Filed by D-Wave Quantum Inc.

Pursuant to Rule 425 under the Securities Act of 1933

and deemed filed pursuant to Rule 14a-6

under the Securities Exchange Act of 1934

Form S-4 File No. 333-263573

Subject Company: DPCM Capital, Inc.

(Commission File No. 001-39638)

SPAC Alpha – 7/25/2022 (available 7/26/2022)

Rajiv Shukla:

Okay. Hello everyone. It’s my pleasure to introduce a very exciting company that’s going through a SPAC transaction, D-Wave Systems and its founder and CEO, Alan Baratz to you this week. Alan is a long time fixture in the high tech industry, having been the founder and president of JavaSoft. At Microsystems, he’s been a CEO five times with many successes along the way, companies built and sold and he went on to, of course, create D-Wave, which is a very interesting quantum computing company that exists on the cloud. So it’s my pleasure to introduce to you, Alan.

Welcome, Alan.

Alan Baratz:

Thanks, Rajiv. It’s a great pleasure to be here.

Rajiv Shukla:

So Alan, we typically start these calls with a discussion of how the company began, what was the unmet need that you were looking at and the genesis of this great idea. But given that this is a very complex subject, perhaps the better place to start would be with just a quick sense of what quantum computing is. We are all familiar with the Heisenberg uncertainty principle of two states resolving to one. But perhaps you could just explain to us what quantum computing is and then from there, get into how you thought of creating D-Wave.

Alan Baratz:

Sure. Happy to do it. So quantum computing is honestly nothing more than using quantum mechanical effects to solve hard problems faster than they can be solved using current computers. The quantum mechanical effects that we’re talking about are superposition, entanglement, and tunneling. And at D-Wave, we have been at this for over 10 years. We were the first quantum computing company. We decided early on to take an approach to quantum computing that’s known as annealing quantum computing. We selected that approach because annealing is much easier technology to scale.

It’s much less sensitive to noise and errors, and it’s very good at solving optimization problems. And optimization represents most of the important, hard problems that businesses need to solve today. These are things like employee scheduling or autonomous vehicle routing for a manufacturing plant for improvement or 3D bin packing for improving shipping logistics. Frankly, as I said, most of the important hard problems that businesses need to solve our optimization, annealing is great at solving optimization problems.

And so we decided early on to go down that path and it’s played out very well for us. We’re now at over 5,000 qubits when everybody else in the industry is at about 50 or 100 qubits. We are solving real world problems at production scale and we’re helping business improve their business operations.

Rajiv Shukla:

I’m not sure if I followed everything there, Alan. It’s very complex. So help us understand what can a quantum computer do that a regular computer cannot besides of course the speed perhaps. But are there certain things that a quantum computer can do that regular computers just cannot handle?

Alan Baratz:

Yes. So, I think of it in terms of revolutionary applications of quantum computing and evolutionary applications of quantum computing. On the revolutionary front, these are things like developing designer drugs, and a drug for a drug for you specifically, Rajiv, to address all your issues, shall we say, or global weather modeling for things like disaster prediction or building batteries that will last forever. These are applications that require so much computing power that we can’t even start to address them today. And quantum computing will help us solve those problems and address those applications.

On the other hand, there are evolutionary applications. These are applications like what I described a few minutes ago, these optimization applications that businesses are solving today. It’s just that in order to solve these problems optimally, in order to get the best solution, you require more classical computing power than either exists or you’re prepared to apply. And so companies use heuristics to try to get good enough solutions. Well, with quantum computing, we’ll be able to deliver better solutions. In fact, at D-Wave today, we are delivering better solutions to that class of problems.

So there’s the really high profile revolutionary applications that quantum computing will be able to address and then there’s the really important evolutionary applications that businesses are solving today, but just not optimally that quantum computing, at least D-Wave annealing quantum computers can and are addressing today to give them better solutions.

Rajiv Shukla:

Fascinating. Alan, just thinking about some applications, I’d imagine there are industries like weather forecasting, which deal with massive amounts of data, financial services, my world, where the stock market is constantly changing and quant investing, frankly, drives financial markets more than human beings do. I’d imagine medicine is another, healthcare is another area where lots of data requires to be crunched, cybersecurity, bitcoin. Can you just give us a sense of how quantum computing might impact all of these different industries?

Alan Baratz:

Sure. So for example, Volkswagen is a customer of ours. Volkswagen started frankly, fairly early on with quantum computing. They started using our system four or five years ago. Originally, they were focused on global traffic management. So this is not computing the shortest path from my house to your house, Rajiv, which we just found out is only about 10 minutes away from one another. But because that’s a pretty simple computation, computing the shortest path from me to you. But, this is quite different.

This is take a city and you want to optimize the routing of all the vehicles in the city so that everybody gets to their destination in the shortest possible time. That’s computationally a very, very challenging problem. It’s what we call an exponentially hard problem or an NB hard problem and it’s one of those types of problems that a classical computing cannot solve to optimality, so we try to use heuristics to solve it well enough. But with quantum computing, we can do better and Volkswagen started early on with that. Then more recently, they’ve been working with our system on a portion of their manufacturing process.

Basically when they paint the vehicles, the idea is to optimize the scheduling of the painting of the vehicles so you minimize paint changes. Because every time there’s a paint change, you introduce waste and you introduce delay into the process. And this is also computationally a rather challenging problem and they’ve been able to use our systems to come up with schedules that are better than the schedules that they were computing classically. So there’s a couple of examples from Volkswagen.

Moving on, a partner of ours, multiverse working with BBVA, a European bank, worked on portfolio optimization subject to a given risk profile where the risk profile is managed by the Sharpe ratio. So for a given Sharpe ratio, what’s the maximum return you can get on your portfolio? And they used a number of different portfolios from small to large and several different computing engines to solve this problem. What they found was on the largest of the portfolios, only two systems could compute the solution. One was tensor networks from Google. It took about 30 hours to compute the solution.

And the other was our quantum computer where it took less than three minutes to compute the solution. Other examples would include Bank of Canada that’s done some work on modeling uptake of crypto. We just announced an important partnership with MasterCard where we’re going to be working on optimizing loyalty programs, trying to streamline cross border settlements, fraud detection. We’ve done work with GlaxoSmithKline on protein folding, some very interesting work with a partner by the name of Savant that’s working with the Port of LA on optimizing the unloading and loading of container ships at the port.

So, a fairly broad array of applications, but the focus areas for us are on three industries who are primarily focused on manufacturing and logistics as the first, finance is a second, and pharma is the third. Because those are the industries that have most of the really important and hard optimization problems that we can address extremely well.

Rajiv Shukla:

Well, that’s terrific to hear. I know two of those three industries and very excited to see what quantum computing does to our world. Alan, I’m struck by your examples. Oftentimes, when we think about quantum computing, we might be thinking about landing on Mars kind of stuff, which sounds very sexy and exciting, but is 10 years away. But the examples that you gave are of process and system optimization, which seem to be real world problems that impact us now, which I guess has a bearing on how your company grows and the traction that you have and so on.

So, I think it’s worth pointing out, at least in my own head when I heard quantum computing, I thought this is going to be The Positronic Man with Asimov with the three laws of robotics, but this seems to be something that is relevant in the near term. So from there, I’d love to go into how you thought of ... You mentioned that you were one of the pioneers in quantum computing. There are some really big names in this industry. We hear about Google all the time. There are other large players, Amazon, perhaps, and others. There are entire governments.

The Chinese government, I think is one of the biggest spenders on quantum computing, 10 billion or so. So would love to hear about how this thing came about, who were your partners, besides yourself, how did you ... Because one of the things that I think people often don’t realize is how hard it is to create a company from nothing, from scratch. I know. I’ve experienced it. It’s extremely difficult. You need loads of luck. You need lots of believers. So, would love to hear the story as anecdotally as you like to describe how this company came about.

Alan Baratz:

Yes. So, luck doesn’t hurt, but you also need some really smart people. And we’re very fortunate at D-Wave to have an amazing team of individuals. So to answer your question, let me take a step back for a minute. I talked about the fact that D-Wave has started with the annealing approach to quantum computing. Now there are two primary approaches to quantum computing. There is annealing, and then there is gate-model. Those are the two primary approaches. As I said, at D-Wave, we started over 10 years ago and we selected annealing for the reasons why I gave you.

Everybody else that has jumped into quantum computing came in maybe about five years ago, and they all decided to go with gate-model rather than with annealing. And there’s a reason why they selected gate, but it was a big mistake, and let me kind of explain why. So back five years ago, we knew that annealing was very good at solving optimization problems. That’s D-Wave. But we also knew that annealing could not solve all quantum problems. For example, D-Wave annealing quantum computers cannot solve differential equations problems for things like quantum chemistry.

However, five years ago when everybody else decided to jump into quantum, it was believed that a gate-model system could solve all quantum problems, including optimization. So everybody else said, well, if gate-model can solve everything and annealing can’t, we might as well build a gate-model system. And that’s pretty much why everybody else decided to go into gate-model. However, we all got surprised about a year ago when some theoretical results were proven that showed that gate-model systems will likely never deliver a speed up on optimization problems.

So what this means is that there’s now kind of a bifurcation in the quantum application space. There are applications like optimization that will always require annealing. And applications like quantum chemistry of computational fluid dynamics, differential equations that will always require gate-model. Okay. Okay. So, that means we’re going to need both annealing and gate-model systems into the future. But interestingly, there’s only one company in the world that does annealing, that’s D-Wave. We picked it early on and we’ve gotten it to the point where it’s commercial today, but we’re the only company in the world that has that technology.

And as a result, the only company in the world that can address the optimization portion of the market for quantum, which according to BCG represents upwards of a quarter of the market. So, that’s ours exclusively. Then everybody else is competing for gate-model. Now, we also are now in the gate-model game because we decided that we wanted to be the one stop shop for quantum. We wanted to support all of our customers’ use cases. And so we’re now developing gate as well. However, it’s likely going to be seven or more years before a gate-model system can actually deliver speed ups on business applications at production scale.

So for D-Wave, we’re in a really interesting position right now, kind of a first mover advantage. With our annealing quantum computers, we’re out in the market commercial today helping customers improve their business operations, continuing to enhance our annealing systems to always do better and better for that class of application-

Billing systems to always do better and better for that class of applications. And then when our gate model systems mature, we’ll be able to support even more applications for our customers. But that’s an amazing first-mover advantage that gives us a significant leg up over everybody else, including the big guys, like the IBMs or Googles of the world.

Rajiv Shukla:

Alan, I’m going to ask you a question that goes deeper into the tech, although I’d love to hear about the story of the company, so as you’re talking about these different types of technologies, Gate versus Annealing, are there metrics that you could use to compare? Because I see terms like fidelity, coherence times, error rates, things like that. How would you help folks who may not understand this technically to grasp these differences?

Alan Baratz:

Yes. So Annealing and Gate are very different. Let me talk about how they operate and as a result, why they’re different and why it’s hard to compare them on fundamental technological metrics. You really need to compare them on application benchmarks, but Annealing Quantum Computers solve only one problem. The problem they solve is finding the lowest point in a multidimensional landscape and they do it really well. They use superposition, entailment and tunneling. Especially tunneling, to find that low point in a multidimensional landscape. What’s really interesting about that particular problem is that it’s computationally very hard. It’s an exponentially hard problem or an NPR problem, so one of the hardest of the optimization problems. It’s also a problem that any other optimization problem can be pretty easily mapped into. And so what that means is that solving a problem on the D-Wave System or programming the D-Wave System is a matter of mapping your optimization problem into the optimization problem that the D-Wave System solves. And then the D-Wave Annealing Quantum Computer solves it and gives you the solution.

So A, it’s very easy to program. You don’t need to understand quantum Gates and B, because it uses Annealing to solve this problem, it’s much less sensitive to noise and errors. So we don’t need error correction to get good solutions to this problem. Gate model on the other hand, is programmed more like you program a …

Rajiv Shukla:

Alan, sorry to interrupt. So this error correction being more robust in your technology, does that link with the speed at which you can move and is that somewhat linked to the optimization hurdles you were talking about?

Alan Baratz:

So let me talk about the Annealing algorithm. So as I said, our system solves only one problem, the low point in a multidimensional landscape, and it uses Annealing to solve that problem. So it really runs only one algorithm, the Annealing algorithm. The Annealing algorithm takes, call it a microsecond to run on the quantum computer. So roughly that’s how long it takes to solve one of these problems on the D-Wave Annealing Quantum Computer. Now, because of that fact, our coherence times do not need to be as long. That’s quite different from Gate model systems where you need to specify the instructions needed to solve the problem. The quantum gates needed to solve the problem. You could have hundreds of thousands or millions of gates that you need to get through, that algorithm could run for minutes, hours, days, or years in fact, to solve the problem on a Gate Model System.

So there are much more stringent coherence requirements. More over when you have those long running algorithms, you need to worry about errors creeping into the computation And that’s why error correction is so important. It’s much less problematic on the D-Wave Annealing Quantum Computer because we run only one algorithm, the Annealing algorithm, it’s a relatively fast algorithm to run. And so coherence times don’t need to be as long, and we’re not as sensitive to noise and errors. So that’s the fundamental difference. But the Annealing Quantum Computer doesn’t solve all problems. Great at optimization, good at linear algebra, for things like machine learning, but not good at differential equation. You really need Gate Model for the differential equations class. But then Gate Model, isn’t very good at solving optimization problems.

Rajiv Shukla:

So clearly you need both.

Alan Baratz:

You need both.

Rajiv Shukla:

You need both solutions.

Alan Baratz:

Yes... and by the way, sorry, many customer use cases require both. So think about bringing a new drug to market. You’re going to design the drug. You’re going to put it through trials. You got to manufacture it. You got to get it to market. That requires both Gate and Annealing right up front, a portion of designing the new drug is quantum chemistry. That requires a Gate Model System. Optimizing the trials, what locations, what characteristics of patients, that’s an optimization problem. That requires Annealing. And so you can see that many customer use cases will actually require both. And that’s why D-Wave being able to bring both to market is going to give us a significant advantage in the market.

Rajiv Shukla:

Very interesting, Alan, thanks so much. I think that helps us understand how these two technologies are different. We hear folks talk about the number of Qubits their systems can handle and things like this, are those metrics relevant to you? And how should we think about Qubits?

Alan Baratz:

So number of Qubits matters to us. Connectivity also matters. How many other Qubits is each Qubit connected to? And we do care about noise and coherence time. It’s not that it isn’t important. It’s just that it doesn’t need to be quite as long. So the reason why Qubit count matters to us is because the number of Qubits determines the size of the problem that we can solve on the Annealing Quantum Computer. So let me give you an example, today with our 5,000 Qubit processor natively, we can solve problems with a couple thousand variables. With our hybrid solvers wrapped around that 5,000 Qubit Quantum Computer, this is where we use classical together with quantum to solve larger problems. Today we could solve problems with up to a million variables. So, that sounds like a lot. And it is in the sense that many important business problems have a million or less variables as a part of the definition of their problem. But there are larger problems.

If we wanted to go for FedEx or UPS full up routing, optimize their complete routing problem from the backbone through to the last mile, that would require 50 million variables. So there’s still a ways to go even from a million variables. So we continue to focus for our Annealing Quantum Computers on growing Qubits, growing connectivity and reducing noise to continually solve larger and more complex problems. So just like Gate Model number of Qubits does matter.

Rajiv Shukla:

So, Alan, how do you compare your Qubits with say IonQ, Rigetti, Honeywell, Google, the usual, your peers.

Alan Baratz:

Yes. So this is where it gets very difficult, because the Qubits are different and the way the systems operate is different. So you can’t compare an Annealing Qubit to a Gate Model Qubit, or Annealing coherence times to Gate Model coherence times. They really are quite different. This is why we say, the best way to compare these systems is with application benchmarks. It’s kind of like LINPACK for high performance computing, for super computers. You really need application benchmarks. The industry isn’t there yet. And there’s an important reason why the industry isn’t there yet. It’s because while on the Annealing Quantum Computers, we actually can come up with real world problems that are good benchmarks, the Gate Model Systems are too immature to solve any of those problems today. You can’t really do much more than research experimentation today with Gate Model Systems or try to solve toy problems. It’s going to be many years before Gate Model Systems can solve commercial problems because you need error correction and you need many more Qubits than they have today.

Rajiv Shukla:

Understood. Got it. Great. So let’s go back to your Genesis, your inception story. How long has it been since you had that Eureka moment and how did you think of pulling the initial team together? Who were your initial investors and how did the company come together?

Alan Baratz:

Yes, so first of all, at D-Wave have a great base of investors that are totally committed and very supportive of the company. Just to name a couple of them, the PSP, Public Service Pension fund and Montreal, Canada is a very significant investor in D-Wave. Goldman Sachs is an important investor in D-Wave. NEC corporation, an important investor in D-Wave. So we’ve got some very good, very important investors. I’m also quite excited, frankly, about the SPAC that we’re partnering with, because this is a team of individuals that have very strong operating experience and a really strong, proven track record. And I’m very much looking forward to working with them. But great investor base to get started. With respect to building the team, it all started on the technology side.

Obviously when you want to build a deep tech company, you need the science and the research and the engineering and the software development capabilities. And so for many years, D-Wave was all about the technology. It was all about really getting to the point where there was that first system that we could use for experimentation and then iterating until we got to the point where we were commercial. Our current 5,000 Qubit Annealing Quantum Computer, our advantage system is our fifth generation quantum computer. We’ve been roughly doubling Qubits every two years or so for the last 10 years, roughly.

Rajiv Shukla:

So, your technology is all in house? It’s not been licensed in from some university …

Alan Baratz:

We own all of the key intellectual property to all of our products, whether they be hardware, software, or services. So for example, we fabricate our processors using superconducting technology. The processes and the materials that are used are ours. We own all the key intellectual property for that. Now we don’t have the big fab center, the clean room. We have a partner, we use SkyWater in Minnesota. So they actually fabricate the chips, but they fabricate them to our specifications, to our processes and using our materials. And we own all the intellectual property for that superconducting circuit design. We’ve developed many tools that don’t exist in the industry for design and verification of superconducting chips. Quantum circuit design, a lot of intellectual property there. IO, refrigeration, hybrid solvers. In fact, in 2021, we were in the top five for quantum patents alongside IBM, Google, Intel, and Northrop Grumman. We currently have over 200 US granted patents and over a hundred in process worldwide. So very substantial IP portfolio. And we own all that intellectual property.

Rajiv Shukla:

Very impressive. Yes. I think the best metric for an R&D company is to measure their IP portfolio and their patent estate. So those are great numbers. To be in the top five in the world and the other four are behemoths is very impressive. Just staying at the industry level for another minute before getting into the company in more detail, how do you expect this industry landscape to develop in the near-term and long-term? As you explain over the last year, people have come to realize that you need Annealing perhaps for certain kinds of applications, as you do with Gate. How do you see the number of players given that this is, again, an IP focused space, if you have the IP will that drive consolidation? Will there be a gradual pining out of the herd? How do you expect this will play out?

Alan Baratz:

Yes. I think that there have been predictions over the last few years that the industry’s going to consolidate, but that’s not happening. Every day there’s another quantum computing company with another approach, another technological approach. And so we’re continuing to see a little bit of a proliferation. The three primary technologies right now for Gate Model Systems are superconducting, high-end trap and photonic. But there are many other approaches that are being explored both by academic institutions, as well as very small startup companies. So I think there’s going to continue to be an attempt to explore different technological approaches to building Gate Model Systems. And D-Wave is going to be a part of that because as I said, we’re now building a Gate Model System as well. We are using superconducting, we’re using superconducting because our Annealing Quantum Computers we’re build using superconducting.

We understand it. We do believe that superconducting will be the better approach for Gate Model Systems. And we’ve got a lot of technology that we have developed for Annealing Quantum Computers that is going to be directly applicable to our Gate Model System that we think is going to allow us to move maybe a little quicker than some of the other players. But I’m not seeing industry consolidation in the very near-term. Maybe a couple of years out, we’ll start to see that as some of the technologies mature and we get more clarity around which approach or approaches are likely going to be the better ones. But not yet. There is still a little bit of a thousand flowers blooming right now.

Rajiv Shukla:

Yes. I guess that’s indicative of an industry that’s undergoing rapid change and is driven by innovation. And innovation is best done by small companies, not by the mega companies. But do you expect over time barriers to entry emerging as customers like Volkswagen, for example, has experienced with you, Goldman has experienced with you for instance, and those are great names in their respective industries. Do you expect that there will be barriers to entry beginning to emerge for other players to come into the space after you?

Alan Baratz:

I think it’s a little different for annealing and for gate, so let’s start with annealing. With respect to annealing, it’s going to be very difficult for anybody else to come into that space, and the reason is we’ve got a 10 year head start, and we’re not standing still. We’re at 5,000 cubits and continuing to add, and so for anybody who wanted to start today to come after us, it would be very, very difficult to catch up. Moreover, I talked about those 200 granted in patents and over 100 in process worldwide. We’ve also got a pretty substantial patent mode. So I think annealing is ours for quite some time to come, maybe forever, and that means there’s an important portion of the market that’s pretty much ours exclusively.

In the gate model space, it’s less clear when and how those barriers will emerge. I think at some point we may get some clarity around which technologies are the best for gate model systems. Maybe it’ll turn out that only super conducting is ever going to work, and then the companies that have invested in superconducting, they may find themselves with some significant barriers to entry from other players having to do with how far ahead they are in the technology that they’ve developed along the way. Or maybe it’ll turn out that super conducting is good for some class of applications in the gate space and [inaudible] in others. It’s just too soon to know how that’s all going to play out.

Rajiv Shukla:

Very interesting. But I imagine, again, I come from the world of healthcare, where it’s very routine for even the most innovative companies in the world to eventually be acquired by the biggest players, and the biggest players are really good at sales and marketing, they have huge cash flow, not necessarily very innovative. Innovation happens best in a Darwinian model in small companies, which either die out or innovate and survive, and then they become, like you’ve seen in the Darwinian model, a model of success. So, is that something that you’re seeing that perhaps down the road, the likes of Google, who operate a huge cloud business, or Amazon come after some of these quantum players like yourself?

Alan Baratz:

Possibly, but we need to keep in mind that Google has their own superconducting gate model quantum computer program. Amazon has a superconducting gate model program-

Rajiv Shukla:

But they don’t have annealing.

Alan Baratz:

Right, exactly. Nobody but D-Wave has annealing. Oh, okay. So, if you’re asking me then do I think that somebody might try to come after D-Wave at some point into the future and acquire us, that’s a much more specific question. We’ll see, right? Look, annealing is extremely valuable. I can’t say that we planned it out this way, but we’re very fortunate that it did play out this way, and we’re at a point now where we’ve got a technology that’s very valuable and we’re the only ones that have it. So, could a big player come after us at some point in time? Maybe. But for now, all we care about is building the business and building the company.

Rajiv Shukla:

In terms of your technology, I guess it’s well past the proof point of proof of concept, right? It’s a functioning technology that’s being used in the real world?

Alan Baratz:

No, absolutely. We are at the point now where we are able to support business applications at production scale and help our customers improve their business operations. We’ve got a go-to-market program that’s designed exactly to do this. We actually call it our launch program. It’s a four phase model where we basically have our professional services organization, because we do have a professional services team, that engages with a customer to help evaluate their applications and determine which can most benefit from our quantum systems, build proof of concept for those applications, help with pilot deployment, all the way on the path to getting those applications into production and their environment as a part of their business operations.

Rajiv Shukla:

So your sales model is like that of a consulting firm, where you’re going and providing solutions, or is it like that of a product company that’s going in selling a product?

Alan Baratz:

First of all, our product is a quantum computing as a service offering. We sell access to our quantum computers through our quantum cloud service. Our quantum cloud service is called Leap, and I’ll talk in a minute about why that’s so important. But nonetheless, our primary product is quantum computing as a service. We also do have a professional services organization, and we do sell professional services engagements for customers who want or need help identifying and building out those applications.

Currently, about 50% of our revenue is professional services, and 50% of it is quantum computing as a service. But over time, the bulk of our revenue will become quantum computing as a service, and the reason is that those professional services engagements, they end up being relatively short, upfront engagements to help the customers just get an application to the point where it can go into production. But once it goes into production, it just runs year after year after year, continuing to generate revenue for us. So, upfront PS engagement, long-running production, and so those production applications just start building that recurring revenue base for us.

Rajiv Shukla:

Alan, I’m sure you’re familiar with Palantir as a very innovative company that is the beloved of Silicon Valley, and that company operates a little bit in a consulting fashion where they work with clients to solve business problems together, and that company’s worth $16 billion. I think they’re based on a conventional computing platform, although it’s cutting edge algorithms. How would you look at Palantir perhaps as a model and compare D-Wave and say, “Look, that company’s worth $16 billion. We also are in the business of solving business problems with technology.” How would you make the comparison between yourself and, say, a Palantir?

Alan Baratz:

I think the model that you just outlined of helping customers to solve their important, hard problems better than they’re solving them today and applying a combination of, you called it consulting, I call it professional services, it’s essentially the same thing, plus novel, really fast computing capability is an excellent model, and that’s exactly the path that we’re going down, and we are still early days, but I think we are going to build a very strong, successful business around it.

Rajiv Shukla:

Yes. You mentioned a BCG study at the start of this talk, and I’m a BCG alumnus myself. I worked at BCG right out of grad school, and of course it was a terrific experience. But I’d imagine all of these large consulting firms, McKinsey, BCG, Bain, and others, who are all very cash flow positive, by the way, they’re amazing businesses. Once you have relationships with people and organizations and you keep creating value for them, they trust you, which is why consulting is such a great business, which is why consulting firms never go public. The cash flow is just too good. Have you contemplated partnerships with some of these large consulting players?

Alan Baratz:

Yes, not only have we contemplated, we have them. For example, we’re working with Deloitte, we’re working with Accenture, we’re working with smaller consulting firms like Multiverse. It is an important component of our model. So, we’ve got a professional services team, we’ve got a direct sales team, so we’ve got that component, but we also have close partnerships with other entities that can help us expand the reach.

Rajiv Shukla:

Yes. Remind me to put you in touch with Rich Lesser, who is the chairman of BCG.

Alan Baratz:

Please.

Rajiv Shukla:

Stepped down this year. He’s a brilliant guy. I think he’d love to talk to you guys.

Alan Baratz:

Love to have that conversation.

Rajiv Shukla:

Very happy to make that introduction. So, let’s segue from the technology and the story of the technology to your business. Tell us about your customers. You mentioned three segments, so maybe we can segment it along those lines. Give us a sense of how deep that engagement has been. You mentioned Volkswagen, for example, in auto. Are there other auto players as well? In pharma, are you going deeper? Again, by the way, in pharma, I can make lots of introductions for you to potential customers who would love to learn from you. So, let’s get into the customer discussion for D-Wave.

Alan Baratz:

Yes. I mentioned some of the customers a few minutes ago, but in the pharma space, and I mentioned GlaxoSmithKline, we’re also working with Johnson & Johnson, we’re working with some smaller companies like Menten AI. We’re working on not just protein folding or peptide design, but we’re also working on helping them with elements of their manufacturing and logistics. So, it’s broad based with pharma companies.

In the finance space, I mentioned BBVA, Bank of Canada, we’re working with [inaudible], we just announced our relationship with MasterCard and a few others. In the manufacturing and logistics space, Canadian grocery chains, Save-On-Foods. I mentioned Volkswagen, DENSO, Toyota.

So, we have a number of customers in each of these industries. In fact, in 2021 last year, we had over 55 commercial customers on our quantum computing as a service platform, and in fact, over 68% of our quantum computing as a service revenue was commercial, and of the 55, over two dozen were global 2000. So, we’ve got a pretty good base of commercial customers even today, and it’s still early days for us.

Rajiv Shukla:

These customers, are they proof of concept projects where you’re still in testing mode, or are you past that point and you’re now in a full-on relationship?

Alan Baratz:

Yes and no, in the sense that some customers are further along, some customers are at the beginning of the journey. I have to point out that we did not even have a quantum computer and hybrid solvers that could support real commercial applications until beginning of last year. So, we’re only a year and a half or so into it, and we did not put our professional services organization and that go-to-market model in place until toward the end of Q1 of last year. So we’re only roughly a year into this, so we’re starting to see the first of the customers that have gone through that program getting ready to move into production.

At the same time, we’ve got customers that have just, we call it do it yourself. They just bought time on our quantum computers to service platform, and they’re doing it themselves or asking us questions or for help along the way, but not within the formal program. We’ve got customers that are going through our formal multi-phase program, we’ve got customers that are just buying time. But in all cases, we try to stay close to them, to help them, to try to ensure that they have a successful journey, and they get applications built that can help their business.

Rajiv Shukla:

Right. Alan, in terms of the potential market size, the TAM for each of these segments, how large are these opportunities, and where do you expect to be in those segments?

Alan Baratz:

We use the Boston Consulting Group data. It’s pretty much the data that everybody in the quantum industry uses. Let me step it down for you. BCG puts the near-term TAM at $2 to $5 billion, and they put the longer term TAM roughly 20 plus years at $450 to $850 billion. So that’s the total TAM for quantum. Moreover, they estimate that about 20% of that is what’s available to the quantum hardware, software, and services providers. So that’s us, [inaudible], all the other players in the quantum space. So that means the TAM is maybe $500 million in the near term growing to call it $150 billion in a roughly 20 year timeframe.

Now, BCG, then subdivides that TAM into four technology areas: optimization. We’ve talked a bit about that, and I’ve explained some of the problems that fall into the optimization category, linear algebra, that’s basically machine learning, factorization, that’s crypto, and then differential equations, that’s quantum chemistry and computational fluid dynamics. They’re estimating that about a quarter of the TAM falls into each of these four areas. Now, here’s the interesting thing. D-Wave, with our annealing quantum computers, is the only company that can address the optimization-

Rajiv Shukla:

You have four of those, yes?

Alan Baratz:

Of the TAM. So that means that roughly $100 million near term growing to $20 to $40 billion longer term is available to D-Wave only. Now, since we’re also building gate, we’ll be able to address the other portion of the TAM, and in fact, we’ll be the only company that can address that full TAM right?

Rajiv Shukla:

Yes.

Alan Baratz:

But that’s the way we look at the numbers.

Rajiv Shukla:

The multiples once a company is $100 million in revenue is what?

Alan Baratz:

Not that. Not that.

Rajiv Shukla:

I want to ask you a question, being careful about it, because there’s been a lot of controversy about using forward projections and the like, and public company CEOs, I’ve been a public company CEO now for companies myself, we know not to provide projections unless it’s completely in the bank, and those kinds of projections actually work for certain kinds of companies. They work for mature companies that have leads to certain degree of predictability, and you’re not really playing…and you’re not swinging for the fences really. It’s all about putting that 15%, 20% incremental growth number on the chart. So with that caveat, have you been giving any forecasts to investors or have you stayed away from forecasts?

Alan Baratz:

So unlike companies that go through the IPO process where you do not give out forward projections, companies that go through the SPAC process do provide five year projection. We did provide that. We are not giving any data beyond that five year projection and that’s in our investor deck and you can also see it in our Form S-4 registration statement.

Rajiv Shukla:

I see your figures. So you’re expecting about $27 million or so in revenue this year?

Alan Baratz:

No. This year is $11 million. 2022 is $11 million.

Rajiv Shukla:

Oh, sorry. I already jumped ahead.

Alan Baratz:

I think you were looking in next year projections.

Rajiv Shukla:

Okay, wonderful. Great. And that number rises up to about half a billion by 2026.

Alan Baratz:

Five years out, yes. That’s what’s in the projections.

Rajiv Shukla:

With a very significant EBITDA margin in 2026 of $226 million.

Alan Baratz:

Yes. Now a big part of the reason why EBITDA improves is because gross margins improve.

Rajiv Shukla:

Yes. This is the nature of tech businesses that high fixed costs and super high operating leverage.

Alan Baratz:

Yes. And the fact that early on, a larger percentage of the revenue is that professional services revenue, but as time goes on, the production applications just build the recurring revenue base. And that quantum computing is a service recurring revenue is much higher margin.

Rajiv Shukla:

I’m sure, I mean, AWS must have margins like this for Amazon.

Alan Baratz:

Yes. I don’t know AWS as much. Sorry.

Rajiv Shukla:

80% of gross margins are quite expected for tech businesses that have a recurring revenue component.

Alan Baratz:

Yes. We have good margins on our quantum computers as a service business.

Rajiv Shukla:

Terrific. We’re nearly getting into the end of an hour here, so I want to keep getting into your financials. So in terms of your burn rate, what burn rate are you expecting for the coming years and by when you expect to be cash flow positive so you don’t have an EBITDA burn rate?

Alan Baratz:

Yes. So look, I don’t want to get into the details of the expenses year after year, quarter after quarter. You can go see what’s in the five year projections, but let me make a couple points. First of all, we are very capital efficient. It costs us well less than $2 million to build one of our annealing quantum computers. We can build and calibrate it and have it in operation in three to four months. And yet, one of those systems will support $25 to $30 million of revenue per year.

Currently, we do have three systems in our leap cloud service, which means if we didn’t add any more capacity, we could support revenues of $75 to $80 million. So we don’t even have to add another system to our cloud service for a couple of years. We will, but we typically add systems for sovereignty reasons. We put a system in Europe for European customers that want their applications to run on a European system, in the U.S. for the same reason. And we’ll put some systems in other key geographies around the world and stay ahead of the curve with respect to the quantum computing capacity we need in our cloud service.

Secondly, there are a lot of levers that we can pull on expenses. And so at this point, we are very expense efficient in the operation of the business and continue to be very careful about managing our expenses as we go forward. In the five year projections, you can go look at the data, but I think we said that we turn positive cash flow in a few years at around $200 million revenue, something like that.

Rajiv Shukla:

That’s right. Yes. Your projections indicate positive EBITDA in 2025 with $219 million of revenue and $14 million of EBITDA. I do note that your 2022 EBITDA projected is $59 million negative, but for 2023, it’s $83 and then it recovers from there. What’s happening in 2023 that the EBITDA will decline further? Are you planning a big sales push?

Alan Baratz:

So keep in mind when we put these projections together, it was probably a year and a half ago. And at that point in time, the markets looked very different from the way they look today. And we expected that the SPAC transaction would yield significant cash and that we could make significant investments to try to really grow as fast as we could possibly grow.

The realities are different today. We are waiting to see what happens with redemptions on the SPAC transaction. We should have the answer to that question within a week or so. We’re that close at this point. And once we see that, we will pull the appropriate levers on expenses. But here’s the good news: we have no revenue from our gate model system in the five year projections. We have no revenue from linear algebra and factorization that annealing can address as well. It’s only optimization. And so if we had challenges on the expense site, if we needed to build it out a little less rapidly, there are programs we could slow down, like the development of gate model system, that would not impact our top line at all. So we’ve got a lot of levers to pull to allow us to manage this business in exactly the right way.

Rajiv Shukla:

So if you fine tune your expenses based on your cash position for the next couple of years, it’ll only impact stuff in the pipeline. It won’t impact your revenue projections. You expect the revenue numbers to be roughly in the same ballpark?

Alan Baratz:

We think that there are levers that we can pull on the expense side that will not really impact the revenue side.

Rajiv Shukla:

Understood. Okay. Got it. And then in terms just as an overview of your deal structure here, I note your pre-money valuation is $1.2 billion. There’s no secondary selling of shares. None of your investors or your team are selling any shares. All the money is going directly to the company. It’s a pure primary transaction. And I see that the valuation is roughly in the same ballpark pre-money for the IonQ transaction, maybe a little bit lower than IonQ and their De-SPAC transaction, similar to Rigetti as well. All of the quantum, you guys have all found the local maxima, I guess. And then in terms of your PIPE, you have a $40 million PIPE committed?

Alan Baratz:

Yes.

Rajiv Shukla:

And then there’s $300 million of cash in the trust. I looked up the SPAC ticker symbol, DPCM, very impressive SPAC team led by tremendously experienced, sponsor, Emil, and one of the finest boards that I’ve seen with incredible people on the board. So it looks like you have a lot of smart people who are backing you guys on this transaction. In terms of thinking about your valuation, how would you help investors think about valuation comps, think about valuation, get a sense of how attractive you are or are you priced as a premium or are you priced as an attractive valuation relative to where other players are?

Alan Baratz:

Yes. Look, when we set the valuation for the company, we thought that IonQ and Rigetti were two of the closest comps. We also looked at the five year projections and multiples. And combining all of that together, we felt that 1.2 billion was a fair valuation pre-money for the D-Wave company.

Rajiv Shukla:

Yes. And then I note that you’ve put in place a very interesting bonus structure which is, I must say that this structure reminds me of the Pershing Square team structure where you... And again, being a bit of SPAC nerd, I guess, I really like the structure very much. It’s a structure that rewards people for not redeeming and I’m a big believer in this. I think people who keep their money invested should be the ones rewarded as opposed to people who redeem their money and get to keep their warrants, which is basically free money for them.

Rajiv Shukla:

So I note your structure as being a Tontine type structure, which rewards non-redeemers. And depending on how much of the redemption toggle there is, the stock price can be as low as $6.90, which is a 31% discount, which would be if you apply a 31% discount to your pre-money, that’s nearly a third of $1.2 billion. So it would look like something in the 800s, as opposed to the 1.2 billion, which would certainly look attractive, I guess, relative to where Rigetti and IonQ are trading. But of course, if investors are more excited and they redeem less, then the cost basis would basically toggle a little bit higher and I would certainly share this screen with some investors so that they understand where the numbers are. Alan, as we wind up this call, are there any other points that you’d like to share in closing with the viewers?

Alan Baratz:

Yes, just a little bit more in the bonus share structure. So look, at D-Wave, we’re problem solvers. We’ve had to solve a lot of really complex problems along the way. And when we looked at the market six or seven months ago, and we saw that it was challenging and it wasn’t going to improve any time soon, we wanted to do something for the DPCM stockholders to demonstrate that we’re committed to them if they’ll commit to us. And that’s why we put this bonus share structure in place. It is an up to 5 million share bonus pool that gets allocated on a pro rata basis to all of the stockholders that do not redeem. We’ve got a separate pool that will be used for the pipe investors so that once we know the cost basis that comes out for the public company stockholders based on redemptions, we will give that same cost basis to the pipe investors by using that other pool of shares. And again, this is all about just trying to do the right thing by the stockholders in DPCM.

Rajiv Shukla:

I’ve been a professional investor over the course of my career, outside of doing SPACs and run hedge funds and PE funds and so on. And for us, it’s very important to find businesses that are led by people who are investor focused. And we are able to judge investor orientation by how communicative they are, meaning how transparent and how willing they are to speak with investors. This call is certainly one example of that.

Rajiv Shukla:

The other is how much they care about investors. And there are some CEOs who get extremely wound up with their own value, not looking at market conditions, but the fact that you looked at market conditions and you took a call to make this more attractive to investors is a very good sign. It shows that this team will continue to look after investors’ interests. So on that happy note, thank you very much for taking the time to speak with us.

Alan Baratz:

Thanks, Rajiv. It was my pleasure and thank you for the opportunity to be here.

*****

Important Information About the Proposed Transaction between D-Wave Systems Inc. (“D-Wave”) and DPCM Capital, Inc. (“DPCM Capital”) and Where to Find It:

A full description of the terms of the transaction between D-Wave and DPCM Capital is provided in a registration statement on Form S-4, as amended, filed with the Securities and Exchange Commission (the “SEC”) by D-Wave Quantum Inc. that includes a prospectus with respect to the combined company’s securities, to be issued in connection with the transaction and a proxy statement with respect to the stockholder meeting of DPCM Capital to vote on the transaction. D-Wave Quantum Inc. and DPCM Capital urge investors, stockholders, and other interested persons to read the proxy statement/ prospectus, as well as other documents filed with the SEC, because these documents contain important information about D-Wave Quantum Inc., DPCM Capital, D-Wave, and the transaction. DPCM Capital commenced mailing the definitive proxy statement/prospectus to its stockholders on or about July 13, 2022 in connection with the transaction. Stockholders also may obtain a copy of the registration statement on Form S-4, as amended—including the proxy statement/prospectus and other documents filed with the SEC without charge—by directing a request to: D-Wave Quantum Inc., 3033 Beta Avenue, Burnaby, BC V5G 4M9 Canada, or via email at shareholdercomm@dwavesys.com and DPCM Capital, 382 NE 191 Street, #24148, Miami, Florida 33179, or via email at mward@hstrategies.com. The definitive proxy statement/prospectus included in the registration statement, can also be obtained, without charge, at the SEC’s website (www.sec.gov).

Forward-Looking Statements

This communication contains forward-looking statements that are based on beliefs and assumptions, and on information currently available. In some cases, you can identify forward-looking statements by the following words: “may,” “will,” “could,” “would,” “should,” “expect,” “intend,” “plan,” “anticipate,” “believe,” “estimate,” “predict,” “project,” “potential,” “continue,” “ongoing,” or the negative of these terms or other comparable terminology, although not all forward-looking statements contain these words. These statements involve risks, uncertainties, and other factors that may cause actual results, levels of activity, performance, or achievements to be materially different from the information expressed or implied by these forward-looking statements. We caution you that these statements are based on a combination of facts and factors currently known by us and our projections of the future, which are subject to a number of risks. Forward-looking statements in this communication include, but are not limited to, statements regarding the proposed transaction, including the structure of the proposed transaction; the total addressable market for quantum computing; the projections of D-Wave included in the proxy statement/prospectus; future growth; and the anticipated benefits of the proposed transaction. We cannot assure you that the forward-looking statements in this communication will prove to be accurate. These forward-looking statements are subject to a number of risks and uncertainties, including, among others, various factors beyond management’s control, including risks relating to general economic conditions, risks relating to the immaturity of the quantum computing market and other risks, uncertainties and factors set forth in the sections entitled “Risk Factors” and “Cautionary Note Regarding Forward-Looking Statements” in DPCM Capital’s Annual Report on Form 10-K filed with the SEC on March 15, 2022, and in the proxy statement/prospectus filed by D-Wave Quantum Inc. in connection with the proposed transaction, and other filings with the SEC. Furthermore, if the forward-looking statements prove to be inaccurate, the inaccuracy may be material. In addition, you are cautioned that past performance may not be indicative of future results. In light of the significant uncertainties in these forward-looking statements, you should not rely on these statements in making an investment decision or regard these statements as a representation or warranty by any person that D-Wave Quantum Inc., DPCM Capital, or D-Wave will achieve our objectives and plans in any specified time frame, or at all. The forward-looking statements in this communication represent our views as of the date of this communication. We anticipate that subsequent events and developments will cause our views to change. However, while we may elect to update these forward-looking statements at some point in the future, we have no current intention of doing so except to the extent required by applicable law. You should, therefore, not rely on these forward-looking statements as representing our views as of any date subsequent to the date of this communication.

No Offer or Solicitation

This communication is for informational purposes only and does not constitute an offer or invitation for the sale or purchase of securities, assets, or the business described herein or a commitment to D-Wave Quantum Inc., DPCM Capital, or D-Wave, nor is it a solicitation of any vote, consent, or approval in any jurisdiction pursuant to or in connection with the transaction or otherwise, nor shall there be any sale, issuance, or transfer of securities in any jurisdiction in contravention of applicable law.

Participants in Solicitation

D-Wave Quantum Inc., DPCM Capital, and D-Wave, and their respective directors and executive officers, may be deemed participants in the solicitation of proxies of DPCM Capital’s stockholders in respect of the transaction. Information about the directors and executive officers of DPCM Capital is set forth in DPCM Capital’s filings with the SEC. Information about the directors and executive officers of D-Wave Quantum Inc. and more detailed information regarding the identity of all potential participants, and their direct and indirect interests by security holdings or otherwise, is set forth in the definitive proxy statement/prospectus for the transaction. Additional information regarding the identity of all potential participants in the solicitation of proxies to DPCM Capital’s stockholders in connection with the proposed transaction and other matters to be voted upon at the special meeting, and their direct and indirect interests, by security holdings or otherwise, is included in the definitive proxy statement/prospectus.