What changes when quantum computing is mainstream?
Quantum computing is on the far reaches of science, using technology that accesses aspects of matter at quantum scales where physics almost overlaps with magic.
Classical computing is simple: deterministic. You have something, or you have nothing. Quantum computing is complex: you can have something, or nothing, or both something and nothing at the same time. If that’s hard to wrap your head around, you’re in good company. Even Richard Feyman, 1965 Nobel Laureate in Physics and one of the founders of quantum computing famously said, “I think I can safely say that nobody understands quantum mechanics.”
But we’re seeing major advancements in quantum computing today. You can now write a program and deploy it on quantum computers from anywhere. And D-Wave says that it’s doubling qubits every 2 years.
In this episode of TechFirst with John Koetsier we’re chatting with Alan Baratz, president and CEO of D-Wave Systems, one of the very few companies on the planet that sells functioning quantum computers.
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(This transcript has been lightly edited for length and clarity.)
John Koetsier: What changes when quantum computing is mainstream, or … perhaps, normal? Welcome to TechFirst with John Koetsier.
Quantum computing is on the far reaches of science, right? It uses technology that accesses aspects of matter at quantum scales where physics seems to almost overlap with magic. Classical computing is simple: it’s deterministic, you have something or you don’t. Quantum computing is complicated: you have something or nothing, or maybe both something and nothing at the same time.
If that’s hard to wrap your head around, you’re in good company. Even Richard Feynman, who was a 1965 Nobel Laureate in Physics and one of the founders of quantum computing famously said, ” I think I can safely say that nobody understands quantum mechanics.” But we’re seeing major advancements in quantum computing today. You can now write a program. You can deploy it on quantum computers from anywhere on the cloud.
And to dive into what changes when quantum computing goes mainstream or almost becomes normal, we’re chatting with Alan Baratz, president and CEO of D-Wave. Welcome, Alan!
Alan Baratz: Hi, John. Thank you and it’s a pleasure to be here.
John Koetsier: It’s a real pleasure to have you. I almost interviewed you guys way back when I was writing for VentureBeat, and missed that opportunity, but I’m happy to have you here. So, let’s start kind of at the beginning because quantum computing is maybe getting a little more normal, but it’s not normal yet — it’s still on the far reaches, it’s probabilistic not deterministic. Can you explain what that means and what its significance is?
Alan Baratz: Yeah. So, first of all, John, since you said ‘far reaches but maybe getting a little closer’ … let me start by saying I’m often asked when will quantum computers be able to do something useful, deliver significant positive value to some business? And most folks that you ask will say five to ten years.
I’m unique, being from D-Wave, we’ve taken a very different approach to quantum computing from everybody else. And I can tell you the answer to that question is: today. With our systems, we are already making a significant positive impact on businesses, and I’d kind of love to walk you through that.
But I do want to answer your question. So—
John Koetsier: No that’s perfect. I mean, that’s a great way to start. You have the cloud-based access to D-Wave computers, you’ve got 250 apps up and running, you’ve got customers who are using it, who are building things. So we’re going to dive into all that, what that looks like, and we’re also going to dive into where that’ll go over five to ten years.
How much more normal will that be? Will that be just cloud accessible by any app? Will that ever make it to my home set-up, your home set-up or not? But let’s maybe start at the beginning then, and talk about what a quantum computer really is, and how it’s different than a classical computer.
Alan Baratz: Yeah. So at the highest level, quantum computers use quantum mechanical effects to perform computations much faster than they can be performed classically. And so, the easiest way to think about this is with a conventional computer, information is stored as bits — zeros or ones. With a quantum computer, we use qubits rather than bits, and qubits can be a zero and one — zero and one at the same time.
So if you think about this for a minute, your problem, when running on the computer, is able to see all possible solutions at the same time. Because all the bits are in a zero and one state all at the same time, and so the system sees all the possible solutions.
And so this is kind of infinite parallelism, and that’s really what gives quantum computers its computational power.
John Koetsier: It is really amazing. It is impressive. It also means that a quantum computer doesn’t always arrive at one answer, or maybe never arrives at one answer, correct? How do you deal with that?
Alan Baratz: Yeah, so that’s back to the notion of probabilistic. So you’re absolutely right. Today, all quantum computers — whether it’s the D-Wave quantum computer or quantum computers from any other vendor — they are all probabilistic.
And what that means, is that you submit a problem to the quantum computer and you will get back an answer, and the answer will be correct with some probability.
Now what’s interesting about quantum computers is that in many cases the problems that you’re trying to solve on the quantum computer will exhibit good solutions in addition to the best solutions. And so, what’s interesting about these probabilistic systems is you submit a problem and you get an answer back. With some probability, it’ll be the best answer. And with a higher probability, it’ll be a good answer.
And so, the name of the game is to build systems for now that have a high probability of giving you really good answers, or even the best answer.
Now, what makes these systems probabilistic is errors. It has to do with the fact that because we’re using quantum mechanical effects, the systems can be influenced by our environment. And our environment can kind of introduce errors into the computation — things like electromechanical effects, or light, or heating, or cooling. These can all introduce errors into the computation.
And so the probability of getting a correct answer is related to how much error is introduced. In the case of the D-Wave system, we are much less sensitive to errors than any of the other systems. And for that reason, we are actually able to deliver really good solutions, and in many cases optimal solutions, with a very high probability of success.
John Koetsier: Very, very interesting. It’s kind of funny, it’s almost real-world in a human sense, in that there are very, very challenging problems that we know we can find bad solutions to; we know we can find good solutions to; we know we can find great solutions to. It’s almost impossible to know if we have the perfect solution.
Alan Baratz: It is, and for the following reason: think about typical problems that businesses want to try to solve … maybe shipping optimization, or logistics management, or employee scheduling, or protein folding for a new drug discovery, right?
These are all very, very hard problems in the sense that the compute power required to solve these problems grows exponentially with the size of the problem. But in many cases these are very large problems that need to be solved, and so coming up with the optimal solution is out of the reach of classical computers, right? And so that’s where we start looking at quantum computers.
Now what’s interesting is, as you said, how do we even know if we got the optimal solution? Because we can’t go try to run it on a classical computer to get the optimal solution because that would take too long, right? But the interesting thing is what businesses do today, is they run them on classical computers knowing that they’re probably not going to get the optimal solution anyway because that would take too long — just looking for a good solution, right?
John Koetsier: Yeah.
Alan Baratz: Now, these can be run on quantum computers — in particular, on the D-Wave quantum computer because we’re the furthest along with this — and they can compare and see, wow, that’s actually a better solution than the one I got out of the classical system, so I’m going to go ahead and use that. So that’s another way to think about it.
John Koetsier: That’s a really good segue actually, because what I wanted to get into is variables. Leap 2 is your cloud service, and you are now allowing people to solve problems with up to 1 million variables. That’s a lot of variables.
I’m sure there’s many variations of the traveling salesman problem in there and other things like that, but what kinds of challenges are people trying to solve? What kinds of business problems are people trying to attack with quantum computers that require that many variables?
Alan Baratz: Yeah, so let me give you a couple of examples, okay.
Protein folding and protein packaging is one example. I mean, with very large proteins … think about COVID, a COVID vaccine, right? What I want to do is I want to create a new protein that’s going to attach to the COVID virus protein, right, that will render it harmless. Now the problem is, when I attach a new protein to that, I need to understand how it’s going to interact with the COVID protein, right?
And that is the problem of protein folding or protein packaging, and this is a really hard computational problem. And for real-world molecules, it’s outside of the reach of what classical computers can do. And so we’re working with a company that’s actually used our system to develop some new proteins that they’re now synthesizing for potential use in COVID therapeutic trials.
Another example is Volkswagen. Volkswagen has used our system for a number of years now. Initially they were working on global traffic optimization, global traffic [routing]. But with the new Advantage quantum computer in Leap 2 able to use hybrid solvers — where we combine classical compute with the quantum compute — they’ve been able to optimize a portion of their manufacturing process. So this is at the tail end of their manufacturing process when the vehicles end up being painted, they call it the ‘paint shop scheduling application.’ And the idea is you want to schedule the painting to minimize the paint changes and the nozzle changes, right?
Because whenever you make a change, there’s waste.
So, what they’ve been able to do is use our system to come up with schedules that they can show have significantly less waste than the schedules that were created using the classical computers, and they’re now working on moving those new schedules into production use in their environment.
John Koetsier: Well, it’s really neat to hear about real-world applications. It’s also a test of a company to not just acquire a customer, but keep a customer. And you mentioned there ‘for several years,’ so that’s impressive. Can you quantify, like how much faster are they in getting vehicles through the paint? How much waste are they saving? I don’t know if you have those numbers top of mind, but can you quantify at all?
Alan Baratz: So it’s measured in reduction in waste. Yes, I know the number, it’s in my head … they are reluctant for me to talk about it, but I can tell you that from a percentage perspective, it’s very significant. You’d probably be surprised at how high a percentage improvement they’re able—
John Koetsier: Okay. So let’s talk about the hype cycle, because that’s been a thing obviously for quantum computing for quite some time. You’ve already answered this partially, because you’ve talked about what you’re doing, that you have customers, repeat customers, multi-year customers, and growing.
But if we look at that standard hype cycle for technology, you’ve got this peak of inflated expectations, this trough of disillusionment — the standard Gartner thing — slope of enlightenment, a plateau of productivity. Where is quantum computing? Now that may be different for different companies in the space as well. Where’s quantum computing generally? And where do you think D-Wave is?
Alan Baratz: So, I do believe it is very different for D-Wave than for anybody and everybody else, because D-Wave is the only company that chose annealing as our architectural approach. And I’m not going to get into the definition of annealing versus gate model—
John Koetsier: Nobody would understand it anyways. It’s all good.
Alan Baratz: But suffice it to say, annealing — A: scales faster, and B: is much less sensitive to errors. And that’s why we’re able to solve real business problems at production scale and get really good results. That’s not possible with gate model systems and likely won’t be possible for five plus years.
So, if you ask where we are on the hype cycle, I think that D-Wave is actually starting to come back up the slope — I guess you’d call it the slope of enlightenment.
Because, you know, we went through a period with our last generation system, the 2000-qubit system, where there was some interesting research that could be done on it, but it wasn’t powerful enough yet to solve real business problems. Now, with our new Advantage quantum computer, the 5,000-qubit system with a new Leap 2 cloud service and the ability to use a hybrid solver that solves problems with up to a million variables — we’re actually able to support real business problems.
And so now we’re bringing customers in, helping them understand which of their problems are most appropriate for the quantum computer, and then helping them build those applications so that they can start getting benefit from them. I think we have a long way to go before you’ll see anything like that in the gate model space.
John Koetsier: Let’s talk about those 250 applications you’ve got running on D-Wave cloud systems then. Can you give us a general flavor of the kinds of apps, maybe one or two of the more surprising ones?
Alan Baratz: Yeah. So, first of all, I will say that those 250 applications, I call those ‘early applications’ because they’ve been built up over the last couple of years. These were real business applications, but running at a small scale.
Because remember, you know, until two months ago our largest processor was only 2,000 qubits, not 5,000 qubits, and even with our previous generation hybrid solver, we could only go up to about 10,000 variables. And so we were solving problems that are relatively small scale. It’s only now, with the new Advantage system and the new hybrid solver, that we can solve production scale problems.
But, look, these problems range from a whole host of optimization and satisfiability problems, and we’ve talked about optimization before. It’s things like scheduling and logistics management, you know, you could apply it to manufacturing plant floors. You could apply it to airlines. You could apply it to shipping. So, lots of optimization type problems.
There are problems in machine learning. We’re not really better than GPUs for training models, but there’s one element of machine learning that we are quite good at, that’s called feature selection. And so, if you’ve got a problem that you’re trying to solve, a model that you’re trying to build, and you’ve got lots of features that you need to train on … well, typically there’s not enough horsepower to train on what real large number, hundreds of features.
So what you want to do is you want to try to figure out if there’s a small set of most representative features in that larger set. That’s called feature selection, and we’re really good at the computation to kind of narrow down the number of features so that you could train more efficiently. So, we add some applicability and machine learning.
We have applicability in material simulation. We can’t simulate all materials, but right now we’re quite good at simulating magnetic materials. And in fact, about a year and a half ago, we did a simulation of a very specific phase transition in magnetic materials. This is called the Kosterlitz-Thouless phase transition. The theory behind it won the Nobel prize in 2016. It’s computationally very hard to simulate. We’ve been able to simulate it on our system 3 million times faster than it can be simulated classically.
So, you know, the system is actually quite broadly applicable.
John Koetsier: I love that you said ‘until two months ago,’ or whatever the timeframe was, ‘we were only 2,000 qubits.’ Because I’m clearly no expert at all in quantum computing, but I’ve been following the field for probably a decade more or less, and I remember when it was 16 qubits or 32 qubits.
Alan Baratz: Yeah, honestly, at D-Wave we’ve done a really good job of doubling qubit count roughly every two years. If you look back over the last 15 years, we’ve gone from 2 to over 5,000. Now, if you look at gate model, they’ve gone from 2 to about 50 in the same period of time, right? Which is evidence that our architecture is inherently much more scalable. And we see that continuing. We don’t see any fundamental impediments to continuing to scale into the future.
John Koetsier: Is there a Moore’s Law around that?
Alan Baratz: Well, nobody’s developed a formal Moore’s Law. The gate model systems are … developers are kind of in too early a stage to be able to do anything like that. One of our investors actually did map our qubit growth against Moore’s Law in the early days and said, ‘You know, you’re tracking exactly on Moore’s Law, right?’ So, we’ll see.
John Koetsier: Moore’s Quantum Law. Very interesting.
Okay. So, you’ve launched the cloud, we’re going to talk a little bit more about that. We’re also going to talk a little bit more about the 5,000 qubits. But what apps do you anticipate over the next year now that you’ve got the 5,000 qubits, you’ve got the cloud accessibility, somebody can write it anywhere, run it whenever they want, and you can handle a million variables right now and presumably more in the future — what apps do you see coming in the next year?
Alan Baratz: So, we are engaged with both end-customers and partners to help identify and build out the apps.
Now, I can’t go into the details of the customers because we don’t have signed agreements with many of them yet, but it’s in the banking industry, it’s in the pharmaceutical industry, it’s in the transportation industry. Those are areas where we’ve got direct customer relationships.
And then we’ve got partners that are regularly coming to us. Accenture, for example, is a partner of D-Wave. They come to us and say, ‘Look, we’ve got a customer that has a problem that can maybe benefit from your system. Can you help us work with them to kind of flesh this out?’ So, really right now is a pretty broad, broad range. And in fact, our business model as a result of that, has just transitioned.
So, up until now, we were really selling just platform access, right, buy time on our Leap quantum cloud service, right? Now, we are actually selling a phased engagement with a customer that starts with an initial application investigation and exploration, right? There’s a charge associated with that, and our team engages with them and helps them. And then once we identify the applications, we transition to a second phase where we actually help them build those applications in proof of concept. And if that’s successful, we transition to the third phase where we help them do an initial small deployment in their environment. And then if that’s successful, a full deployment across their environment. And this is a new business model for us, and so far, it seems to be resonating really well in the marketplace. We kind of announced this at our Advantage launch. We announced something called D-Wave Launch — not to use the term launch too [frequently] — but this is this model where customers can come to us and we’ll help them identify the right application, build a proof of concept, and get it deployed into production.
John Koetsier: I think that’s a really smart model because as far as D-Wave has come, as far as quantum computing has come, it’s still very new. And having people understand what they can do with it, how they can integrate it, how they can use it with the other parts of their compute platform is still really, really challenging. I want to talk about cloud and your move into cloud.
Cloud obviously has been huge in the past decade. Quantum computing is now in cloud, you’re in cloud. It’s kind of funny, because if you look at a quantum computer — and there was a point in your history where you were selling a box, right? And it wasn’t really a box, it was kind of room-scale, and it’s kind of steampunk as well, right, it’s very arcane the way that a quantum computer looks and how it’s built — but now you’re selling access on the cloud. And how has that gone for you?
Does on-prem even make sense in the future? Or is cloud the way to go for quantum computing?
Alan Baratz: So, let me tell you why we made the transition and then how it’s going.
So, first of all, we use superconducting technology to build our quantum computers, and that means that the chips need to run at millikelvin temperatures, right? Almost absolute zero. And that means the chips need to be inside a refrigerator. Now, that kind of dictates the size of the box.
The chip is a thumbnail chip, right, like any other microprocessor, but it needs to be in the fridge, and the fridge determines the size of the box. And so the fridge itself is a cylinder that’s maybe four feet in diameter by six feet tall, and that’s inside of a 10 foot by 10 foot box for electromagnetic shielding, okay.
Well, these systems are pretty pricey and they have some requirements associated with on-prem installation. And, you know, back two, three, four years ago, we weren’t at the point where we had a proven business ROI on the systems. We’re now there, for some applications, and building it out for more, but at that point in time we didn’t really have it. So there are only so many customers that are willing to pay many millions of dollars for a system that doesn’t have a proven ROI, right?
And so it became clear, when I joined the company three years ago, that we needed to broaden the base of customers, right? And the way to do that was to give them easier access and allow them to buy-in incrementally, and then grow as the capabilities as a system and their need for using the system grew … and that meant cloud. So, as a result, we built our Leap cloud service.
Leap is a very comprehensive cloud server. I actually think it’s the best quantum cloud service anywhere in the world. We have real-time access to the quantum computer, that’s unique for us. You submit a job, it’s queued, it runs, you get your results back; you have to schedule a block of time in the future; you don’t have to wait for somebody to run your job.
It’s true real-time queued access. We have demos. We have reference examples with live code. We have a full developer environment integrated into Leap fully configured with all our tools, Python-based, you can write your application right there and submit it. We have training modules. We have community areas where developers can meet one another, ask questions, get answers. So it’s a full-featured quantum cloud service, and it’s worked out really, really well for us.
John Koetsier: Mm-hmm.
Alan Baratz: First of all, anybody can sign up. You sign up, you get a minute of free time. If you’re willing to open source your work, you get — you can renew that minute every month. So that’s to build the developer community, developer ecosystem.
We now have over 18,000 developers that are on the system, over a thousand of them regularly using the system.
And then secondly, it’s become our basis for kind of selling access for commercial use. And our revenue has grown nicely since we launched it back at the end of 2018. And now we’re adding the professional services capability around that that I described previously, and we feel really good about the prospects for the future.
John Koetsier: I wonder if there’s ever a time when you can write an application, a business application, or frankly, any other type of application, and you can natively, seamlessly access resources in AWS for classical computing and — oh, this problem, huh, it’s a quantum problem — seamlessly access resources in Leap in D-Wave’s cloud.
Alan Baratz: So, first of all, I do believe that quantum computers will likely always be co-processors.
In other words, you know, I would never run a user interface on a quantum computer. Classical systems are really good at doing that. The quantum computers are for the hard computational core, a little bit like GPUs.
So I always think of the quantum computers as co-processors. Well, now, if you think about it, if you go to AWS, for example, there are GPUs that you can [tap] into, in addition to CPUs. And you can build applications that use the combination of the two.
In fact, in our Leap cloud service, when you run the hybrid solver, we’re actually spinning up AWS, CPU, and GPU instances for the classical piece of that, and then submitting the quantum piece to our quantum processor back in Vancouver. So we’re kind of doing it today.
John Koetsier: Amazing. Very, very interesting. You’re at 5,000 qubits. You talked about this — well, let’s call it the new Moore’s Law, the Quantum Moore’s Law — we’re just inventing the term right now on the fly, it’s the Quantum Moore’s Law. You’re doubling every two years.
You’re also finding new ways to increase connectivity between qubits, which is really, really interesting. Non-obvious and kind of reminiscent of the human brain, right? It’s not just the number of neurons, it’s how many interconnections between those neurons that you have. I want to ask you a bit of a hybrid question.
Like, how many qubits is enough? And maybe the answer is never enough, I don’t know, maybe there is an answer to that. But also what’s the impact of this connectivity and how’s that helping compute?
Alan Baratz: Yeah. So, at the highest level, think of the qubits as the size of problem you can run, and think of the connectivity as the complexity of the problem that you can run, okay. So we absolutely want to continue to increase qubits and we want to continue to increase connectivity. And so when we went from our previous generation system, which was 2,000 qubits with each cubic connected to six others, to our new Advantage 5,000-qubit processor with each qubit connected to 15 others — so, over double the number of qubits, over double the connectivity — what that allowed us to do was to run problems three times as large and three times as complex as what we could previously run.
Now, with respect to how many is enough, you know, I probably shouldn’t say this, it’s kind of like money, right? [laughter] It’s never enough, you always want more, right? But I guess that makes me look quite capitalistic, but in any case … right now today, some of our commercial problems have hundreds of thousands of variables, okay.
We have only 5,000 qubits, so we can’t map those problems natively, right? That’s why we need the hybrid solver. The hybrid solver takes the full problem. What it’s doing is looking for the hard computational core of that problem, and sending that off to the quantum processor, sends the results back, and then we iterate. So that’s how we operate today.
Now the larger the quantum computer gets, the larger the hardcore can be. Ultimately, maybe you don’t need the front end that’s looking for the hardcore because you can map the entire problem, but it’ll take a while to get there.
John Koetsier: Can you explain the difference between a complex problem and a large problem?
Alan Baratz: Yeah. So, let’s suppose that we are scheduling employees throughout the week and the work day, right? So, the size of the problem may have to do with the number of employees and the number of time slots that those employees are being slotted into, right? I mean, are you scheduling them on an hourly basis, or a half-day basis, or a full-day basis? That’s the size of the problem, the number of employees and the number of time slots that you’re scheduling them into, in a one week period, say.
The complexity of the problem is things like constraints on when an individual can work, or which individuals can work with one another, or can’t work with within another, or these individuals have these set of skills and these have those set of skills, and I need to make sure that I have at least people covering both set of skills in any time slot, right? And that’s related to the complexity of a problem.
John Koetsier: That makes sense. That makes a lot of sense. Okay, cool. Almost done. There’s a couple more questions here.
One that I want to get to, we just saw this past week, that a Chinese company that has a photonic computing project claimed quantum supremacy. We saw about a year ago, Google, I believe, claimed “quantum supremacy.” First of all, what does that mean? It sounds like ‘we’re the best in the world’ — it’s not exactly that — what does that mean? And, secondly, what do you think about those claims? Do you agree with them?
Alan Baratz: Okay. So, first of all, I struggle a little bit with the terminology—
John Koetsier: Yes.
Alan Baratz: Some people talk about “quantum supremacy.” Some people talk about “quantum advantage.” But whichever you talk about, it’s all about how fast the quantum computer can solve the problem versus how fast a classical computer can solve the problem.
And so if you can show that a quantum computer can solve a problem in a reasonable amount of time — seconds, minutes, even hours — but any classical computer running any algorithm would take years, or tens of years, or hundreds of years, or thousands of years … okay, that’s a quantum advantage. And depending on the time differential, maybe you would call it quantum supremacy.
So, for example, if it’s three minutes on the quantum computer and a year on the classical computer, maybe call it quantum advantage. If it’s three minutes on the quantum computer and a hundred years? Maybe you’d call it quantum supremacy. Okay. Now, Google’s supremacy result was interesting, but it was also disputed, if you remember—
John Koetsier: Yeah, I do remember.
Alan Baratz: That ‘well, wait a minute, if you were to use our Summit processor’ — which is a multiprocessing GPU-based system — ‘and run it in this way, it wouldn’t take tens of thousands of years, it would only take three days.’ And then Alibaba came out with a similar analysis. So, I’ll leave it to the listener to decide whether that was quantum supremacy or not.
In the case of the announcement from China, I’ll be honest with you, I don’t know enough to be able to answer the question. Honestly, I saw it just today, probably like everybody else. But here’s what I can tell you: it doesn’t look like they’re actually trying to do a computation, okay.
John Koetsier: No, it’s a very arcane problem that they solved with this very odd computer. That is very true.
Alan Baratz: Yeah. And it’s not even clear it’s a computer, you know, versus a detector of some sort. So, I think we all need to learn more before kind of weighing in on that one.
John Koetsier: Good answer. Alan, this is TechFirst. I do these podcasts for my Forbes column, but there’s a particular focus. I do these about tech that’s changing the world, and innovators who are shaping the future. Kind of two questions here, and you can kind of mix and match between them. How will quantum computing change our world? And personally, how are you shaping the future?
Alan Baratz: Yeah. So first of all, in the nearer term, the impact of quantum computing is more efficient operations, for many, if not most things that we do, right? I mean, we’ll be able to solve these hard problems that businesses run on, and come up with better solutions that may allow them to run more efficiently, maybe higher productivity. Maybe there’ll be some costs reduction and as a result, some market benefit for consumers.
But it’s, you know, in the near term, it’s efficiency.
Longer term, it’s potentially transformational, right? I mean, when we start talking about things like designer drugs, right, where the drug I take for my — maybe not just one, but collection of ailments, is designed specifically to me, and the one you take is designed specifically to you. Or the ability to do really good global climate modeling, right, and to be able to kind of operate based on an understanding of climates around the world.
I mean, I think these are the kinds of things that represent more transformational opportunities. As far as me, I’m thrilled to be the CEO at D-Wave. We have delivered amazing technology. We have an outstanding team, you know, world-class in many different areas: whether it’s super conducting, fabrication process design, or superconducting circuit design, quantum circuit design, cryogenics, software to be able to solve hard problems in a hybrid environment, building cloud services. It’s just an amazing team. I love being responsible for this company and this team … and we’re doing what we can to continue to help solve hard problems.
John Koetsier: Excellent. Well, Alan, I want to thank you for your time.
Alan Baratz: My pleasure. Thanks for the opportunity.
John Koetsier: For everybody else, thank you for joining us on TechFirst. My name is John Koetsier. I appreciate you being along for the show. You’ll be able to get a full transcript of this podcast in about a week at JohnKoetsier.com, and the story at Forbes will come out right after that. Plus the full video is always available on my YouTube channel. Thank you for joining … until next time, this is John Koetsier with TechFirst.
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