scale quantum computers?
Alan Baratz: I hate to say this, but we have practical, large-scale quantum computers today. D-Wave systems are 5,000 qubit quantum computers, and that order of magnitude larger than any other quantum computer available today. And they are practical.
Not only are they solving business problems, but we've also demonstrated the ability to solve materials simulation problems in minutes that would take well over millions of years to solve on the fastest supercomputers in the world.
By the way, they happen to be massively parallel GPU systems. Just think a little bit about that. We are solving problems today, practical problems today on our quantum computers that massively parallel systems based on his processes cannot solve. Scale practical quantum is a reality today. But again, to be specific, because we got it. Jensen got himself into trouble by not being specific.
There are different approaches to quantum D-Wave's approach is very different from IBM's, Google's and Amazon's. They're very different approaches, and our approach has allowed us to become commercial much faster. Their approach is still going to take some time.
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TheStreet: D-Wave has been at the forefront of quantum computing for many years, as you've made explicitly clear. Can you share what you believe are some of the most significant advancements that you've made in quantum computing recently?
Alan Baratz: First of all, we're on our fifth-generation quantum computer today. Over the last roughly 15 years, we have gone from 500 qubits to 5,000 qubits. So an order of magnitude increase over about 15 years. Now that's on a kind of advancement scale much faster than anybody else in the quantum industry.
Everybody else in the quantum industry has been working on their systems for maybe 8 to 10 years, and they've gone from maybe 30 qubits to 50, maybe 30 to 80, right? So we are advancing these systems quite fast.
We've gone from 500 qubits to 5,000 qubits. We've also increased the connectivity in our systems. The early systems had connectivity of six. Each qubit was connected to six others in our current systems. Each qubit is connected to 15 others.
That means we can solve larger and more complex problems, bigger systems, more qubits, more connectivity, larger and more complex problems. We've also increased the coherence time in our systems significantly.
Now the coherence time is the time that the qubit remains in the quantum state. By increasing coherence time, we're also able to solve problems faster.
We're able to solve larger and more complex problems because we have bigger systems, and we're able to solve those problems faster because we have longer coherence time. Finally, we've increased the precision with which our systems operate, so that we can specify the problem parameters more precisely to get better solutions. Bigger problems solve faster with more precise solutions.
TheStreet: Now I'm a little curious. How can businesses and industries outside of tech, let's say finance, health care, energy, just to name a few examples, begin to take advantage of these quantum computing advancements?
Alan Baratz: I'm going to talk about D-Wave because that's what I know best and care most about.
We make our systems available through our Quantum Cloud service. As a result, it's very easy to get access to our quantum computers. You do not need to go buy a quantum computer. You can access it through our Quantum Cloud service.
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Our Quantum Cloud service is not only performant, but available and reliable. In fact, we provide service-level agreements on the availability, reliability, and the performance of our systems. Nobody else does that because they don't have systems that are large enough and stable enough. They have research points right now.
But nonetheless, you can access our systems through our Quantum Cloud service. And most of our customers do access the systems that way. We also have a professional services team that will help you to build out your applications to leverage our quantum system.
In addition to a cloud-based service offering and our professional service. If you want to buy a quantum computer, we support that model as well. We've got a pretty complete business model.
TheStreet: Thank you. Now, one thing I'm also curious about that I feel like isn't discussed as much in the industry space is quantum error correction, which as I understand it, remains a significant hurdle, at least for some companies. How do you see D-Wave addressing this issue, and more broadly, how critical is it for widespread quantum adaptation?
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Alan Baratz: So D-Wave's approach to quantum called annealing does not require error correction. We are able to solve hard problems today without error correction. We could do a little bit better with error correction, but we do not need it.
There's a very fundamental reason why that's the case. It has to do with the fact that when errors creep into our computation, it does not destroy the computation. We may not get to the optimal solution, but we'll typically get to a good solution.
We can always restart the computation from that point to continue on to optimality. Error correction is not an issue for our approach to quantum everybody else.
Their approach, called gate, does require error correction because errors are much more prevalent in those systems, and as soon as an error creeps into the computation, it destroys the computation.
Typically, today, you can perform tens of computations before errors start to show up. That's nowhere near enough. You need to be able to support hundreds of thousands or millions of computations.
Error correction is absolutely fundamental and critical to everybody else's approach to quantum computing. We have a long way to go before we get to full error correction.
Now, the Google Willow announcement was a good step forward in that arena. This was really the first time that somebody demonstrated on an interesting scale, the ability to ever correct a qubit. But that's the beginning. There's a long way to go.
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Most don't believe we're at least seven more like 10 years away from true error correction. And it's absolutely essential for gate model quantum computers. And in some sense, that's part of why, when Jensen says 15 to 30 years, if we're talking about the approach to quantum that requires error correction, he's not totally off base.
TheStreet: Another question I have is on quantum computing scalability. As I understand it, that's also been a challenge for the industry. What steps is the D-Wave taking to overcome any scalability challenges?
Alan Baratz: Until recently, our approach to scaling has been more qubits on a chip. I talked about the fact that we went from 500 qubits 10 or 15 years ago to 5,000 qubits today. That's all been by putting more qubits on a chip. We still have some headroom in that arena.
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But we are also now working on multi-chip integration. Right now we use superconducting technology, which means we live inside a dilution refrigerator to run at millikelvin temperatures.
Well, a dilution refrigerator can support more than a single chip. It can easily support tens of chips. As we start interconnecting chips, that will be the next wave in scaling for us. And we've already started working on that technology.
It's pretty exciting. If we're at 5,000 qubits on a chip, if we put four chips interconnected in a fridge, we're already at 20,000. So we are on the path to grow the scale of the system, not just by looking at more qubits per chip, but starting to interconnect chips.
TheStreet: You've recently written that there is massive potential for artificial intelligence (AI) and quantum to work together, advancing limitations of today's classical computing capabilities. Can you tell us a little more about that, and what you and what you see for these two industries working together in the future?
Alan Baratz: I think there's a lot of opportunity today to put AI together with quantum. And I've talked about this before, but the idea is simply that there are problems where each can bring value to different parts of the problem. For example, using AI to predict product demand in the future, and then using quantum to optimize the supply chain to meet that demand.
This is an example of the two working synergistically together, and there are many use case areas where we can leverage that kind of synergy. Now, beyond that, we are also working on how you could use quantum computing to speed up and reduce the energy required to do model training and inference, which is very significant because, obviously, energy consumption is a huge issue right now on GPUs and AI.
We've got early results that are quite promising in that arena, but we still have some more work to do before we're really prepared to come out and talk definitively about the results. But we think this is an area that could be quite important as well.
TheStreet: I am also curious about the ways in which commercial clients are using D-Wave's quantum technology. Can you give me a brief rundown on who's using it and the advancements they're making?
Alan Baratz: I talked about a few companies already. NTT Docomo is using it for cell tower resource optimization. Ford Otosan is using it to optimize body assembly for automotive vehicles. Pattison Food Group is using it for workforce scheduling and last-mile routing for e-commerce delivery.
We have a relationship with Mastercard (MA) working on how to optimize loyalty rewards programs to improve uptake. So we have work going on with a number of companies across a variety of different industries.
TheStreet: Are there any industries that you think could be using quantum technology a lot more than they currently are?
Baratz: Just about every business has hard optimization problems that they need to solve. And this is where Heather West, an analyst at IDC, recently said that optimization could be the killer app for quantum.
This is where we excel. Our approach to quantum is very, very good at solving optimization problems. In fact, there's recent results that show that not only are we good, but that the other approach is not very good at solving optimization. This is an area where we almost exclusively Excel.
Any company that has hard business optimization problems they need to solve, which is pretty much everyone should be taking a look at us.
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