Capital Markets Day Transcript - XNDU 2026-08-31

Speaker 10

Welcome to our 2026 roadmap presentation.

Brett Harris, Head of Investor Relations

Thank you very much for coming here, kind of in the last minute, and also in August. I am just going to flash this in front of you to remind you there will be forward-looking statements that are accurate only of the date given out. The disclaimers will be provided at this. Oh, the other thing is this slide presentation will be available on our website at 415, so no need to frantically take screenshots. And you can read the disclaimers at 4.15 if you so desire. And I think with that, I'd like to invite up Christian. There it is.

Christian Weedbrook, CEO

I've just got to warm up the audience, that's all. Thank you. Do I use this or not? The mic? Okay. So thank you, Brett, and welcome, everyone. I'm really excited today to be here to tell you about our roadmap particularly and a few other things, both hardware and software roadmaps. And it should be a lot of fun, and particularly looking forward to hearing any questions you may have as well. And we've got a really good turnout, which is awesome to see. So thank you, everyone, for making the trip today. So the speakers today, myself, Raf, and Michael, we always like to start off with our mission. It's guided us from very early days, nearly from day one, and it's to build quantum computers that are useful and available to people everywhere. And we often come back to this because a lot goes on during the day, during the weeks and months, and you have to remain focused. And that's one of the characteristics of Xanadu. We're focused on building a large-scale quantum computer. So fundamentally, we're a quantum hardware company, really exemplified by our use of photonics to build our quantum computer. And it's useful through the fact that we're building a large-scale quantum computer. All the stuff that you hear about, fault tolerance, error correction, many, many logical qubits and also the cloud-deployed nature where Penny Lane is also wrapping together everything here. Okay, so this is what some of the slides, including this one, is what we presented, I think, five months ago now at our first analyst day. And a lot of that, obviously, the past stuff has remained the same. I'll just go over it fairly quickly. The hardware is on top, the software is on the bottom. First chip fabricated in 2017, and then we had our X-series Borealis, and then also the Quantum Supremacy on that one, and then Aurora. And then also our first on-chip photonic GKP qubit. So GKP qubits, don't worry about the name too much, it just means Xanderthus photonic qubit. Something you may have seen on Friday, along with the $195 million Canadian announcement from the Canadian government is Inception. And so Inception is a nearly 160,000 square feet Toronto manufacturing facility that was announced, I guess, three days ago, and we'll talk more about that. And then from the software side of things, Penny Lane, obviously a big marker there, it was released eight years ago, launched the world's first commercial photonic platform so you could access quantum computers over the cloud. And then you'll see some of the next stuff that we'll talk about in the coming slides, including re-architecturing Penny Lane for scale, a very important thing. You just can't have Penny Lane from day one accessing and building out scalable quantum computers. So this was announced. This is converted into USD. It was $195 million Canadian from the federal government for our project that we call Optimism. It was executed a few days ago, announced on Friday. We had one of the main ministers, Minister Solomon for AI in Canada. He was there to announce this. Really excited about this is what we alluded to five months ago. This is part of the money. This is the federal side of things. And stay tuned for any announcements soon related to the provincial side of things. This one was announced first because it was in discussions initially. Use of proceeds established an advanced manufacturing facility. That's why we announced Inception, which I'll talk more about soon. And the cool thing here in terms of finances offsets a substantial portion of the capital for our next phase of development. Okay, we have a track record. It's all about hardware in the quantum industry. It's all about our hardware roadmap, which we'll talk about. We're a photonic-based company. Pros and cons of that coming up soon, which I'm sure you're all familiar with. But If you check out this, four Nature papers in the last few years, this is our X series, Borealis, which showed quantum supremacy, Aurora, which showed how to network quantum computers together. Remember, the goal is to have smaller, well-performance quantum computers, and you have hundreds of them and network them together. This is the only demonstration that was published in Nature by any company which shows how to solve that in a scalable manner. And then the world's first demonstration on chip of our GKP qubit, our photonic qubit as well. All of them, as mentioned, have made it into nature. This is more of a reference point. If you were at our analyst day, our first one last time, this is the same slide here. We basically compare ourselves to other approaches. So you've got the photonic-based approaches, Xanadu, and other photonics, and you've got the electronic-based ones, which are the other four columns there. Pros and cons of each. I'll just quickly go through some highlights. We've demonstrated scalable interconnects, meaning that we had four server racks at a network together. It could have been 40 or it could have been 400. We know actually how to do that at scale. The clock speed, that's something, if you want to keep an eye out over the next year or two, is going to matter more and more. This is where Photonics really demonstrates one of its extreme values, where it orders a magnitude faster than a lot of the other approaches, which is really key. So one way to look at that is we benchmark, let's say it took a year to solve one problem for the electronic-based approaches, whatever that problem is, just abstractly, 365 days to solve it. That's incredible because it would have taken hundreds of years to do it, so you're well ahead. Our computer, because of the clock speed and using photonics, would solve that same problem in eight hours. So you go from a year, which is already the promise of quantum computing, but we're better in terms of that, significantly better. The intuition is we're operating at the speed of light. Logical overhead, this is a really key one. One of the things that we decided to do today is go over some of the basics on how to think about quantum computing despite what we may hear in the media and from other companies. And so that's what you'll see. You'll see some basic things about why logical qubits is one important thing, but there are other important things. Hopefully this helps in the education. Logical overhead is always a challenging one. For us, we believe it's on the order of 100 to 1. So 100 crappier physical qubits gives you one very good or logical qubit. Now, everyone, including us, to be fair, we try to be as balanced as possible. We've said we've got 12 logical qubits. We do, but the devil's in the details, and there's many other things to really care about. But it's still an important first step to get some logical qubits. Others are saying, other companies are maybe saying 2 to 1 or 20 to 1 or something like that. I can categorically tell you that's not true. Now, if it is true, then it also applies to us, meaning that maybe 21 is fine, but the logical error rate is disastrous. Or maybe it's only correcting a certain subset of errors. Basically, in order to make real money and in order to have significant customer adoption impact, you need to have these numbers here. And for us, we actually have roughly an order of magnitude better overhead there. Less overhead means less capital, means less hardware. So that's a huge benefit for us. Two-qubit gate fidelity, we're up there with the best, I think Tide in this case. Our two-qubit gate is actually a beam splitter, one of the most famous optical elements ever. And so that becomes our two-qubit gate. Connectivity is all to all, along with a few of the others. room temperature computation. This one's always challenging to hear. We actually have seen other decks by other competitors. I love looking through them and gauging where they're at, particularly the public companies. They will have... Everyone has these crosses and all of that, and we recommend everyone dive into this and see who's being truthful about it. Often you'll see a cross near photonics, but they're talking about, say, PsyQuantum or someone else. But ours actually operates. Computes is the right terminology. meaning the logic gates and the qubits and the measurements are all done at room temperature. Now, I don't mean cryogenics, and I don't mean laser cooling either. Cryogenics operates around 10 to the minus 3. Laser cooling actually operates at 10 to the minus 6, so 1,000 times colder than the others using laser cooling. It's kind of a cool technology, literally a cool technology. And so the room temperature side of things, we truly operate. Now, the others, you know, photonics, not all photonics are created equally. They need other photonic-based approaches need to have cryogenics everywhere. That adds cost, iteration, time, slow down. Superconductors, they have the famous chandeliers. Iron traps and neutral atoms use laser cooling, so they don't have cryogenics, but they have laser cooling. They still need to go down coalesce, 10 to the minus 6 for that sort of thing. And they sometimes say they're room temperature. I don't know how. Maybe it's like saying, you know, some of their pot is outside, but their fridge, yeah, it's hard to sort of justify. But the photonics technology and roadmap, we all have that aspect of things. You're going to need some way of scaling up to do that. Just to be fair, we try to be as balanced as possible. Superconductors, some of them, like, say, IBM, are looking to do these kind of tubes. So you've got a chandelier here and a chandelier here. And they have, like, a tube, a coaxial cable, where they actually are not sharing photonics. They're sharing still whatever's in the electronics, the Josephson junctions, and the qubits created from that between it. So they're looking to scale up there. Neutral atoms are talking about shuttling things around. That's how they get all-to-all connectivity, but they still need to have photonics if they want to go beyond that once they've kind of maxed that out. Just some of the benefits. I think I've mentioned them. You know, superconducting, significant cooling and networking requirements, lower gate speeds, blah, blah, blah. I've mentioned all these sorts of things, but you're welcome to ask me any questions after that. Okay, this is one of those kind of educational slides, and I don't mean to be kind of demeaning or anything like that, but people have different definitions, and we want to kind of get back to the basics on some of these slides. So perhaps you folks already know about it. I'm sure you know about this one. Basically, you can build a qubit using photonics, which is what we're doing, or not photonics, electronics, roughly speaking. Pros and cons of both that I mentioned before. And so we encode our quantum states into a physical beam of light, and it's at room temperature. And this is one of our earlier chips, and you can see the size against, I think it was a thumb. Maybe it's a thumber. And the other based approaches, superconducting trapped ion neutral atoms, spin silicon qubits, and other photonic-based ones are listed there, and they all face their pros and cons. The other thing we like to get across is we think there's a lot of great approaches and a lot of great people out there. We believe we'll be one of the winners, but we don't think it's going to be a winner-takes-all market. It's hard to really think about too many industries where it's a winner-take-all, really. AI is not going to be like that. Automobile, 100 years ago, wasn't like that. Maybe some examples like AWS is an example of winner-take-all to some extent, and maybe Google is search. But for the most part, it's very, very rare, and we think that we'll be one of the winners, and there may be like three to five winners at least this decade that can kind of scale up to a large-scale quantum computer. Beyond that, it's always hard to. It becomes less about the technology and more about traditional business metrics and strategies. So the benefits of us, I've talked about it already, no cooling required for computation. We do need some cooling. That's the key aspect. But we don't need it for the qubits, the gates, and the measurements. They're all at room temperature. We do need some cooling. Basically, you think of it as turning on the computer or initializing it. They help prepare the qubits. But because they're entangled, the qubit ultimately never sees any cooling whatsoever. Manufacturable using silicon processors. So every quantum company that I've seen, they all say, including us, say we're CMOS compatible. To my mind, that just means you can use foundries out there. Now, I don't think anyone's lying with that comment when you see all the ticks there. But there's a difference between can you in principle use TSMC or global foundries and actually using it. It's a very different thing. With us, we're actually using global foundries, tower, UMC, and others because they're very, very, very picky about the tools they use, the processes, contamination, materials, and substrates. But when we go to these large foundries that we're already using for high-volume manufacturing, they will take our chip designs. We can email them to them. Now, the other approach is, to my knowledge, you can't sort of send an IronTrap or, you know, Superconducting Cube at Josephson Junction to TSMC, and they'll make it. They can, in principle. It's just we're not their biggest customers. They're addressing AWS, Apple, NVIDIA, everyone. And so you want to make it so it's so easy for them to say yes, and we have that huge benefit. Error correction flexibility, this is a key one we don't talk about enough, but other approaches, not all, most approaches have to already assume a particular architecture for their chips. We're like, we are working on, say, LDPC codes, but we leave that to the end. Our architecture, the designs, and the hardware don't really care what choice of error correction you use. And so what that means is we can save time and money by having a more generic system. And that comes back because we're using photonics and we have this all-to-all connectivity. And that's going to become more and more important over the coming years. Compatible with telecom, we didn't have to invent a laser or optical elements, fiber optics and so forth. Faster clock speeds I mentioned and the modular and network ability I mentioned as well. I'm going to get to it, but I'd like to be... Okay, here's a perfect... I'd like to be as balanced as possible. Our biggest challenge is loss. So we talked about how amazing photonics is. Our biggest challenge is loss. So just keep that in mind, and I'm going to come back to that. So let me start down the bottom. So every modality is judged on the same fundamentals, fewer errors, lower error rates, and more logical qubits. So today, that's what we would like to sort of let people know, that that's what we think are the biggest indicators. You often hear originally about things like physical qubit count, logical qubit count, and all those. We think these are the three biggest indicators. Errors, for us, that's loss. Remember, everyone has errors. It's just ours is loss. So there's nothing unique about us except we're using photonics and loss is what's causing the errors. Everyone has this. Everyone needs to have a logical error rate and everyone needs to have logical qubits. You don't need to worry about physical qubits. You don't need to worry about code distance and many other things. You just sort of say to a company, including us, how many logical qubits do you have? Well, we're used to that now. You ask them what their error rate is, and then you also ask them about what type of errors that they have. And these are the three things to kind of keep, be aware of, essentially. So the logical error rate, you can think of that as, remember there's like 100 to 1. You have 100 worst qubits, and that creates one very good qubit, a logical qubit. Those worst qubits, they have gates acting on them. If you can get the errors below a certain level, then that's when the logical stuff kicks in. You can kind of think of it this way. If you add more crap, you're not going to get good stuff until you're below a certain level. You can't take advantage of that. There's just a lot of crap and a lot of noise everywhere. And so this is the logical error rate where once you're below threshold, you can keep driving it down lower and lower, and you often see metrics like 10 to the minus blah, blah, blah. And 10 to the minus, getting back to what some folks say is logical qubits, If you have a ratio instead of 100 to 1, but 2 to 1, but your logical qubit rate is 10 to the minus 3, it doesn't matter. It's just not error. The error is not low enough. So this is an important slide, key technological indicators. So for any hardware, including obviously ours, real hardware is always imperfect. It doesn't matter what anyone says. It doesn't matter what we say. no one has perfect hardware anywhere in the industry, whether using photons or electrons. Having said that, if you get the error rate low enough and add error correction and fault tolerance, you can tend towards a perfect system, meaning that the errors you're getting are so low that you're going to get the right answer, which is ultimately what we all want to do. So for us, photon loss is the main source of error. Other approaches have their own sort of error. The error for everyone, you can think of it characterizes decoherence, so the loss of coherence. And so we all have decoherence. It's when our world interacts with the quantum world, makes it noisy, and photon loss causes ours. Other approaches that are electronic, they would have, say, laser noise. They have spontaneous emission. They have T1 noise, all this other sort of stuff. The great thing, and also the sucky thing about our approach, is we can just sort of say, we don't have all those other things. We just have one source of errors that we need to overcome. And that's what we and we suggest everyone here focuses on is loss reduction. And as it says there, in Zanadu's photonic architecture, nearly all these imperfections reduced to one primary challenge, photon loss, whereas other approaches has a whole lot of them. Still solvable, to be fair, but it's nice to sort of think you just have one. So how do we often... Okay, so we understand that. Then it's the case, well, how are you going to deal with loss? How are you bringing it down? Well, there's two ways to reduce loss is through hardware improvements and through theoretical architecture improvements. And so the hardware improvements is essentially wherever light goes, it has a chance to lose photons. So when you start the laser and all the way to the end, some of the photons get absorbed, they get scattered. That's a problem because the photons encode information. So that means you're losing and absorbing information, which is not good. So if you can make the hardware smoother and less rough, then the Photon has a higher chance of going from the start to finish without anything happening to it. So architecture, we say you need to tolerate abstractly this amount of loss. And then the architecture team can kind of say, hey, guys, we found some other model of the system. You can now tolerate more loss. So you see you're reducing loss by the hardware and improving the loss balance. So you're trying to reduce this gap, and then you want to get that gap to one, and then you have everything you've ever been promised when you get to one. In terms of the hardware, we have this flywheel happening here where we design our own chips in Toronto, our own photonic integrated chips, and we heavily use, particularly last year, more and more AI to really optimize the structure. So you can think of it as if you have too many elements on the wafer, on the chip, then you've got more loss. So if you can reduce elements while still maintaining the same functionality, then you can actually have a lower loss system. And then we go to multiple foundries around the world. We have three different substrates, so we work with multiple foundries because some are better at low loss in certain materials than others. We also want backups as well. So every few weeks we're getting back chips and wafers. And so then you go down to the testing. You'll hear about our new facilities soon. We can do test and measure there. That's really important. You do test and measure for us to get more information. It's like, well, it's more lossier than what we thought, so we'll have to alter it based on this feedback. And then we have a dashboard and sort of repository where we have all the data and information. So basically, you can kind of see this iteration process happening more and more. This is related to the hardware side of things. I can't overemphasize enough. We always get asked about AI and quantum. They do go together, but I would kind of tend to think that quantum is the next big thing after AI rather than trying to force two things that are hyped together, although having said that, there is some overlap. But the biggest thing that we've seen the benefit of AI, significant benefit is using LLMs for the chip design and bringing down the loss there. It's been huge for us, and I think we're ahead of the curve when it comes to that. Okay, so we had this plot, if you remember, five months ago, we stopped it here because we couldn't show any forward-looking statements. So we had it from 2023 to 2026. So here we have, think of it roughly speaking as loss. It's more of a loss gap you're trying to close. So when you hit one, you've closed it. And along here we have the timeline. Now, if you remember in our first analyst day, we had two lines. Just to make it easier and to track things better, we got rid of one of them and chose the harder line. Meaning, if we have this and we can achieve this line, then the other line automatically follows from it because there's overlapping features there. But there's harder things to do on this one. That's the first thing. The second thing is our optical system has approximately 17 different optical elements associated with a quantum computer. We have lost where we are today and lost where we need to get over the coming years. And we've aggregated them in such a way that instead of having to track all 17, we have an aggregated line for that. So that's where that comes from. The other thing, and it's really important, this is what we think everyone should judge us by, is this dotted line. Remember, you want to get down to 1. I think roughly, you can see it's log scale here. Roughly, this is 2. 1.5 is break-even on this scale. So break-even means that you've hit the threshold. Now you need to go below. And as soon as you go below, you start racking up 10 to the minus 3, 10 to the minus 4, 10 to the minus 6, minus 10, and so forth. but fairly quickly after that. The key is getting to that. In this case, it's normalized to 1.5. The other thing that I would kind of stress is that this is a midpoint. So if you think of it, it could be above or below. It's like sort of a sleeve that's error bars that encompass it. The key point is making sure it's headed down and making sure it gets to 1. So it's also not something that happens every day. Every quarter, there could be an announcement, But on average, you're heading down at the end of the year. And so, so far, you know, if you look at, say, this year, we're on track to hit the target for end of 2026, which is here. We're on track for that. Okay, so crossing the threshold, we need error reduction. In our case, it's loss. Everyone has their version. We need error correction. And that equals a logical error rate. So I've already talked about a lot of these. So threshold-driven performance, you know, the thing everyone needs to start questioning and asking is, what is the error rate? What is the logical error rate? That's the key thing. Not really now, but over the next coming years, that's going to get more and more important for folks. And I like this phrase down here. Cut loss enough to get under the loss threshold. Remember, heading towards starting at 1.5 and going to 1. Then the error correction turns pretty good hardware, the physical cubits, into almost never wrong logical cubits, the good cubits. That's that ratio, in our case, 100 to 1 there. The other thing to keep an eye out, and that applies to us too, we've said we've got 12 logical cubits. I think maybe Kiara says they've got 35. None of them are equal. It's not apples to apples, and it applies to us too. So not all logical cubits are created equal. They only become equal when you also compare the error rates. So if someone says they have X logical qubits, you then ask them what the logical error rate is. And then when those things line up, then you can kind of compare apples to apples. And that's where the devil's in the details. So it doesn't matter how many logical qubits you have. That's not the whole story. But the error rate completes the whole story there. So the error rate as mentioned is how long can you compute before the state decoheres, before it becomes crap? So 10 perfect qubits can't solve a useful problem. 1,000 that fail after 10 operations solve nothing. So it goes to the extreme of either of them. So you want them both coexisting. So to sum up a lot of stuff, key indicators, loss, key indicator, logical error rate, key indicator, logical qubits. And in between them, you need error correction. We have a whole family of error correction codes, unlike others, that we can choose from. and then the networking is that piece that we solved last year with our Aurora quantum computer. Remember, us and others will master a small quantum computer, whether it's in a server rack or a chandelier, and then you need hundreds of these things and you need to network them together. And so we've solved the networking piece. So this is very much a blueprint for anyone doing quantum computing. How is your errors? How is your error correction? What's your logical error rate? how are you doing with networking, and how many logical cubits do you have. It's kind of putting it all together. So Inception, as mentioned on Friday, we announced $195 million Canadian to build, among other things, our new facility, which is called Inception. So Inception, we moved in into Inception. It's a huge warehouse, 160,000 square feet. There's nothing in there yet. It looks really cool. It's huge. And we'll be using that money along with our own money to populate it out. So there's a few things we'll be doing in this facility. And it really is state-of-the-art. There'll be some of these machines that we buy that no one in America even has, some of these tools. So they're that state-of-the-art. It will be the best in the world in terms of low loss for our architecture. So some of the things and the capabilities, test and measure. I mentioned that flywheel. You get the wafers back. You've got to test them. You've got to understand, like, is it a performer or is it not? Get some data there. We'll be able to do that ourselves. Heterogeneous integration, that just means two different types of chips essentially glued together. We'll be doing that ourselves. Packaging, how do you control, how do you get light in and off the chip? You need to package it optically and electronically. We'll be doing that. And then how do you start assembling some of these things together? It doesn't matter if you've got them all individually. You've got to start putting them together. And not only are we shoring up the supply chain, but if you take packaging, We used to work with a company in the U.S., not far from Lake America, and they were, I think, in Ohio, and it would take up to three months to actually package it, and that was fine. But now that we're doing it in our $10 million one as a precursor to this, it was done in eight hours, three months to eight hours. And so not only are we shoring up the supply chain, which is really important in quantum, but we're also speeding up our iteration times, which is also pretty amazing. And obviously all of this helps with loss reduction as well, which is our key challenge. Okay, so I think this is the summing up slide here. Yeah, before I hand it over to our CEO, Raph, to talk about software. So this is kind of a take-home. This is our roadmap. So we have time along here. It goes up to 2030. and beyond 2031, we have key technological indicators. Loss, this is just the loss gap taken from the plot. As mentioned, one is where you need to get to, and 1.5 is the breakeven point. So you've got these numbers from the plot. Logical error rate, now this is the first time we're talking about Xanadu's logical error rate. So this is what's really important. There's a couple of things that I would say is a takeaway from today's presentation. One is our error rate. We anticipate to be far far lower than anyone else's in the world the lower the error rate you can think of that also as being proportional to More applications higher quality applications you can do compared to anyone else to our knowledge looking at publicly available Presentations the most I think I've seen someone can correct me is 10 to the minus 12 something like that So we're far lower than anyone else. That's the beauty of the photonic based approach And the other key thing is we've announced today over 1,000-plus logical qubits in 2031. Previously, we announced up to 500 in 20... We said 2029, 2030, and up to 200 before that. So this is our roadmap, and it comes back to what we've been talking about this morning. How many logical qubits? You've seen no physical qubits there. There are physical qubits, but the key thing to judge on is logical qubits and the error rate. And I don't think anyone is claiming that they can get to this. This is the great, amazing thing about photonics because the networkability, you keep adding machines and the error rate goes lower and lower. It's adding extra hardware. So that's really exciting. And then we can break these phrases up into less sort of specifics and the projects. Qubit factory build-out, this is the toughest part of what we're doing. Fault tolerance is reached in 2028, 2029. That corresponds to between these, so 1.5. and then start building the quantum data center in 2030, 2029, 2030. Now, in order to achieve loss reduction and start making sure we achieve our goals, we need to open up a facility, Inception, that was open two days ago, and we'll start building the quantum data center. We anticipate early next year is our roadmap to start that. A lot to take in, so I can answer some more questions later, but I guess we'll leave that to the end, and I'm going to hand it over to Raf now to talk about the software. Thank you.

Reuben Roy, Analyst — Stifel

I'll assume that applause is for me, not for Christian.

Speaker 18

Thank you, Christian. And, you know, we talked a lot about hardware so far. But in order to satisfy that mission of building quantum computers that are useful and available to people everywhere, you really do need a software stack to be able to enable all those applications to make it useful and, of course, provide that availability. Penny Lane is that leading software framework with over 30% of developers last year having used it. And this is across large multinational enterprises, government labs, academic institutions. There's kind of an issue in quantum computing right now in that there's a large amount of investment happening into the discovery of various quantum algorithms and applications across many industries. But fundamentally, we're bottlenecked by the availability of hardware, so this utility-scale quantum computer coming online, but also the availability of proper tooling to be able to build these applications out. If you want to think back, this is like sitting in the 1940s, 50s of digital compute, thinking about breaking codes and solving chemistry and physics simulations, but nobody's thinking about what the internet and e-commerce and all these things are going to bring because that tooling is not yet available. That tooling also provides some issues right now in terms of what's available out there, in terms of being more broadly adopted. One, a lot of it requires pretty high expertise in order to be able to leverage. You do need to be a quantum expert for a lot of the tools to be able to program. One of our investors in the past did a survey and identified that there's less than 2,000 people worldwide that have that deep expertise in quantum application development. There is substantial vendor lock-in. If you go start developing on something as simple as IBM Qiskit, you very quickly are tied to that stack. Maybe you're looking at NVIDIA CUDA Q, you get tied to NVIDIA GPU accelerators for your workflows. There's also fundamental issues that Christian already alluded to that limit existing software and being able to actually scale to the level that it needs to to provide applications for utility-scale quantum computers. To give you an idea today, you're dealing with handfuls of qubits deployed for tens or hundreds of gates if you're running on nisc era hardware that's publicly available if you're going to be running on a utility scale quantum computer you're going to be working with hundreds to thousands of logical qubits operating billions of gates over thousands of repetitions so trillions of steps and these programs today are not able to be captured by the tooling that actually exists and then finally adoption does require effort nobody wants to pick kind of the wrong stack but in the world of multiple winners a lot of the value is going to push into software so on various other compute platforms you know whether it be digital CPUs whether it be GPU accelerators whether it be AI we see that more and more of the value actually does get pushed into the software stacks and video that this incredibly well as predominantly a hardware company using the software layer to actually lock the entire ecosystem into their hardware. AWS built an entire platform and commodity compute that was able to provide huge amounts through a software layer of value. And this is exactly what we're looking to do with the entire Penny Lane family of programs. So not just Penny Lane on its own, but also Catalyst and Light Lane, which I'll touch on in a little bit. We're definitely focused on developing those high performance compute and AI applications for quantum computers. So these range from existing ideas that we have around materials and chemistry out to pharma and drug discovery to crypto analysis and broader optimization in places like finance systems. So this is really built on top of Penny Lane as that software development kit for our universal thought-tolerant quantum computers. Over this year, we've embarked on an enormous effort to actually raise Penny Lane from something that was previously targeted NISC era and I would say early midterm quantum computers to now being fully future-proof for that utility scale level. In addition to that, we also continue developing Catalyst, which is our compilation layer, actually being able to take these programs and compile them down to things, machine code, that could actually run on individual quantum computers. This is an incredibly important step as it can actually provide huge optimizations in how much you can reduce the gate count required for a given program. In some other examples, we were able to take a program And reduce the computational resources by a factor of 10 just by acquiring the proper compilation passes This means that you could take an application and run it effectively a year earlier than you would expect on hardware based on currently publicly available Roadmaps and then as these utility scale quantum computers are not available here today You do need a suite of simulators to actually be able to validate Prove out and understand the full barriers to running these in production environments And we've deployed those with lightning simulators that are now available as part of Penny Lane as well The really important part two that I'm sure everyone here knows is that Penny Lane is very much hardware agnostic and this goes both for The quantum hardware, so there's no reason why you could not run on any of the systems or modalities listed below And in fact are ready for superconducting trapped ion neutral atom and photonic. We have a number of publicly available Devices that have built-in plugins within Penny Lane so you can access those but also on the classical compute Accelerators, so we have long-standing partnership with Nvidia But also a recent one with AMD where we're targeting both of their devices along with other less traditional compute accelerators in order to let people leverage the top computational tools today to be able to rapidly develop quantum applications. This has actually attracted a huge amount of adoption for PennyLane. Like I mentioned already, 30% of quantum developers out there have adopted PennyLane within the last year. Over 1,800 publicly available software projects Reference penny lane as a dependency so are actually building on top of it that includes 1.1 million package installs Monthly on average across the entire family so penny lane catalyst and the lightning simulators and like I mentioned There's a large number of hardware and simulators that are available. So I believe 51 kind of across penny lane This has really been a massive adoption for us It's been great. It's also been great in that the reason that people are adopting this is the true fundamental technical capability of it. I mentioned the available in all modalities, Catalyst and Just-in-Time Compilation having the huge performance boost. Christian identified the importance of air correction, and this is something that is fundamentally built into Penny Lane at its core level. The other big part was originally when Penny Lane was being built out, it was considered to be a quantum machine learning software stack this was I'm gonna say some of the researchers that Xanadu were the founding figures within that field and it still holds true throughout and that's differentiable end-to-end meaning that it directly integrates with today's advanced AI platforms like TensorFlow and PyTorch and allows for easy transition of developers within those Spaces to be able to actually be incredibly familiar with Paying land as a programming language One of the major reasons why this massive adoption has happened aside from the technical pieces is the fact that Learning has really been the final that's allowed developers to come into our our software stack According to the unitary foundation over 50% of developers within the quantum space site poor documentation as the reason that they kind of drop off from various other quantum packages. We have been leaders in terms of providing material to not only get new people into quantum computing, but also transition existing developers from traditional industries like high-performance compute and AI to become quantum developers. This means we have over, you know, close to 600,000 codebook submissions. This is our coder-sized type of exercise platform where it allows people to self-assess their skill level. We have over 200 demos. So these are the latest research papers that are coming out that are then fully implemented by the community within our ecosystem and made publicly available. We also have over 4,500 distinct learners within our codebook, so people that are actively progressing. And this has become so popular that, in fact, a number of our enterprise partners have requested an enterprise version of this to be built to upskill their workforce in quantum development. One publicly that we released recently and were able to talk about is our partnership with Lockheed Martin, where they're looking at about 200 people internally every year being interested in transitioning from AI and classical developer roles into quantum developer roles, but realistically only have resources to take a handful of those folks. So we were able to work with them to build a self-serve platform where the remainder of those people are able to continue on that learning path. Lockheed Martin is not our only partner, and education is not the only place where we partner with folks. Definitely partner on a number of application development. I think this was disclosed previously, but in chemistry, finance, machine learning, and fluid dynamics. We work with folks like Volkswagen, Toyota, BMW on some battery and sensor use cases with Rolls-Royce, the aerospace company on that computational fluid dynamic applications with folks like Mitsubishi Chemical Group on next generation photoresists for semiconductor fabrications. But then also with folks like AWS, AMD, and NVIDIA, we're working to build out the next generation of tooling. If you're familiar with AWS Bracket, Penny Lane is closely integrated and in fact supports many of the features on that platform. If you're familiar with AMD and NVIDIA and what they're doing in the accelerated compute space, you will see quickly that those devices are natively supported within Penny Lane, allowing you to be able to deploy from your laptop to the largest supercomputers in the world without having to change the code in any sort of way. And then there's a wide range of companies like Agnostique, QNSS, and Menton AI that are quantum startups that are actually fundamentally building their businesses on top of Penny Lane as well. We're also doing this as part of the broader open source community. So developing this on your own is, of course, quite challenging. but we're able to now have an incredibly good reputation across various research institutions in the broader community, allowing them to contribute back into our code base. Penny Lane itself has over 2,500 scientific citations, over 75 outside contributors that are pushing code and improving in Penny Lane every single day. We're actively involved with the MLIR ecosystem. This is the middleware stack for AI compute that is now being leveraged within quantum And this just drives a broader adoption across enterprise government lab And of course academic partners as well and really It allows us to be able to start looking at early commercial adoption of of penny lane the long-term vision is definitely one of serving as that funnel for our hardware and providing an enterprise-grade platform where you're able to make the adoption and integration with quantum computers very straightforward, but we don't have to wait for the utility-scale quantum computer to be able to do that. Already today, like I mentioned, workforce training and enablement is something that we're aggressively pushing on. We have a dedicated platform that we've built out that we're continuing to provide to to various partners. We're continuing on our algorithmic engagements. So this is actually identifying billion-dollar-plus value problems that our partners have through commercial engagements and being able to capture the IP to be able to deploy those problems on a utility-scale quantum computer. It also gives us a fantastic ecosystem as we're getting developers onto Penny Lane to be able to provide future products. There's many ideas that we're currently following is just getting an understanding of when is the right time in the journey, especially as hardware develops, to be able to deploy those. And then ultimately, all of this is building out a very large funnel of not only individual developers and companies that are fully integrated and understanding quantum computers and how to develop applications for them, but also a wide range of ready-to-go applications that have been developed on Penny Lane that could be deployed on our quantum computers as of course will be a first-class citizen within the Penny Lane ecosystem. With that I'll pause there and I'll hand it back to Christian for a quick summary.

Christian Weedbrook, CEO

Thank you Raph. So we're talking about four different ways of commercializing this is also from our analyst day it's a repeat of that so I just wanted to make sure folks understand that looking to sell through make money through the hardware access to the cloud, selling individual computer systems is part of it, advanced photonic devices and IP licensing. That's a key aspect of what we do as well. Really the world's leaders when it comes to that in the ultra low loss. And then as Raph was just mentioning, the other revenue market that we're after is commercializing Penny Lane as well. Michael, is this your slide? Okay, so I think it's quite self-explanatory. It's $686 million of capital sources. We have a synthetic ATM, which we've mentioned before. I think everything is Canadian government funding. That's $195 million from the federal government. That's a non-dilutive loan. And then we have the synthetic ATM as well that's ongoing. okay so some of the key takeaways the first pure play publicly listed photonic quantum computing company deeply technical leadership and experience board category leaning leading technical position particularly with the Aurora quantum computer we salt the networking side of things that's key to how you actually scale up quantum computers not just ours but nearly all else all the others. Raph went over the penny lane. It's really one of the leading software stacks out there. And targeting meaningful and customer commercialization by 2029-2030, that corresponds to our large-scale quantum computer or quantum data center. As we've mentioned in the past, too, the top two priorities for us doesn't include revenue, not at this stage. It really is hardware and technical milestones, achieving them, because ultimately the only thing that matters in this industry is that you've built a large-scale quantum computer and secondly some partnerships with some great companies. We announced AMB and Lockheed Martin at the start of the year and hopefully partners like that will become our first and earliest adopters of our technology as well. And as mentioned, available funding that we have is $686 million USD and we also have a synthetic ATM. Okay, so we'll leave it there. Thank you very much for everyone's attention.

Kevin Garrigan, Analyst — Jefferies

Can you guys hear me?

Brett Harris, Head of Investor Relations

All right, that works. All right, we have a lot of questions. So this is the Q&A section. We have mics moving around, so if you could just start with John.

Speaker 10

Thanks for doing this, guys. 10 to the negative 16.

Christian Weedbrook, CEO

Super impressive. how are you going to get to that kind of error rate and your confidence there too Christian confidence we're in the deep deep tech sector everyone in quantum computing so there's going to be small error bars that should be taken into account for us and anyone in the industry we're very very confident in that I would say that the challenge really is getting the loss to the threshold that's where the biggest challenge is after that when you go below you pass through so I didn't mention there but 10 to the minus 3 is where our threshold sits just for our the way it's been normalized for our system so we're going to hit 10 to the minus 3 and 10 to the minus 3 is related to where we are with this loss reduction or this closing of the gap so once you pass that the way anyone gets lower is you keep adding more stuff to it because once you're below threshold the really cool thing is now that you add more stuff instead of the errors going up which you would think with error correction it actually goes down lower. The reason I bring that up is that for us and I believe it's the easiest for us in terms of this is that we can just keep adding these server racks these modules to our system because now we know how to solve the interconnect problem and the assumption there is the interconnect problem has been solved for a certain loss as well once you hit that the loss targets and then the more you keep adding the more the lower that it actually goes in the error rate so very very confident of that that's not the challenging part the challenging part is getting to that threshold and everything else there as you saw it's an aggregate of the top 17 optical elements and we needed to in this case normalized to one but you're still at that 100 to 1 physical to

Harshkumar Bhima, Analyst — BMO

logical at the 10 to the negative 16 that's correct that that doesn't change though those numbers will be pretty fixed it could it's proportional to that but yes you referred to this as copy paste I think to me yes that's the evolution there that's right thank you yeah yeah hey Harish Kumar BMO just and feel free to get technical or detailed about this you're basically dealing with photonic qubits and you said that your error consists of the photons getting lost or the information getting lost so how do you this is kind of the mechanism of error correction I'm trying to understand how do you know what's

Christian Weedbrook, CEO

lost and what's the false information that's being absorbed how do you separate between the two to get to the proper amount of information that you're designing to get to well what you can do just as a very simple example let's say you have it's going to be quite small but it's for argument say let's say the chip is this big for argument's sake so you couple in fiber optics goes in and fiber optics goes out and so you need to pump or put laser light through and it propagates along this distance here. And so what we want to do, let's say there's no optical element, it's just propagation loss, so how much of the light actually makes. And like you said, some of the light gets absorbed or scattered as it's going from A to B, and then it gets coupled out. There's loss there as well. What you do is, this is a known problem and you forget about quantum and you just shine classical light through it, so like a traditional laser. And then you know how much, you can calculate how much light you're expected to go in. Let's say it was a perfect system and then you detect how much light you actually collected at the end and then that tells you how much the loss is, the difference between the two. And it's more detailed, but that's essentially the way it is. You measure the light that comes out and compare it to the ideal case where you assume, say, zero propagation loss, for instance.

Craig Ellis, Analyst — B. Riley Securities

Craig Ellis, B. Riley Securities. So no offense, Christian, but I'll direct one to Raphael, although it may be a question for Michael. So I really appreciate all the information on Penny Lane and how we're taking that capability and trying to develop an ecosystem as many others have in the past to attract developer interest. But what I'm not clear on is how the pricing model is going to work and how we actually monetize it. Is it as a service, one-time, seat license? How's that going to work?

Speaker 18

Yeah, so I think in the long term, it really depends on how the industry does develop. What I can tell you right now that, and for example, the educational offering, the appetite from our partners is very much as part of broader engagements and kind of on a per seat basis. But what I will say is that all of this is always with an eye towards that utility scale quantum computer. The driver here, like Michael and Christian often say, is not the revenue. it's the adoption signal, it's the large number of developers that we're able to get, it's the fact that we're getting Lockheed Martin and our other partners hands-on with more people with Penny Lane, that is the push. Because what we want is not, you know, I'm not sure what the TAM of quantum compute is today, but it's small. We don't want a big chunk of that. We want a huge chunk of ultimately the TAM that will be here in 2030 when the utility scale quantum computer comes online so I think great question as something that we're continuously testing discussing with our partners internally and externally but not a major driver until that utility scale quantum computer lens no I think that the third of the last slide that Christian presented which was also from our first analyst day covers it that's that's the way that we're thinking about it how those models flush out really depends I think how many people are there at the starting line I think that side does a good job of laying out our

Todd Copeland, Analyst — CIBC

thought process great Todd Copeland from CIDC how does your position now with your technical spot in 26 stack up with DARPA's not there now I would you need to be, to satisfy stage C? Thanks.

Christian Weedbrook, CEO

Yeah, we're on, the easiest way to sort of say is we're on track for the timelines here and the timelines that I mentioned there. As you know, stage B is roughly a 12-month program. It could be longer, it could be shorter. Honestly, depending on DARPA's resources, how quickly they can get to people for the analysts, analyzing the hardware. But we can't mention more until we, you know, find out how we're doing, but so far so good, and I can say we're on track. The key thing really is the on track really relates to the loss. That's really the be-all and end-all where we are with loss reduction. As mentioned, this has to make sure it keeps going down. There'll be quarters where we may, like I think last quarter, we mentioned a really good fiber chip coupling result. We'll announce them every now and then, but it really is study at NDS, seeing that go down, and making sure ultimately we're hitting that value of one.

Joe Moore, Analyst — Morgan Stanley

Joe Moore and Stanley, just a question on the software side. I want to understand something you said about CUDAQ. I would understand reservations with IBM and Google that you're tied to their hardware. I guess NVIDIA would argue that they're sort of agnostic to the quantum side of it, and you're locked into the coprocessor and simulation side. But what am I not understanding about that? Why would people be afraid of NVIDIA locking on CUDAQ?

Speaker 18

Yeah, so I would make the argument that you're actually pretty locked into still the hardware side as well. And it's because that advanced compilation requires hybrid compilation between all your computational resources. And if you're going to limit yourself in building a quantum computer to just NVIDIA GPU accelerators, for example, you will never be able to achieve above megahertz clock rates. And the honest truth is, you know, NVIDIA is great at wide pipes, but not low-latency compute. If you want low-latency, you're probably going to be going with AMD, with their RFSOC or FPGA products through Xilinx or others. And now if your software stack can't compile down to those types of resources, you've disqualified a whole host of quantum computers. So it does look like, you know, people often think only of the simulation side. but there's the entire runtime and compilation side as well that you want to make sure you are relatively vendor agnostic because right now within the broader quantum ecosystem that is very actively playing out and at Xanadu we're not even limiting to only existing hardware we're even thinking things about like what happens when there are custom ASICs that people start building out specifically for their purposes for their quantum computer and how do you have a broad plug-in architecture that can support rapid development of those types of elements that you're able to compile down into.

Nehal Choksi, Analyst — Northland

Yeah, thanks. Nehal Choksi from Northland. Thank you for showing that roadmap in terms of DB improvement. It is somewhat nonlinear as we go through time, so it looks like it's carefully thought out with respect to particular improvements and components, particular components. So given that context, what is that one photonic component or material parameter that's going to give that big boost, especially going from 28 to 29?

Christian Weedbrook, CEO

It really is the 17. There are maybe one or two that were pretty much already there in terms of the number of nines and say fidelity we need, but you can make say one quarter we make progress on the second out of the 17 that's good and then you need to go to the next one or the one after that to improve it so it really isn't the way I would look at it is the one thing has already been chosen that's loss but then you really do break it down into many other things the other thing too is you can trade off numbers as well so you can kind of say I've made more progress than I thought here and that will allow the other one above it for instance to tolerate more so it's it's really hard to sort of narrow it down to one another way another way to look at is let's say you had 16 of them amazing and one is really really bad you can't do anything so they're all really interconnected and you really need to make sure that you're lowering the loss on all 17.

Nehal Choksi, Analyst — Northland

One other question maybe quick maybe not talked to you about this before but it's hard to wrap my head around it so why can Xandu tolerate any photon loss at all? Because there are some other photonic quantum computing players out there. They're thinking about, hey, we got to be able to do single photon detection, no loss of photons at all. So can you talk to that?

Christian Weedbrook, CEO

The single photon ones, yeah, they will have loss. In that case, they're losing their whole carrier. So when you have a single photon, you're losing the whole information that's been encoded. Technically for us we're using many photons where theoretically it's looked at as having a mixture of vacuum mixed in, basically so you never really lose it it's just a quirk of ours where you don't lose it but you actually get more noise to the system that's how you theoretically work it out. But to answer your question, ultimately it's just the type of encoding that one approach compared to another approach was taken we're taking a many photon approach where you can encode information using these GKP states, which has many photons and a superposition of photons, whereas the other one, roughly speaking, is a single photon. So we have more resistance to our GKP. We have the first layer of resistance is with a GKP encoding that already tolerates a certain amount of loss because you're actually not worried about a single photon in that case. Photonics, that's correct. There is another one in Japan that's been getting some funding. It seems like they're doing GKP, but it's unclear, and they're using a different sort of rate, a repetition rate for theirs. For the most part, yes. There are some in superconducting. Nordquantique has it.

David Williams, Analyst — Needham

Thanks. David Williams with Needham. So you talked about the 17 components earlier, and you've got Inception that you're building that's for a couple of different purposes, really for packaging and bringing that in. How do you think about these components? are there opportunities for you to bring those in-house or maybe to accelerate this path forward on some of these components that aren't quite performing as you would think, or how is that?

Christian Weedbrook, CEO

A hundred percent, yeah, for sure. So packaging, like you mentioned, is one. I gave that example where it would have taken, it was taking three months, and with last year, now it's a $10 million, kind of a baby version of Inception. We spread that up to eight hours. But another example is the heterogeneous side of things that I mentioned, you know, it's for test and measure, which you can measure the loss and then iterate to bring it down. Packaging, I mentioned, that's what it's doing to reduce the loss. But heterogeneous integration, it just means that one way to look at it is perhaps instead of using fiber optics to connect two different chips, you can actually place them on top of each other. And now you're reducing the opportunity for there to be loss. Because when you take light on and off chip, there's the coupling efficiency, which you'll lose light as well. and then it propagates through fiber optics, which is less of one, but it's still losses there. Now, if you put them on the top, there's no propagation. Compared to that, there's less propagation risk there. So we'll be doing that at inception as well. And there's also the assembly side of things. The assembly side, you have to do a lot of, you know, making sure that you put it together in the lowest way possible and you're optimizing it from an architectural point of view as well. But yeah, the answer is yes. It's a huge yes there.

Speaker 14

Kings of Karina Canaccord. Thanks for doing this really helpful update in encouraging advancements over the past couple of days. So I think one of the things that was talked about in the advancements was commercializing photonics IP outside of quantum computing. And just want to check if there's been a change in stance within the company, like maybe that there's more potential to do that.

Speaker 18

And if that could be like a revenue driver in advance of 2030 yeah it's definitely something that we're continuing explore what I'll say is maybe the bottleneck or the barrier to being able to fully push on that is just understanding how much of a gap there is between RIP and a dedicated product you know packaging Christian mentioned we have world-leading packaging capabilities so the ability to move light on and off chip this is something I'm going to say every single hyperscaler out there is super interested in as pico joules per bit transferred is now one of the major metrics that they worry about so ultra low loss coupling is important but the process we're going through is understanding and is there a gap if any between what we have and what they need if there is do we want to build that product on our own and what would be the cost and deviation from our path do we want to partner uh with somebody in order to be able to do that so this is just uh what's happening internally i can tell you we've been progressing on that thinking so uh it's something we're going to definitely continue exploring but still still a priority uh and still a topic internally yeah thanks it's reuben roy from stifle i know you guys are calling this the technology roadmap but michael maybe just high level on the uh funding congrats on the 195

Reuben Roy, Analyst — Stifel

million CAD. How much of this is fully funded? Any changes to the spending, you know, kind of forecasts or how you're thinking about spending against the new roadmap?

Michael Trupeck, CFO

Yeah, I know I'd reiterate some of what we shared. So as we talked about in the last earnings call, you're certainly going to see R&D and CapEx increase in the second half of the year. it's also as we move forward that'll start to accelerate but we've said that in the past at this point we're not ready to update our full outlook we're in the process of continuing to evaluate that i would say there's exciting work as we do that you you hear some of the things that we're working through in some of those and so we're in the process of optimizing around those decisions and when we're ready happy to update on that yep hey

Christian Weedbrook, CEO

guys thanks for putting this together Jesse Solveson with BTIG just curious why the range in logical area guidance for 2028 is it all just loss reduction or is there any dependency on the the error coded error codes being used or any other factors well the range was more about you got to pass through all those 10 to the minus numbers as you're reducing so the range is once you hit that you can fairly quickly start reducing it so in that particular year I think 2029 we expect to see orders of magnitude in loss in range of the error rate being reduced so it's just in that year over that 12 month period we'll be passing through all those error rates.

Paul Trevor, Analyst — RBC

Thanks for taking the questions Paul Trevor from RBC you mentioned building the qubit factory is probably more challenging than reducing losses. Can you elaborate on that? You know, what's the challenge there? And then what's the path to reduce or to create the qubit factory?

Christian Weedbrook, CEO

Actually, it's both. So the qubit factory is the most difficult part of the system. And the biggest challenge there is loss reduction. So they're not the same thing, another way to look at it so it's just the reason why that part is the most challenging is more components and more configurations and more stuff happening in the creation of the qubit that's where the bulk of the work is done in our system and then it's like well what's the biggest challenge there it's losses there's so many more places where loss can go second question also on the that the roadmap logical qubits going from 200 to 500 to a thousand plus implies slower growth is that intentional?

Paul Trevor, Analyst — RBC

And then longer term, what's the rate of scaling? Is it even relevant to think about the scaling of logical qubits beyond the time frame? Does that become the competitive advantage over time as a scalability of the growth?

Christian Weedbrook, CEO

Definitely a scalability, yeah. And I would say the clock speed for us, as mentioned, it's orders of magnitude faster than others. So even we expect to be one of the first, but if others do, as we expect, we'll be much faster than them. So I would assume that customers would choose us in principle over a slower modality with all things being equal. In terms of the slower jump at the start and scaling up more, that's really just a function of the loss reduction and the error rate above it and the row above it, bringing that down faster.

Atif Malik, Analyst — Siri Group

Atif Malik, Siri Group. Christian, you mentioned you solved the networking problem.

Christian Weedbrook, CEO

If you can just give a bit more background, how big is that challenge across other modalities like neutral atoms or superconducting annealing it's really it's really challenging because the idea if you you want to have hundreds of these quantum systems and you need them to talk to each other and so light is the natural choice to to network these things together the idea is let's just say you've got hundreds let's say you have two so you have two two systems for us the same photons we're using here are the same photons that have been distributed through the fiber optics to the other side to compute as well you basically what you're sharing is entanglement. That's the networking side of things. For other approaches, if they've got electronics in these two systems and they need to connect, as mentioned, IBM is using a different sort of tube sort of thing. It's a coaxial cable that allows whatever you're doing here to funnel it through. It's not really scalable, but others are actually going to connect using photons. And the challenge there is that you've got to convert from electrons to photons because you're distributing it, and then you've got to convert again to electrons. This conversion process is very noisy. It's very lossy, so they'll start having to deal with loss. That's the other thing to kind of note is most other approaches are going to have to have their own loss metrics as well, which is kind of interesting. And then the other thing is probabilistic. So if you press an abstract button and you want it to distribute across, you have to press it, say, 10 or 1,000 times or more. Just keep pressing until it happens because it's not a deterministic event. So that means now you've got to scale up more, so you've got to have a factory of sorts in order to overcome the probabilistic nature. The more things you have, the more likelihood that that event that you want actually does happen.

Speaker 18

I'll just add one other note from the algorithmic side. If you look at our modularity, the connectivity between two modules is the same as the qubits within one module. So you shouldn't be thinking about this as like a data center where you have, you know, really good computation on a CPU, but the moment you put two together, there's some bandwidth limitation. That's definitely true for like the superconducting folks or the trapped ion folks, where they have a good cluster of connectivity, but then a very thin connection to the other cluster, which really provides challenges when you start thinking about compiling down efficiently to actually run a utility scale algorithm. In photonics, you have the advantage that that all-to-all connectivity goes across the modules. So it really is a truly modular architecture, not a kind of independent systems network together.

Harshkumar Bhima, Analyst — BMO

Thanks. Harshkumar Bhima. So a couple of smaller, sort of like housekeeping-type questions. You mentioned system sales as one of the avenues for commercialization.

Speaker 18

Can you elaborate? would you stick to research organization universities or do you plan on selling your computers to corporations yeah I mean I think very naturally given the how these systems will roll out you know we will focus on the applications the cloud access as we can provide more value I imagine our initial customers for system sales will be ones with data that is so sensitive that they do not willing to actually submit it you know naturally you can think of nation-states probably can figure out who those customers would be. But then also that means that we would be able to price it accordingly to the value that it will generate, as opposed to what a research institution or maybe an enterprise would be able to pay.

Harshkumar Bhima, Analyst — BMO

And then most of the other companies we talked to say to get anything logical, like practical done, you need 100 working or logical qubits. Is that the threshold for your technology as well?

Speaker 18

A couple of hundreds. So we have a number in that 100 to 500 logical qubits. We have now a good handful of applications that our commercial partners have identified as multi-billion dollar problems for them. And that's really kind of what we're focused on. Now, there's the hope that there'll be a breakthrough that will move that to a smaller number. But yeah, hundreds of logical qubits, billions of cases, where we believe that is.

Harshkumar Bhima, Analyst — BMO

And sorry, my last one. You know, we've been sold this vision that quantum computers will sit right next to traditional computing, you know, and then work together harmoniously. Is that your vision of how quantum data centers will be, or do you envision standalone data centers with technologies such as yours and others?

Speaker 18

They'll be standalone, but it's because the coupling will be a lot tighter than you expect it to be. So one of the really powerful things of Penny Lane is that heterogeneous compilation. So right now if you go to a data center like LRZ in Germany, they've bought a quantum computer, they have it, they have a network cable running from the quantum computer to their data center, that's useless. That connectivity is going to be incredibly close, incredibly low latency, the user is not even going to feel it, they're going to take their program, they're going to compile it, pieces are going to go to the FPGA, the GPU, the CPU, the QPU, and they'll come back. But ultimately, that whole thing is what we're gonna call our quantum data center.

Kevin Garrigan, Analyst — Jefferies

Yeah, hey guys, Kevin Garrigan from Jefferies. So you gave some metrics around Penny Lane. Software's obviously gonna become very important in the long run, but you also have everyone saying that they're a full stack company. So just what are kind of the biggest KPIs that you want us to track to really let us know that Penny Lane is continuing to lead in the software race?

Speaker 18

Yeah, I mean, I think user adoption, and it's a little bit difficult to track that, but we have the number of downloads right now publicly available sourced through PyPy as the package is open sourced. The Unitary Foundation has a survey every year. It's a little bit limited in scope in terms of who it attracts, but it's still a very good signal. University engagements, I believe we're now over 150 universities. The number's higher, but I think that's the one that we're putting out publicly. Over 250 courses taught at the undergraduate and graduate level. but really this number one metric really out there is looking probably at the top researchers, so folks at national labs, at top enterprises that are using and building on top of Penny Lane in order to drive applications right now.

Speaker 10

I had to say, John McPeak from Rosenblatt Securities before so non-Clyfford gates can you talk a little bit about universal gates and how you plan to deliver those over the timeline and whether they're still at that same 100 to 1 ratio?

Christian Weedbrook, CEO

Same ratio, yeah, that's just across the board. The non-Clifford gates, in our case, are non-Gaussian gates. You know, the T-gates would be the most common version of that. What I can say about that is, if you look at our last big quantum computer, Aurora, all the Clifford, or in our case, Gaussian gates, along with T-Gates and magic states were another way to create the non-Gaussian or non-Clyfford states as well. All the components and all the sort of stuff you need to sort of create them were in our Aurora quantum computer. And it's kind of getting a bit old now, but the thing that really brings it alive is loss reduction. So all the parts for Clifford and non-Clyfford gates, they're all there, they're all working, but they're working badly. at least three years ago when we had the Aurora computer. But the good thing is everything is there. We just need to hit that loss reduction roadmap.

Craig Ellis, Analyst — B. Riley Securities

Craig Ellis, B. Riley Securities. I want to start on a software slide and use it as a way to understand how you're thinking about the broader business. So the software question on the slide that presented application development and application interaction, I thought it was impressive because there were 15 entities that looked like partners that were true international blue chips. And the question is, to what extent did the company one, two, three years ago have ambition to create an international set of partners, or did the partners find you as the first part of the question? And then the second part of the question is more for Christian. Christian, as I think about what we heard today, we heard a lot about loss and error rates and system optimizations and things that are driving us forward to something more performant. but we also heard about Penny Lane. To what extent are those two really inter-independent roadmaps versus there being interdependencies between the two? And if there are interdependencies, what are they and what should we be looking at as things you need to execute there?

Christian Weedbrook, CEO

I think they're definitely interconnected. Because Penny Lane, we often just think about it as, very roughly speaking the operating system for the quantum computer way to log in and program quantum computers but it's a full stack within itself so we often say full stack as a company does hardware software but those things themselves are a full stack and so penny lane itself is a full stack where you've got the kind of higher level stuff that maybe people are more associated with penny lane but as raf mentioned as you go down the stack you get to the compilation stuff which is, roughly speaking, related to the optimization of quantum and classical hardware. And as you go down further, you also have the error correction side of things, the software associated with that. Then you go down even further, you've got all the stuff that we keep in-house until you hit the bottom where you've got the hardware and the software controls, say the voltages of which lead to the gate control of the chips themselves. So you can imagine now, getting back to your question, that all of these things are deeply related to the hardware world, particularly when you look at how you control the hardware, that's obviously related. Then you've got the error correction codes and how they can get implemented and how you do the software for that, how you do the feed forward and feedback onto the system. Yeah, I don't know if you've got anything else.

Speaker 18

Yeah, no, no, I think that's really valid. And maybe one thing to add is, it's a word I hate, but the term co-design, I'm beginning to hate a little bit less. and it's just because algorithm developers come up with some really crazy ideas and if you're not paying attention as a hardware developer of how to implement that it could be actually pretty limiting and there's a number of times now that I've been in conversations with the Department of Energy or other favorite three-letter agencies where they're like this is how a quantum computer is going to work and then somebody whips out a paper and it's like have you seen what these algo developers are doing? they're going to want access to the error-correcting code and be able to program it. And then everybody's model fully breaks apart. So having that really close tie between software and hardware, both internally from our software team to our hardware, but then also to our algo team and the broader universe of researchers, has been incredibly powerful to make sure that the hardware development doesn't paint itself into a corner in building a quantum computer that won't have the capabilities that people actually want. So that's really powerful. To your first question, it's been a mix of both. So some of the companies that are in that list, and it's not comprehensive. There's other companies we have and continue to work with. We definitely pursued. We identified them as, like, these are great researchers with an enterprise, exactly the people we want to work with. We build those relationships. Others, like Lockheed Martin, had been using Penny Lane for six years prior to a commercial engagement with us. And some of them we know about while they're using it because they come out, we've seen them on our forums, we see them through our other platforms as using the software. Other ones are complete surprises. Somebody shows up at our door and says, you guys broke this feature in Penny Lane, it killed our entire stack, and we're like, we didn't know if you had a stack, right? Like, let's talk about how we formed a partnership now. So it's always a great surprise, but we've been definitely surprised with not only how many developers are out there using Penny Lane, but how many enterprises out there have actually built internal product on top of Penny Lane, and now we're coming out to form partnerships with us.

Adrian Colby, Analyst — Citigroup

Adrian Colby, Citigroup, thanks for taking the question. You touched on the scarcity of quantum talent in your remarks, and I'm interested in how we should think about the head count that's necessary as you scale as a company and if that's a bottleneck?

Speaker 18

Yeah, so, I mean, luckily I think we've hit a bit of a transition point with that Aurora paper. It was actually a pretty big milestone where, I'm going to say we turned from a research and development organization into a development and qualification organization. So on that raw quantum talent, we are hiring now much more from semiconductor engineers than we would be for, let's say, quantum optics people in the past. On the algorithmic development, it's still definitely a thing where there's a challenge within the industry as a whole. I think we're in a unique position, both where we're located geographically and that we're able to attract talent from globally and have very little barriers and work closely with the Canadian government to be able to bring those people into Canada and be able to tackle it. And then also the fact that some of the leading institutions in quantum were early on developed in Canada, so a lot of that talent tends to go through our backyard, even if it's not grown in our backyard, between the Institute of Quantique in Quebec and the University of Waterloo and IQC. It's always something we keep in mind. We've been incredibly lucky in being able to track the top talent so far and really have not too much worry that it won't continue into the future.

Brett Harris, Head of Investor Relations

Any other questions?

Nehal Choksi, Analyst — Northland

Thanks. So the 10 to the negative 16th error rate is indeed very impressive. I think telecom standards are kind of known to be very rigorous, and 10 to the negative 12 is sort of like the error rate that's required, I believe. So what applications are you expecting that will benefit from getting all the way down to 10 to negative 6 error rates relative to the next closest competitor talking about, 10 to negative 12?

Christian Weedbrook, CEO

Maybe I'll let Raf answer the application side of things. But my biggest excitement about quantum computing is the applications we don't know about yet. There's still so many out there that we do know about. You know, we're focused on material design, quantum chemistry, and things of that nature. Pharmaceuticals will be one. But the idea for the stuff we don't know, lower error rate is always going to be better. And presumably, the lower you go, the more applications can open up that we don't know about either. So that's important. I would say more about the other companies is it's just potentially it's harder for them to get to those error rates. And it's much easier in principle for us to get to them, as I mentioned before. So I don't think, I don't know. I just don't know how low they can actually go. So, and then I've seen also others, as I mentioned, 10 to minus 12, I think, is the lowest I saw with roadmaps for a given time period. And other ones are 10 to the minus 10, for instance. So we're very aggressive with our 10 to the minus 16. It's because we can actually get there. We see the pathway to getting there. And the other take, too, that not many people say is no one really knows, honestly, no one really knows what the error rate you need is. It'll depend on the algorithm. As I said, with algorithms, you don't know. You have no idea, but you need to get lower. But there's even some skepticism whether 10 to the minus 10 is actually low enough or 10 to the minus 12. So why not go as low as you can, the reasons I mentioned, and the fact that our photonic architecture and hardware actually allows you to get there.

Speaker 18

Yeah, I mean, I think probably the most famous one is breaking RSA, right, where you start needing to push that. Personally, it's my retirement plan to take a third of all the Bitcoin, so that's why 10 to the minus 16 is important. But more seriously, 10 to the minus 12 is right now just on the beginning of those applications. And you mentioned that 10 to the minus 12 is a standard in telco. But if you had your CPU operating with an error rate of 10 to the minus 12, you would throw it in the trash bin, right? You wouldn't be able to do much of the stuff that you do. So in compute, we expect error rates much, much lower. I don't know what exactly the error rate of a CPU right now is, but I can tell you it's much, much lower than 10 to the minus 16. So on the compute side, like Christian said, Instead, the applications begin in that range of 10 to the minus 12 to 10 to the minus 16 of what we understand right now, but you definitely want to push higher because it will open up bigger and bigger capabilities.

Nehal Choksi, Analyst — Northland

Great. That's helpful. Probably for you, Michael. In the agreement that was published to the SEC on Friday, it indicated that the Canadian government's funding against a up to an 895 million project cost so is that 895 million dollar project cost effectively the same as the billion dollars that you guys talked about earlier in the year to build your fault quantificator no no different yes all right uh so then obviously that building is much more encompassing and that project there is just a part of it what part of that canadian-funded project does that represent the overall billion then uh it is a big part of

Michael Trupeck, CFO

getting through the next few years it's uh and it's um our new packaging facility uh it's the research that we're doing uh to get to the data center so uh as christian laid out you know the slide that either of us could have presented there's numerous various sources for us to get to that data center so this is just one of many sources that's gonna help us get to that point.

Nehal Choksi, Analyst — Northland

Is that 895 million inclusive of the R&D personnel cost?

Michael Trupeck, CFO

I actually don't know the source the original source of the 895.

Christian Weedbrook, CEO

Yeah the project open was $893 million.

Michael Trupeck, CFO

Yeah, but it's inclusive of the work, the R&D work that we're doing, yes.

Nehal Choksi, Analyst — Northland

It is, okay, great, thank you.

Brett Harris, Head of Investor Relations

Going once, going twice. Oh, back row. We were so close.

Julian Frost, Analyst — Wedbush Securities

Hey, guys, thanks for doing this. Julian Frost with Wedbush Securities. So a lot of investors think that simulation, like chemical materials, are probably among the near-term applications that'll be most accessible as we get into like the hundreds of logical qubits. So does Xanadu share this view? Do your early partners have certain application areas they're most interested in working on or building towards as we move from your build-out phase to the fault tolerance phase in the near future?

Speaker 18

Yeah, it's one of the earliest areas that we kind of focused on. I'm going to say there's a number of applications we've published on, we've disclosed publicly that are in the material and chemical space, battery development, next generation sensors, both classical and quantum, light matter interactions, so photovoltaics, photosensitive drugs, photoresist for semiconductor industry. This is just a handful. If you go to our website, you'll be able to see a list of them. But I'd say in addition to that, there's been some really exciting advances, both at Xanadu and externally, that have opened up other sectors. So I'd say we are definitely looking at financial services a lot more. We published some of the original papers on quantum for finance with quantum Monte Carlo for pricing derivatives. We've gotten a lot more sophisticated since then. BUT IT'S REALLY BEEN THE BREAKTHROUGHS IN THE LAST YEAR AROUND QUANTUM MACHINE LEARNING AND OPTIMIZATION THAT HAVE OPENED THAT FIELD BACK UP. SO WE HAVE A NUMBER OF PARTNERS, I THINK, F-CAT HAS A PUBLIC ONE THAT WE WORK WITH ON THAT AREA AS WELL.

Julian Frost, Analyst — Wedbush Securities

THANK YOU.

Brett Harris, Head of Investor Relations

GOING TWICE. I'D JUST LIKE TO TAKE THIS MOMENT. THANK YOU ALL FOR BEING HERE. AND WE'LL BE AROUND TO CHIT-CHAT WITH YOU AFTER. SO THANK YOU.