Welcome to the studio. I am here with Alan Baratz, the CEO of D-Wave Quantum. Welcome to you, Alan. I know this is your first London Tech Week. How are you finding everything?
First of all, thank you for having me here. I appreciate the opportunity to speak with you. It's amazing. Tons of people, lots of companies displaying, government officials talking. I found all of the discussions interesting, insightful. Obviously, I run D-Wave Quantum, so I'm interested in quantum computing. We are participating in an AI session and talking about the interaction between quantum and AI. But once again, very interesting, insightful conversations.
I want to touch a little bit on that. I mean, London Tech Week brings together pretty much every corner of the tech world, AI, fintech, climate, defense. For people watching this, where does quantum fit in that conversation right now? Is it still the futuristic one in the corner, or has it finally earned a seat at the main table?
It absolutely has earned a seat at the main table, but there's a caveat. So, I have to kind of raise the discussion up a little bit. There are two important approaches to quantum computing. One is called annealing, and the other is called gate model. And I'm not going to go into the details about how they work or how they differ, except to say they are each good at solving different types of problems. So, annealing quantum computers are very good at solving business optimization problems, things like workforce scheduling, manufacturing plant floor optimization. Gate model quantum computers are very good at solving quantum chemistry problems for drug discovery or materials design. So, these are two important approaches to quantum computing. They're each good at solving different classes of problems. At D-Wave, we are what we call a dual platform company. We're actually the only company in the world focused on both annealing and gate model quantum computers, as well as, you know, giving us the opportunity to address all the different use cases for quantum. Now, to answer your question, annealing quantum computers are commercial today, and they do have a seat at the, you know, the big table because we actually have Fortune 100 companies that are using our systems in production today as a part of their daily business operations, delivering significant value. Gate model quantum computers are not there yet. They're still very much in the R&D phase. They require error correction to do anything really useful. They need to be scaled out to do anything really useful. And so we're probably 5 to 7 years away from seeing commercial gate model quantum computers.
So that's interesting. So why have you at D-Wave Quantum decided to, you know, to go across both?
So we actually started with annealing. And in fact, today, you know, we're the only commercial annealing quantum computing company. We're far ahead of anybody else in that space. We have over 4,500 qubits. We've demonstrated quantum supremacy on annealing quantum computers. We do have customers using us as a part of their business operations. So we started there. We started 20 years ago, and we're on our sixth-generation annealing quantum computer. We jumped into the gate model space about 4 years ago. So we were a little late getting started in gate model, but we recently acquired a very interesting company spun out of Yale University that has some revolutionary gate technology that has really accelerated our gate model program. Really moved us from probably the back of the pack to the front of the pack on gate model, as well. So now we're leaders in both annealing and gate model systems.
Okay, interesting. I mean, we've heard it here at London Tech Week, and just everywhere you go, the broader tech narrative right now is almost entirely dominated by AI. Does that frustrate you or does that actually create an opening for quantum as a way to talk about what quantum can also do?
Yeah, it's really the latter. It creates an opening for quantum. So, I think there are a number of ways that quantum and AI can work together. First of all, let's talk about quantum aiding AI. We can use these two technologies together to solve a problem. Each of them focus on the part of the problem that they're best at solving. So, for example, you might imagine AI predicting product demand for some new products in the future, and then quantum optimizing the supply chain to meet that demand. Here, AI is solving the portion of the problem that it's best at solving, quantum is solving the portion of the problem it's best at solving, they're working together. In addition, you can envision quantum actually improving how AI works for both model training and inference. And in fact, we've got the first commercial example of that with our annealing quantum computers. We're working with a Japanese pharmaceutical company called Shionogi. They developed an AI model to generate new molecular structures for human drugs. What they found was that the model was pretty good in that it would generate structures some of which were good for human drugs, many of which were not. They then retrained that model using our quantum computers. They got a more accurate model. 10 times more of the molecular structures generated are well suited to human drugs using the models trained with our quantum computer. And they were able to train the model faster and use far less electricity. So, quantum can actually deliver much more energy efficient AI model training and inference.
Okay. And I want to come back to that energy efficiency. To what extent is this happening where quantum is aiding or improving AI? Because it's not something that you hear talked about very often.
Well, it's still early days. I mean, both of the two approaches that I talked about where the two are working together to solve hard problems, as well as where quantum is making AI better and more energy efficient, it's early days. We at D-Wave have the first and only commercial example of that with the Japanese pharmaceutical company, and even that is early stages. We're trying to bring that capability to other companies in other industries, but it's very early days.
I know that you publicly disagreed with Jensen Huang when he suggested quantum was decades away. I had to ask you this, Alan. And you said he had a misunderstanding of the technology and that D-Wave was commercial today, as you've just been telling us. You know, we saw that Continuum IPO happen, I think last week or the week before. Is 2026 the year that we are finally going to see the momentum start to build more in investors' minds when it comes to quantum?
Well, so look, our momentum has been building in the investors' mind for 3 years now, since we went public.
Our stock price grew dramatically through 2025, up again in 2026. So, we're seeing an enormous amount of focus on quantum by the investor community and interest in quantum by the investor community.
What's been that driver, that inflection point? You know, why did that happen in 2026?
I think it was awareness of the fact that on the one hand, quantum is real today.
Our annealing systems are commercial today. They are solving important business problems and delivering an ROI today. So, there's that awareness that quantum is starting to become commercially viable. And then, just in general an awareness of quantum. I mean, I think if we go back to 2024, nobody really knew what quantum was, understood what it could do, and now there's a much greater awareness of what it is, what it can do, what it can do today, and the huge market opportunity for quantum in the future. You know, nearly a trillion-dollar market opportunity as we look out 20 or 30 years.
Okay, I was going to say what is that time frame? That's what it is, 20 or 30 years.
To get to on the order of a trillion-dollar market opportunity, 20, 30 years. It's, you know, approaching 10 billion today.
Okay. Wow. Okay. And when you come to an event like this, which industries do you think should be moving fastest on quantum, and which ones are just kind of sleepwalking their way through it?
Well, because annealing is the only commercial approach to quantum today, and annealing is good at business optimization problems, you know, we're talking about, you know, a broad array of industries that have problems that require these kinds of solutions. For example, one of the largest airlines in the world we're working with, one of the largest chemical companies in the world, BASF, we're working with, one of the largest pharmaceutical companies, we're working with, one of the largest telcos, we're working with.
