Technology, Connected - D-Wave Quantum Computing - Behind The Scenes

Episode Date: October 1, 2025

Murray Thom, VP of Quantum Technology Evangelism at D-Wave, joins us to break down how D-Wave’s quantum computing technology (as used by NASA, VW, Lockheed Martin) is tackling complex, high-stakes problems across industries. Learn how D-Wave’s unique use of quantum annealing helps solve real-world challenges, from logistics optimization to drug discovery and traffic management.Murray explains how D-Wave’s hybrid quantum-classical systems maximize computational power by leveraging quantum effects alongside classical computing, enabling optimizations that traditional systems simply can’t match. Discover why D-Wave is trusted by organizations, including NASA, to handle high-dimensional, multi-variable data, delivering immediate benefits in efficiency, productivity, and operational insight.From the development of the first quantum computer to real-world applications, Murray explains how businesses are gaining a competitive edge and solving their toughest challenges with quantum technology.Stay tuned for practical insights, key D-Wave milestones, and a look at what’s next in quantum computing.🔔And please subscribe.--TIMESTAMPS(00:00) Introduction to Quantum Innovation(01:22) Meet Murray Thom: Quantum Expert from D-Wave(02:30) How Quantum Incentives Drive Industry Collaboration(05:30) Breaking Down Quantum Complexity for the Real World(07:30) Murray Thom on Joining D-Wave 22 Years Ago(09:02) The Role of Quantum Physics in Real-World Solutions(10:07) Major Milestones in D-Wave’s Quantum Journey(12:36) Understanding Quantum Annealing: A Practical Guide(18:47) Key Benefits of D-Wave’s Quantum Annealing Technology(21:40) D-Wave’s Efficient Power Use: 15kW Explained(23:45) Quantum Computing Through a Pokémon Analogy(25:35) Real-World Impact: Workforce Scheduling with Quantum(31:45) Quantum Systems in Sports Team Optimization(33:02) Tackling Complex Industrial Problems with Quantum(34:48) Portfolio Optimization: Quantum vs. Classical Methods(40:53) What Does a D-Wave Quantum Computer Cost?(42:11) Exploring the D-Wave SDK(44:45) Partnering with D-Wave: What to Expect(45:58) Quantum Collaboration with IBM(49:39) A Question for IBM on Post-Quantum Cryptography--Follow Thinking On Paper:Twitter: ⁠⁠⁠⁠⁠https://x.com/thinkonpaperpod⁠⁠Instagram: ⁠⁠⁠⁠⁠instagram.com/thinkingonpaperpodcast/⁠⁠⁠⁠⁠YouTube: ⁠⁠⁠⁠⁠youtube.com/@thinkingonpaper/videos⁠⁠⁠⁠⁠LinkedIn: ⁠⁠⁠⁠⁠linkedin.com/company/thinkingonpaper⁠⁠⁠⁠⁠--Past guests on The Thinking On Paper Show include: ⁠⁠⁠⁠⁠Ciaran Murray⁠⁠⁠⁠⁠ (web3 for journalists), ⁠⁠⁠⁠⁠Torrey Smith⁠⁠⁠⁠⁠ (Robotics For Medicine), ⁠⁠⁠⁠⁠Jason Lynch⁠⁠⁠⁠⁠ (Quantum Computing), ⁠⁠⁠⁠⁠Joe Fitzsimons⁠⁠⁠⁠⁠ (Quantum Computing), ⁠⁠⁠⁠⁠Dana Sydorenko⁠⁠⁠⁠⁠ (Gaming), ⁠⁠⁠⁠⁠Don Norman⁠⁠⁠⁠⁠ (Humanity Centered Design), ⁠⁠⁠⁠⁠Mercina Tillerman Perez⁠⁠⁠⁠⁠ (Circle & Crypto), ⁠⁠⁠⁠⁠Tyler Adams⁠⁠⁠⁠⁠ (Blockchain), ⁠⁠⁠⁠⁠Todd Haselhorst ⁠⁠⁠⁠⁠(Blockchain for Logistics), ⁠⁠⁠⁠⁠Vince Yang⁠⁠⁠⁠⁠ (ZK Proofs)

Disruptors and Curious Minds. Welcome to another episode of Thinking on Paper. My name's Jeremy. This is Mark. We get to talk to the people that are building tomorrow rooted in today. We've got a great guest to talk more about quantum. We're quantum curious. We're actually quantum aware.

We're not quite quantum savvy yet. Hopefully we'll continue to progress down the timeline and see where we get. But this particular guest has been in the quantum world for, I think he said, 22 years. years in our pre-production chatty. He's been with a company for a really long time. I'm stoked to digging deeper, Mark. How are you feeling this morning? Yeah, also quantum fan boys and girls as well in our community.

Total fan boy. Total fan boys. Of this company of D-Wave Quantum. Yeah, we're talking about the real world applications of quantum, drug discovery, optimization, material science, scheduling, fault detection, traffic congested, and something I learned, I want to get into Liverpool Football Club and the Fantasy Football League, which I'll leave that as a little teaser for later, something I found. But yeah, our guest today is Murray Tom. He is vice president of quantum technology evangelism

at D-Wave at Quantum D-Wave. So welcome to the show, Murray. Hey, good to be there. Thank you for being here. Yeah, thanks for being here. Do we, Do we want to start with the carryover question, Mark? Do you want to start there? And then I've got a T-off question that might be interesting for us to dig into some background as well. I will do that. So all our new listeners, we like to thread our shows together. So we have a guest, leave a question for the next guest.

And we start off from there. So last week's guest, Year Cleper from blockchain, he left the question for Murray, which is about incentives and how quantum technology companies can you. use incentives to foster collaboration to push forward the real utility of quantum over the coming years. Okay. So yeah, this is a really interesting point because, you know, I'm working with customers to help them build value with a new disruptive technology.

And coming from a physics background myself, I've actually got an electrical engineering degree and honors in physics and an observative math in my background. I often think about the world in terms of like, you know, physical processes, right? So we, like, when we're trying to help our customers solve problems, we kind of want to set them up in a circumstance where water is flowing downhill, as it were. I don't want to be trying to push water uphill, kind of thing. I want the water to flow downhill. And so, you know, a big innovation that we had in the development, our systems, and a

realization that we had was that no quantum computer was ever going to be used in an application on its own. And I mean that in a, like, you know, yes, we have to use computers to get data into quantum computers and back out. But I mean more that the tasks of applications were going to be shared between quantum and classical computers because they each have their strengths. They had kind of complementary strengths. And the brilliance about building quantum classical hybrid technology for supporting customers and applications is that it provides a number of benefits related to these kinds of incentives. And it really focuses on aligning our incentives as a full-stack quantum computing provider with their incentives in terms of trying to get quality of solution and business value.

So, for instance, they're working with us because, you know, they've got challenging problems and need workforce scheduling, maybe logistics routing or production scheduling. And they're trying to get to, you know, better quality solutions that allow them to, you know, be more productive, to be more effective in their operations and more competitive. and we are trying to develop the technology in the way that provides that maximum positive impact for their organizations. When we're building a quantum hybrid technology,

the benefit is that for us, the quantum computers that we develop are like the least expensive resource that we can push the work onto. So D-Wave has got a whole program here of like several teams working together to maximize the impact of quantum computing to impact those customer applications

that we're working on. and we actually direct the development and design of the quantum computers in that direction, you know, because we're working with customers. So our incentive is to develop them so they have greater impact and they develop as quickly as possible in that direction. And we're constantly trying to push work onto the quantum computers. And that's exactly what the customers are looking for. You know, they're solving a problem.

Ideally, they want the greatest impact from technology today and in the future. And that sort of aligns our incentives as we're trying to, maximize how quantum computing is providing a role there and providing an impact and they're looking to leverage that impact to bring value to their end customers. So, you know, I think that in as much as, you know, companies are systems of people and technology working together, right, and towards common goals that we as individuals can't do. And as much as we can help those organizations and those organizations working together to have their incentives align, you can get very powerful. you know, results that emerge out of that.

Yeah. And one thing, too, that is a theme, there's a lot of themes. We've been doing up over 100 shows, I think, in this, in this podcast. And the themes that keep emerging are, you know, technologies that come out. Like a lot of people, when technologies come out, they have to be rooted in something we do today. So there's a bridge that people can go, oh, that's kind of familiar. I can kind of do this and do that.

But there's a step you have to take. And that step has to be, it can't be super complicated. Like blockchain's dealing with this right now where they're like, oh, we're going to onboard a billion people into a very complicated process that does something kind of similar to what database is doing. People like, why am I doing that? Right. So the incentive piece is really interesting.

And I love the Be Like Water analogy. That's super cool as well. Mike, go ahead, Mark. Did you have? Well, no, I was just going to comment on your illusion to blockchain. and the problem that blockchain has with this, the terminology and the narrative and explaining the benefits

and everyone's obsessed with the technology. And in fact, if you're not a developer, you don't understand, so you don't understand 99% of these websites. And I have to admit that I thought that that was as bad as it gets, but quantum takes it to another level of not being able to understand. If you focus on the technology and all of your websites explaining the technology,

And you see this on LinkedIn and you see it with the people who, a lot of the people who speak about quantum. It's just, it's intimidating. It instills fear. It instills fear in like very smart developers. It's like, okay,

I can't, I can't go near this because it's too complex. And yeah, that's just my observation for what you're saying about blockchain. Well, Murray, let's, let's go,

go here. So a lot of people are like quantum, brand new thing that's happening. Quantum computing, like, oh, I'm hearing a lot about it. It must only be a couple years old. I think you told me earlier, told us earlier, you've been at D-Wave working in quantum

computing for like 22 years, right? Is that it? All right, I want to ask you one question. It's kind of a two-part. Tell me what you, give me, give me some details about your first day working at D-Wave. And then give me like two or three milestones that you hit throughout that, or D-Wave hit throughout that 22-year period that were like massive aha moments in the development of quantum.

Okay. I might zoom in on you, too, because people are tired of looking at us. Okay, so Okay, so my first, I mean, definitely my first day at D-Wade was September 16th, 2002, and it was just awesome to have a job in quantum computing. You know, I was so excited. I mean, I'm glad you asked me that question because I haven't had a chance to reflect on that for a while. I mean, you're kind of like floating through the office and you're like, yeah, I'm working at a company that's building quantum computers.

And I think there was, you know, when you, you know, we need to. you, when you're in school and you start learning about quantum physics, it's, it's just kind of like mind-blowing. It's a little bit like, what is, what does Morpheus say in the Matrix? He's like, you look a little bit like Alice Tumbling down the rabbit hole, you know, accepting what you see because, you know, that's just in front of you kind of thing. So a lot of quantum mechanics are people who are basically like asserting like, this is the way that the world works. And you're just kind of like scratching your head being like, is that for real? And what I love about the idea of quantum computing is that it's about, hey, let's take what we know about quantum physics and the description of how

the world works, and let's turn that into useful work. You know, if we can make an engine out of quantum physics that can do something for us, well, that's like a new opportunity to understand it in a way that we didn't understand before. And that's actually very much the way that it's materialized. We understand the machines we're building and what their poor capabilities are, a lot better today than when we started. And it's given me an opportunity, you know, personally. And I think a lot of people, you know, who now have access to our quantum computers around the world to, you know, start interacting with quantum physics. I mean, part of the reason why it's so unfamiliar for us is because we don't get to interact with it in our daily lives. I mean, at a quantum level, I mean, we are

all classical systems or quantum, but they behave classically. And so now that we have quantum computers in our LEAP Cloud Network, you know, in several places around the world, available to 42 countries around the world, there's all sorts of folks who can now say, hey, I'm going to, like, create a puzzle for it. I'm going to try to solve a problem with it. I'm going to see how it reacts and how it's different than classical computers and how I can get better solutions out of it. So that's fascinating. And, you know, in terms of the milestones in the last 22 years, it certainly has kind of felt like it's actually been like working at several different companies. You know, in the beginning phases, I was working on building quantum computers themselves,

designing for them. I built several different subcomponents, some of the early generations of them. And I've built a few quantum computers by hand. So I think that there was this incredible validation moment when we had sold our first system to Lockheed Martin, and they installed that system at the Quantum Computing Center at the University of Southern California and the Information Sciences Institute. That was really a point where the rubber hit the road and terms of talking with, you know, customers and learners who are trying to understand how to

program the systems and teaching them everything we could about quantum computing and working with them to try to bring the value. So that was a very, you know, in a technology company, that's always a major moment. And, you know, when I'm advising startups who are doing deep tech, you know, I often try to talk them through, you know, how to get through that early technology development to get to that customer integration point. And then I think that, you know, when we progress to the point where we put our quantum computers on the cloud, you know, it was this opportunity where it was the first real-time cloud access to quantum computers. So, you know, people could go to our leap quantum cloud platform. They could begin programming it and they would get an answer back

like in seconds. So they like now it's becoming much more of like a tool, like a learning tool, a development tool where you could start putting ideas in, getting answers out. And then, and that's really, I think, where engagement and innovation gets like accelerated. So, So yeah, I think that was, those are kind of two key phases in the development of the systems in my career here. I love that. I'm glad you still remember the first day and the date. So it obviously made a big impression on you.

Yeah. Yeah, the rabbit hole Alice in Wonderland thing is that that's super cool. So quantum computing isn't, there isn't just one way to do quantum computing, right? And based on what I understand about D-Wave, quantum annealing is kind of, of one of the is kind of the one of the main philosophies of of how you get you know a couple of of bits to entangle and and and make them do computing things right so annealing is is also like comes from kind of metallurgy right when you heat up a metal and you heat it up to a point to increase increase its ductility right to your ability to be used and flexible in different ways so talk

us through what quantum annealing is maybe try to relate it to to to the metallurgy stuff so we can, you know, extend that knowledge a little bit wider. Yeah, yeah, absolutely. Okay. So I'm also, you know, you mentioned earlier about talking with experts in quantum computing. I mean, as a field and as an industry, I'd have to say that like we tend to make it harder than it needs to be. There's a lot of explanations of quantum physics, which are kind of like, it took folks

a lot of years to build up familiarity with mathematics to be able to describe them. and sometimes it's, you know, you got to go back to the beginning of that journey and be like, well, what tangible things would I relate this to to make it more understandable? So I'm going to try to do that by saying that, you know, a quantum computer is just a device that uses quantum effects to accelerate calculations. That's all a quantum computer is. I don't even need to introduce superposition into that. It's just trying to use quantum effects as a resource.

And that's kind of an important idea to think about it. Like, we've got a computer and they use, you know, chips as a resource. a resource to process information for us. So what kind of a resource does quantum mechanics represent? Well, it turns out there's different ways to use quantum effects as a resource and a computer, and the different ways that you choose basically determine the model that you're building. So in one method, in quantum media language, we're talking about right now, you're using quantum effects to allow the computer to move between solutions more quickly, right? So it accelerates a transition between solutions. So if I'm doing the optimization of a schedule for a grocery delivery

fleet. I've got a schedule right now. I can just start with a random schedule. You know, that's my current state, but I want to move to a better schedule from that. And the quantum effects can help me move to a better solution more quickly. Okay, so that's pretty understandable. And, you know, the other, just to contrast that with the other model, with gate model quantum computing, because D-Wave is building both models. In that model, you're using the quantum effects to allow the computer to store more information. So you're putting more information that quantum states are actually containing information for you. The trick there is that quantum states are famous for being delicate and collapsing. When the quantum states collapsed, then information you put there is lost. So if you're going to

use the quantum states to store more information, then you have to have error correction to kind of keep maintaining that information there to allow you to process over it. And depending on which choice you make, that actually really strongly determines which applications you're going to be targeting. If you're moving between solutions more quickly, that's very useful for optimization problems and applications and artificial intelligence. If you are storing more information, that's very helpful for, like, quantum chemistry, for simulating electrons and molecules, or for like fluid flow calculations. So that gives you a quick taxonomy in relatively plain language about quantum computing. And at D-Wave, we're famous for building quantum and e-link systems because we started so early. We got there ahead of

everyone else. And the idea came out of the Institute of Science, Tokyo, formerly Tokyo tech, and a couple of really, really brilliant professors there, Karawaki-san and Professor Nishimori. So essentially, you know, let's start connecting it to that metallurgical phenomena that you were talking about. In metallurgy, you know, we discovered metals, but to change their shape was very difficult to do unless we heated them up in a fire. So when you heat them up, just as you're mentioning, it becomes easier to change their shape by hammering them out. And then what happens is that when you cool it back down again, it actually, the metal takes on different properties depending on the rate with which you cool it. So if you quench it in like water or in like oil

really quickly, you just drop its temperature really quickly, then what happens is that like its internal structure doesn't change, you know, like everything kind of freezes in place. And so you, you know, it goes from this like soft state into like a crystalline state. And the crystalline state, like the crystals just occur and then they basically like their boundary walls hit each other and they're very internally frustrated. And that frustration makes the metal really hard. And that's useful if you want to make like a knife edge or a sword. Now consider it if we cool it down really, really slowly. the crystals start emerging, but they can actually, their boundary walls can actually move because

they're just on the edge between that sort of like forming and not forming those crystals. And so what happens is that although you've got these crystals boundary walls moving, the boundary walls can actually move through. And if they move all the way to the other side of a crystal, it actually absorbs the other like crystal site. And so you can, these crystals effectively grow and become longer and longer and longer than material. And that means that the internal structure of the material becomes less frustrated and it's softer. So that's great if you want to use it for wire to conduct electricity or for some sort of metal where you want to conduct thermal energy.

You know, transmission through the metal is actually a lot easier and it's also a lot softer if you want to draw it into wire. So fundamental answer is the rate with which you cool those metals changes their properties and how you can use them. So in quantum annealing, it's just recognizing that thermal, that the heat represents thermal energy. and the thermal energy introduces randomness, and then you just reduce that randomness,

and then you can kind of like settle into an unfrustrated state. Well, quantum effects, when you're solving an optimization problem, can be thought of similarly. They introduce randomness in the state. It starts exploring a whole bunch of different states, and as you gradually turn that back down, and it starts to localize, if you do that slowly enough,

not only can you get to a less frustrated state, you can do it faster than any classical system can. So the speed limit for quantum effects to reduce frustration, the speed limit is faster. And it's significantly faster than classical systems. So that was the idea that came out of Japan about building a system that could implement that. That idea was then tested in crystals in the United States.

This was in 1998, 1999. And then in 2005, we started, you know, we were hitting on these ideas. We realized that, you know, if we built a quantum annealing system, it would give us three huge advantages. One was it had practical applications in the near term in optimization and business, which is incredibly important. The second is you don't need to know quantum physics to program it. You can actually program it on Python using classical ideas. So programming accessibility is much higher. And the third is that they're much easier to build. Primarily, I mean, for a number of engineering reasons, but also because of the you don't need error correction in the same way because you're not

putting information into the quantum states. You're letting the quantum states, move you to better quality solutions. And I mean, I'm just asserting that, but you can actually see that it's true because the largest most highly connected quantum computers on the planet are quantum annealing systems. We've got 5,000 qubit processors online at a commercial scale right now with 15 way interconnectivity, which means every cubit is connected to 15 other neighbors. That's by far in a way, the largest, most highly connected systems. And we've got three of them in the field. And here in Burnaby, Canada, in Los Angeles, California. and in ULIP, Germany,

as part of our sort of worldwide quantum cloud network called LEAP. So I got an image on the picture. Yeah, yeah, yeah. That's one of our systems here. And there, I mean, it looks small relative to my hand, but it's actually like taller than I am. It's about if you're in the U.S.,

it's like 10 feet by 10 feet by 10 feet if you're in Europe, three meters by three meters by three meters would be the sort of dimensions. I could, every episode, Mark's going to giggle. I always throw my data center background out there. So I worked in kind of data center capacity planning and more on the infrastructure side. So that to me looks like a hot, cold aisle containment cabinet system within like a traditional data center. It looks pretty

similar to that. Well, and we did the industrial design that way precisely so that it's easier for folks to integrate. It's much more familiar. And in fact, I mean, I've talked to folks who managed data centers. And when we tell them about the like water and power requirements for the system, it's like, oh, man, this is going to be so easy to implement. It's, it's, it just takes like tap water basically to, uh, to, uh, to, to cycle through the system. And then the power requirements are like 15 kilowatts. So like that, wow, like one of those racks here that's in front of that system, sorry, for those who are listening in, you can't see this, but like a 19 inch rack that you would see in a data center, if you completely filled it with like CPUs or GPUs, it would consume like

a hundred kilowatts. Whereas our whole system, uh, consumes about 15 kilowatts, about a sixth of that. And what's fascinating about that is that this is our, actually this is a photo of an earlier system, but our fifth generation, we have five generations of commercial processors and we're working on our sixth right now that we have a prototype for.

Our fifth generation system uses 15 kilowatts, but our first generation system also consume 15 kilowatts. And that's not the case with like CPUs and GPUs. Like the GPUs are basically growing in their power consumption, each year. It's like 30 GPUs is the same power consumption as our whole computer. The reason the power is flat is because the processing is done in a chip, about the size of our thumbnail inside that computer room, and the chip consumes less than a millionth of a watt. So it's the metals that circulate the current have no resistivity, so it almost dissipates no heat.

So what that means is that as we're creating these new generations of systems, we have this really large growth in the performance capability of the new processors, but the power consumption profile is flat. And what's fascinating about that is that, you know, as we look towards quantum artificial intelligence, if we can take some of those workloads from, that are difficult and challenging for high performance computing systems and then move them onto quantum computers, we can fundamentally change their power consumption profile. And that's a really big concern as people are looking to how to leverage artificial

intelligence today. Oh, it's massive now. Like, Mark, just to chime on this. real quickly, like there are municipalities that are heavy data center municipalities around the world that, you know, like Ashburn, Virginia is like one of the hottest data center markets. And they ran into a power constraint issue where they couldn't build power infrastructure fast enough to accommodate the compute infrastructure that wanted to be there. And this could be really interesting as we, I mean, it's going to be a long time, I think, before, you know,

quantum will be above, you know, classical just in general. They'll have to be both for while, but down the road we could see it maybe even being a solve to the power issue in some form. Yeah, I mean, I think that, well, I mean, what we know now is that, let's say, like, let's use an optimization example. And let me put it in terms that are like very, very plain language. Let's say my son, you know, for a long time was really into Pokemon. So we'd be playing like Pokemon card games with one another. And that involved, that's a game where you're like playing a field of players with strategy.

and then you've got like a hand of characters that you're trying to optimize. So a classical computer is very good at saying, hey, this hand's not optimal. I'm going to take a card out and pull another one from the deck and then see if I can improve it. And then I'm going to take another one. And maybe it'll accept or reject that move one at a time. So one at a time card changes are very, very easy for classical computing.

And it can do that blazingly fast. But the trick is that if you have a lot of cards in your hand, and you're looking for a combination of cards that interact with. interact well together. That's a very difficult way to improve your hand. And a quantum computer is very good at sort of saying, hey, I can actually take six cards out and replace them with these other six. And when I do that, they'll be interacting with nine other cards much better in my hand. So it's kind of doing these non-local updates. And it's able to do that because it's using the sort of superposition across states to sort of see how to make sacrifices and gains

in a much broader way. It's really interesting. Like, it's the whole this idea of, there was a book a while ago. I can't remember who wrote it. It was called the adjacent possible. And it was like all of these things on the fringe of one thing

that could be combined in unique ways to make different things. So say like we got three boxes, three rectangles in this video, right? So say there are three pieces in my box, three pieces in your box, three pieces in Mark's box, where a traditional computer

we'll look at the boxes as a whole and go, yeah, we're going to replace Jeremy because he's not optimized with these other guys. But Quantum can look at the little pieces within the boxes and go, well, these two bits work with his bit great. And like, it's like the color palette, it has seen more possibilities in the color palette of something. Is that even close? Well, I would, I would, let's put this in the context. So, like, we haven't, like, a customer here in Western Canada, Pedersen Food Group. And they own like 13 retail grocery brands. and one of the applications that they're working with is in-store grocery scheduling. So, you know, they've got a store and working at, let's say, the Dilley Department,

and they're trying to decide, is Alice going to work in the Dilley Department, or is Bob going to work in the Dilley Department? And let's say they've only got one more space left, right? So they've got decisions to make. Is it going to be Alice or is going to be Bob? And the decisions are related to one another. So they know that, you know, they only want one or the other. to go in. So if they decide that Alice is doing it, then Bob has to be off, like, not

assigned to that task. And so when you're putting a problem into a quantum hybrid system or in directly into the quantum processor, you're telling it those relationships. So it knows, you know, only one decision should be on. It's the other one is off kind of thing. And just on its own, that's relatively easy to work out. But what's happening is that that assumes that everyone else has been scheduled under the deli department and the rest of the store. And the question is, and you've got all these decisions all over the store. And so you've got this question of like, well, what if I pulled three people out of the Delhi department?

Would I be able to cover the floral department of the pharmacy better? In which case, I want both Alice and Bob to be in there, you know? So it might make a sacrifice in between those two decisions by making them like a much bigger change elsewhere. And if you look at the combination of possible decisions you could be making there, it's enormous. So let's say, you know, I'm not. Amongst all those employees and all those different departments in the store, if you've got 300 decisions to make in assignment, well, the number of possible combinations is larger than the estimated number of particles in the universe.

So, again, if you're just, if you're taking someone off a shift and then looking through and putting somebody else on a shift, doing that local update that a classical computer can do, even if you can do billions of those a second, that's nothing compared to like the estimated number of particles in the universe, right? You're making a small impact searching through that space. and the quantum computer, it does this much better job of making these much more non-local changes. So when they're working together, what's happening is that the classical computer is taking in a very large industrial problem like one that Paterson Food Group was working with. It starts doing those local updates and quickly looking for better quality solution.

It's basically running downhill. And then it makes a call to the quantum computer on the customer's behalf and says, based on where I am, can you make some substitutions that move me in the space to an or better quality solution, but that's significantly different. And then it can start running downhill in both of those places. And then if one of them gets stuck, it can continue with the one that's better. So it's the searching combinations. Like, it's actually not a big system itself.

It's that it's the, it's all of the interactions, you know, and the decision space that it's searching for you. It can deal with the complexity of options, right? Like, it would consider, you know, let's say, what were your two names? the two names of the people? All right. So let's say Alice, you know, has been known. She's cut her hand on the deli slicer four times in the last three months.

We probably don't want her slinging sandwiches, right, during the day. But she's really good at detail in stocking in the back. And Bob is really forget. Are those some of the things like it's just the expansion of possibilities to consider? Is that why this system works so well? Yeah. It's precisely because as you're navigating through the space, there are these conflicting constraints.

So for instance, in the real problem, not all the employees are trained to work in all the departments. And then they know that the forecasting, like, varies each day of the week, like the number of employees they want to have based on demand. And that varies each week of the year. So there's that aspect to it. And then you've got like shift consideration. So you've got to schedule employees according to shifts by seniority.

And if they're working a night shift, they can't work in morning shifts the next day. So as you start gathering all of these things together, you know, you're creating a problem which is very difficult for humans to solve, but it's also very difficult for classical computers to solve. And so, you know, our quantum hybrid technology was able to bring a solution to Pedersen Food Group that's saving them a significant amount of time. So the folks building those schedules are actually saving many, many hours. And, you know, they're in their first phase of deploying that in their stores. I think they're up to about 60 or 90 stores right now, their workforce is expecting 50,000 hours of savings annually.

And think about that in terms of its impact on people and processes. These are the most senior employees in those locations. So now they're able to get out from behind their desks or grinding through spreadsheets and actually work with the junior employees. And so they're mentoring them. They're teaching them how to, you know, teaching them new, new skills and about the business. They're working directly with the customers. Now their most experienced employees are actually out interacting with customers.

more. So that's a huge benefit. And then, you know, also, you know, people are on a career journey. And if somebody moves on from that post that used to have a very negative impact on everyone else's schedule. But now it's much easier for, you know, a new employee to come in and learn that scheduling task and make sure that everyone's got a high quality schedule all the time. So it's, it's just fantastic working with customers, seeing, you know, quantum computers use that. and production applications, and then doing all of the engineering around that to make that easy. I mean, we talked about programming the systems, you know, and the complexity of blockchain you were talking about earlier.

I mean, the thing that I have learned is that no amount of computing power is going to overcome a product that's difficult to use. You know, it has to be easy to use. We've got to take complexity out of businesses with quantum computing. That's what we're doing. We're not trying to introduce complexity. So we've got to solve the technical problems and then help our customers solve solve their problems so they can get focused on what their business needs businesses need.

There was maybe it's the same solution but one of the very interesting D-Wave for me personally reports I read was about using D-Wave to pick the optimal Liverpool Football Club in the 2021-2020 season for various formations and there's all the calculations of how it worked this out and it gives you the optimal team and obviously that's a very, kind of niche idea, but you think it's like a new moneyball. You could use quantum computers to make the optimal fantasy football team, the optimal club teams in any sport. And it was the same processes, I guess, that you were speaking about. But moving on from the grocery company, is it the same solutions that you're using for shipping or for portfolio optimization? Is it the same,

Is it the same ideas solving those challenges as well? Yeah, it's the same quantum hybrid technology. And if you're doing, you know, in fact, there's actually a very close connection between picking players for a sports team and picking assets for a financial portfolio for portfolio optimization. Those formulations are actually very close. And so what, you know, what we do is that, you know, our customers are talking with us about this new emerging technology. And the focus here is that, you know, at the end of the day, we build solutions that help them with complex industrial challenges like workforce scheduling, like, you know, routing logistics and final mile delivery and supply chains, like production scheduling. Ford AutoSan, for instance, is working with us on optimizing schedules for like the manufacturing of a thousand cars. And they've managed to bring that down from like previously taking them 30 minutes or more down to five minutes, for instance.

those each time we work with a customer on those applications will work with their subject matter experts and they will say okay here's the description of the problem here's what I'm trying to optimize and here's the constraints I'm trying to meet I've got a series of decisions and I need to orchestrate those resources that network of resources so they can operate as effectively as possible and ideally in a way that you know is agile and reacts quickly to change and then we will create a formulation a description of the problem that is a computer program that they can then run in their application on their systems. And once it, you know, it takes the model of the business task they're trying to do and it takes their data about, you know, the list of available employees or the list of

available, you know, manufacturing machines and combines those together. That is basically like a description of all the decisions that they need to make. Not actually their business data. It's just like, here's a series of decisions I need to have made in the next. they pass it to our system, our quantum computers, and then our quantum hybrid systems, and then they'll start working over the decision space. So they see all problems as a series of decisions that are either happening or not happening, or like I have one and a set of decisions. And here's where, you know, quantum and classical become very complementary, is that, you know, if you're optimizing a financial portfolio, you can sort of be like, well, I'll buy 17 units or I'll buy a

million units, like you kind of scale it, I'll even buy a fraction of a unit. But when you're doing this, you know, the team, the players on the Liverpool Football Club, you can't get a 17th of a player, right? The players that they're on the team or they're not on the team. And that feature of things that are happening are not happening and the choice of which player you pick kind of affects the rest of the team, that's very difficult for classical computers. All classical systems struggle with that. And it's partly because, if they could continuously vary it, if they could just take a scoop of something like a 17th of a player,

they could start calculating gradients like slopes that would help guide them towards, oh, you know, a little bit of this player is good. Maybe I should get a little bit more of that player. That's what they might do in finance, for instance. And once you don't have that, the players there are they not there, there is no slope to calculate.

Then a key strategy is lost. and then because it affects the rest of the network, that creates this very large interconnected decision space that they have to search through. And so there's kind of two main strategies to tackle there, which is either try to reduce the number of decisions you need to make or study the problem very intensely to detect structure so that you can make big moves in the space.

But no classical systems focus on moving through the space more quickly, and that's because they're all running on the same hardware. Right. It's always transistors at the bottom, whether you're using a CPU or a GP or something like that. And quantum systems have this like proven ability to move through that solution space more quickly. And that's what's, I think, really exciting. So, you know, working with customers to discover those applications, build a proof of concept that demonstrates that business value, and then work with them for production, deployment, and release.

I like that moving through the solution space quicker. That kind of gets me. I see this giant map of interconnections and like how to kind of make sense of all those possibilities quickly is really interesting. Can I ask you one thing related? I'm always really interested in and I want to try to make this simple, right? So there's a handoff between classical and quantum. You got to define the problem, right? Spend a lot of time figuring out what you're trying to solve.

But is that transition point? Is how we map traditional classical? to quantum? Is it involved in this like binary quadratic model? Is that like the encapsulation of the problem that you can package and translate to quantum? Is that how that works? Yeah. So, so the way to think about this is that when you're working on an industrial problem, let's say your packing cargo into a shipping truck or something like that. You've got, you've got some binary decisions to make, which is that am I going to put this box into the Alice's truck, or am I going to put this box into Bob's truck? Those are by hundred decisions. And then you've got some category decisions, which is that, like,

I need to orient the box. So I need to, like, rotate it, but it can only rotate a 90 degree increments. So there's that kind of aspect of how it has to move around. And then you've got continuous decisions, which is like, I need to slide along the bottom until it meets the next box. So those are the elements of the industrial version of the problem. And classical computers are very good at moving through the continuous variables. And our quantum computers, the machine instruction that they accept is a binary quadratic optimization form. It's called Cubo. It's actually got different names in different fields.

The Nobel Prize for Physics this year went to John Hopfield and Jeffrey Hinton. and John Hotfield proposed a neural network, which has that same structure. It's called a Hotfield network. So Hopfield network and cubos are the same. So when you're programming the quantum computer, you're just giving in an expression of a binary variables that are related to one another, and it's working out how to set those. But your industrial problem has a whole mixture of different things.

So what happens is that you're taking in the problem, the classical computer is using the strategies that it does best, and then it constructs a cubo of the problem, a binary optimization problem for the quantum computer that helps it. So it's not like you're actually fitting the problem into the quantum computer.

You're saying, I'm searching, like the algorithm is saying, I'm searching through this space. You know, if you can help me, you know, configure these decisions that will help me move through the space more effectively.

And then the quantum computer says, okay, well, why don't you try, you know, rotating your Rubik's cube in this kind of a way? And then it's like, oh, hey, that's helpful.

And then it, you know, it continues on using its classical strategies. So this is less about like, just real quick, but I know I'm, I know I'm commandeering a lot of the airtight, but just following up on that, that's almost like quantum computing helps you ask better questions. Is that like, or it helps refine? Yeah, the, if you were going to try to use that strategy, and instead of calling the quantum computer, you were going to call a classical computer. some of the queries you'd be asking it, it would struggle with.

It would basically give you bad suggestions. And so anybody who was working on that before would have avoided that strategy because it's so difficult. Right. They would have completely cut them off from trying that out. And a quantum computer makes that possible. So it's kind of, it really is the case that a number of folks are thinking about problems differently because they're using our quantum computers. and that's opening up all sorts of strategies that are bringing them quality.

Shout out to Julio, Tino, The Nexus, augment your thinking spaces. Check out the book club, and you can learn more about that. Mark, you got some thoughts. Where are you going with this? I just wanted to, so I think we've got an understanding of what D-Wave are offering. So if we can make this a bit more tangible, so for some of our listeners, I don't know, okay, and I might mention some companies here you're already working with. Maybe they already have quantum solutions in place.

but I don't know if like Oracle or Salesforce or Samsung or Netflix are watching this. Kind of like, what's the D-Wave relationship like? I mean, everybody wants to know, you know, how much does it cost? I've read like 10, 15 million. I don't know. But, okay, Salesforce, they have 20 million. They come in. They buy, and if you buy, they buy a D-Wave computer.

What's the process like? What's the next step? How does it look for them if they're. CMO is watching this or something. Well, if the Netflix CMO is watching this and they have a $20 million budget, absolutely give us a ring. That would be awesome. Is that nice?

A good budget? Is that a good ballpark figure or is that too little too much? It is, it's never too much, Mark. But the thing is that it's not where you need to start. So we're, the way they would experience this is that, so, first they would want to know like we're a full stack quantum computing providers so we both we actually build hardware ourselves but we also have a cloud platform for accessing it so that's like an application platform that we're building that allows people to build applications that call an

API and get high quality solutions and you know if they have a team that are working on those applications or if they're working with our team we're building those applications with a software development kit and SDK that allows them to program on the system with the system in Python. And the SDK is written in Python and C for speed. And then we have a team of folks whose expertise is formulating problems. So if they want to really, you know, we're frequently working with our customers and bringing them millions of dollars of value per year.

So they want to get those applications built quickly. And our launch team is really about helping them move through that journey as quickly as possible. It's not at all $20 million to get started. In fact, you know, everyone who's listening to this program in one of those 42 countries can log in and get trial access to our systems right away in our full-scale systems. You know, when you log into our elite platform, you're going to see that there are demonstrations that you can run with no programming. We've got open-source programming examples that show you

how to do bin packing. It shows you how to do workforce scheduling. It shows you how to do, like, production scheduling and logistics routing. There's open-source examples, and you can start starting from those and then building off of those into your business space. And then, you basically once you're running those applications then that those systems are just calling our platform from time to time and you know production applications really vary you know it's it's it's absolutely the case that the domain of providing you know cloud solution platforms for optimization they need to scale and adjust according to the size of the production application so the goal is always that the costs of the platform are provide a strong return on investment for the

application, whether it's small or large. The key thing here is that, you know, the platform is actually doing, it's not just software, it's actually doing, it's using hardware, it's deploying the machines, both classical and quantum and passing jobs to them, it's managing the scheduling of all of those jobs. The algorithms are optimized for the hardware they're running on, whether with CPUs or the quantum processors themselves. So a lot of that engineering and complexity is basically contained within the service. And because it's a combination of hardware and software, it scales with the application. So if the application is small, then it's using a small set of resources and they're basically

spun up when you're running the application and then released when you're not. And if it's really large scale, then, you know, it basically spins up that many more hardware resources running the software along it. And then, you know, our service, our teams are constantly. constantly improving those algorithms. So, in fact, we've engaged with an open source community of like physicists at the low layer, mathematicians at the mid layer, and software developers at the top layer. So wherever those improvements come in, hardware or software in that technology stack, the folks who are running the applications get a compounding benefit. So, you know,

we started the program talking about incentives. All of the incentives of the community in terms of their engagement and what they develop helps those businesses who are running applications on top of that platform. Okay. Makes sense. Thanks. Yeah, awesome. This has been a great discussion. I've got some great, you know, new nuggets that I can be swirling around and have more dangerous, more dangerous. Let's talk about the carryover question. So Murray, for our next guest, I think we're, oh, go ahead, Mark. You know, I just had like a precursor to that question before you ask the question, because I think it's maybe, the, the, interoperability of quantum systems and our guest is IBM. I was wondering what the communication

is like between yourselves, between Microsoft's IBM, the bigger quantum and the smaller quantum we had Horizon and I think that maybe second tier. Like what's the communication like? Are you sharing ideas? Are you sharing insight or is it a very much protective at the moment? Well, I think there's both aspects at play. The, so at D-Wave, we're building both bottles of quantum computers. We're building quantum-a-nealists and we're building gate model systems. And so we absolutely don't want to reinvent a whole bunch of proprietary work that's solely around, you know, gate model that other people have already done. So we're absolutely trying to work with, you know, from a software development programming standpoint, systems that,

that build on the research community, what they're doing, the compilers that folks have built, you know, the software community so that people can get that kind of benefit. And we love to compete, you know, so I don't mind if people run on multiple platforms because ours is the best. So everyone's going to have the best possible experience. So that's positive. And I think that's very customer-friendly. When it comes to our Neelik systems versus Gate systems, though I mentioned the point before that like you really need to know quantum physics if you're going to program a gate system at a low level. You don't need to know quantum physics to program quantum paed dealers. So, you know, take like quantum and artificial intelligence.

We're seeing this really growing interest from our customers in that space. And they're thinking about, you know, how can quantum computers accelerate that? If you're building a gate model system, then what you're doing is you're trying to train an artificial brain, you know, to sort of mention it, you know, from a conceptual point of view, how to learn. and you're breaking that down into a bunch of linear algebra, and you're trying to accelerate the linear algebra with the gate model system. Right? So that's a way to tackle the problem. With a quantum annealer, the quantum annealer is the brain.

So it is, like the architecture is a quantum neural network. I mentioned like, you know, John Hopfield and Jeffrey Hinton. They were describing neural networks and the way they can, you know, each neuron can excite or inhibit its neighbor from it being excited. and the way that they respond statistically, which is part of the process of trying to figure out how to wire them up so they can detect patterns, when you program our core machine instructions that you're actually setting the connections between a group of neurons and looking at their response. So in that sense, the programming can be very different. And in fact, I mean, that's part of the reason why we've got such strong engagement from the AI community is because that's a programming methodology that they're very familiar with.

and it makes it easy for them to build in the platform. So it's a little bit of both. It's cooperation. You know, you want to, you're always trying to put your resources to the most effective use. And if the community, the research community has solved a problem for you, then bring it in, you know, and work with them. And if, and if it's your core value proposition, then, you know, for that element, work on it internally and then contribute that as a service. But it has to provide a lot of value, you know, for it. to be a business service, and that's what we're focused on.

Makes sense. Makes sense. All right. You think the question, right, the transfer question, is there right? Yeah, yeah. What's the carryover question that you have for our post-quantum cryptography expert? Okay. Well, I think that the, probably the question I would ask is you're going to have different users

who are thinking about cybersecurity from a different perspective. and it's the folks who are trying to control surprise, who are really trying to watch this space most closely. You know, if they're working in health, then they're working on making sure that their people's protected data is protected for decades into the future. So, you know, what advice do they provide

for health care professionals who are trying to control surprise and how they should be thinking about, post quantum cryptography and how it relates to them. That's a good one. Looking forward to asking that one. We'll let you know, we'll let you know, Marie, what they say. Well, I listen to a whole bunch of you guys' podcasts, so I'm going to tune in.

I'm going to listen and hear what they say myself. It'll be fun. Oh, outstanding. Not only a guest, but a listener. That's our favorite combination. Favorite combination. Well, thanks for joining us today. This has been awesome as we continue to step into quantum

as we continue to let our audience learn a little bit more, get quantum dangerous from Quantum Curious and move down the path to maybe incorporating it into their business down the road. Mark, closing thoughts? Join our book club where we are going deeper and deeper into quantum. We're reading Quantum Supremacy by Mitch Yukaku. Jeremy has mentioned the Nexus by Julio Tino a few times.

my favourite book of 2024 complex systems it's fantastic the convergence of science, art and technology so yeah thinking on paper

to XYZ if you like what you listen to please comment share with a friend who's also quantum curious and we'll see you next week for some post quantum cryptography

yes thank you Jeremy be curious stay disruptive keep thinking on paper see you next time guys Awesome.