Hello everybody. Welcome to our 2026 roadmap presentation. 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. The other thing is this slide presentation will be available on our website at 4:15, so no need to frantically take screenshots. You can read the disclaimers at 4:15 if you so desire. I think with that, I'd like to invite up Christian. No claps? There it is. Just got to warm up the audience, that's all. Thank you. Do I use this or not? Your mic. Thank you, Brett, and welcome everyone. Really excited today to be here to tell you about our roadmap particularly, and a few other things, both hardware and software roadmaps. It should be a lot of fun, and particularly looking forward to hearing any questions you may have as well. We've got a really good turnout, which is awesome to see. Thank you everyone for making the trip today. 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. 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. 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. 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 logical qubits, and also the cloud-deployed nature, where PennyLane is also wrapping together everything here. Some of the slides, including this one, is what we presented, I think five months ago now at our first Analyst Day. 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, 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. Also our first on-chip photonic GKP qubit. GKP qubits, don't worry about the name too much, it just means Xanadu's photonic qubit. Something you may have seen on Friday, along with the CAD 195 million announcement from the Canadian government, is Inception. Inception is a nearly 160,000 sq ft Toronto manufacturing facility that was announced I guess 3 days ago, and we'll talk more about that. From a software side of things, PennyLane, obviously a big marker there, was released 8 years ago. Launched the world's first commercial photonic platform, so you could access quantum computers over the cloud. You'll see some of the next stuff that we'll talk about in the coming slides, including re-architecturing PennyLane for scale. Very important thing. You just can't have PennyLane from day one accessing and building out scalable quantum computers. This was announced, this is converted into USD, but it was CAD 195 million from the federal government for our project that we call Project 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. This is what we alluded to five months ago. This is part of the money. This is the federal side of things. Stay tuned for any announcements soon related to the provincial side of things. This one's announced first because it was in discussions initially. Use of proceeds to establish an advanced manufacturing facility. That's why we announced Inception, which I'll talk more about soon. 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. 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-performant quantum computers, and you have hundreds of them and network them together. This is the only demonstration, it was published in Nature by any company, which shows how to solve that in a scalable manner. 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's 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 we had four server racks that are networked together. Could have been 40 or could have been 400. We know 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 we're orders of 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 8 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. That is what you will see. You will see some basic things about why logical qubits are 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 is on the order of 100- 1, so 100 crappier physical qubits gives you one very good or logical qubit. Everyone, including us, to be fair, we try to be as balanced as possible. We have said we have got 12 logical qubits. We do, but the devil is in the details, and there are many other things to really care about, but it is still an important first step to get some logical qubits. Others are saying, other companies are maybe saying two to one or 20- 1 or something like that. I can categorically tell you that is not true. If it is true, then it also applies to us, meaning that maybe 20- 1 is fine, but the logical error rate is disastrous. Or maybe it is only correcting a certain subset of errors. Basically, in order to make real money and in order to have significant customer adoption and impact, you need to have these numbers here. For us, we actually have roughly an order of magnitude better overhead there. Less overhead means less capital, means less hardware. That is a huge benefit for us. 2-qubit gate fidelity, we are up there with the best, I think tied in this case. Our 2-qubit gate is actually a beam splitter, one of the most famous optical elements ever. That becomes our 2-qubit gate. Connectivity is all to all, along with a few of the others. Room temperature computation. This one is always challenging to hear. We actually have seen other decks by other competitors. I love looking through them and gauging where they are at, particularly the public companies. Everyone has these crosses and all of that, and we recommend everyone dive into this and see who has been truthful about it. Often you will see a cross near photonics, but they are talking about, say, PsiQuantum 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. I do not mean cryogenics, and I do not mean laser cooling either. Cryogenics operates around 10 to the - 3. Laser cooling actually operates at 10 to the - 6, so 1,000x colder than the others using laser cooling. It is kind of a cool technology, literally cool technology. The room temperature side of things, we truly operate. The others, photonics, not all photonics are created equally. Other photonic-based approaches need to have cryogenics everywhere. That adds cost, iteration time, slow down. Superconductors, they have the famous chandeliers. Ion traps and neutral atom use laser cooling, so they do not have cryogenics, but they have laser cooling. They still need to go down cold as 10 to the- 6 for that sort of thing. They sometimes say they are room temperature. I do not know how. Maybe it is like saying some of their part is outside, but their fridge. It is hard to sort of justify. But the photonics technology and roadmap, we all have that aspect of things. You are 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 have a chandelier here and a chandelier here, and they have a tube, a coaxial cable, where they actually are not sharing photonics. They are sharing still whatever is in the electronics, the Josephson junctions, and the qubit is created from that between it. So they are looking to scale up there. Neutral atoms are talking about shuttling things around. That is how they get all connectivity, but they still need to have photonics if they want to go beyond that once they have kind of maxed that out. Just some of the benefits, I think I have mentioned them. Superconducting, significant cooling and networking requirements, lower gate speeds, blah, blah. I have mentioned all these sorts of things, but you are welcome to ask me any questions after that. Okay, this is one of those kind of educational slides, and I do not 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 them. I am sure you know about this one. Basically, you can build a qubit using photonics, which is what we are 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 is 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 is a thumb. 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 is a lot of great approaches and a lot of great people out there. We believe we will be one of the winners, but we do not think that it is going to be a winner takes all market. It is hard to really think about too many industries where it is 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 with search. But for the most part, it is very, very rare, and we think that we will be one of the winners, and there may be three to five winners at least this decade that can kind of scale up to a large scale quantum computer. Beyond that, it is always hard to Becomes less about the technology and more about traditional business metrics and strategies. So the benefits of us, I have talked about it already, no cooling required for computation. We do need some cooling, that is the key aspect. But we do not need it for the qubits, the gates and the measurements. They are 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. Bless you. Manufacturable using silicon processes. 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. 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 GlobalFoundries and actually using it. It's a very different thing. With us, we're actually using GlobalFoundries, Tower Semiconductor, 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 approaches, to my knowledge, you can't sort of send an ion trap or superconducting qubit, 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. 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. What that means is we can save time and money by having a more generic system. That comes back because we're using photonics and we have this all to all connectivity. That's going to become more and more important over the coming years. Compatible with telecom. We didn't have to invent the laser or optical elements, fiber optics and so forth. Faster clock speeds, I mentioned, and the modular 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. Every modality is judged on the same fundamentals, fewer errors, lower error rates, and more logical qubits. Today, that's what we'd 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, then you also ask them about what type of errors that they have. These are the three things to kind of be aware of, essentially. Logical error rate, you can think of that as, remember there is like 100:1. You have 100 worse qubits, that creates one very good qubit, a logical qubit. Those worse qubits, they have gates acting on them. If you can get the errors below a certain level, then that is when the logical stuff kicks in. You can kind of think of it this way. If you add more crap, you are not going to get good stuff until you are below a certain level. You cannot take advantage of that. It is just a lot of crap and a lot of noise everywhere. This is the logical error rate, where once you are below a threshold, you can keep driving it down lower and lower, and you often see metrics like 10 to the minus blah, blah. 10 to the minus, getting back to what some folks say is logical qubits, if you have a ratio instead of 100:1, but 2:1, but your logical qubit rate is 10 to the - 3, it does not matter. The error is not low enough. This is an important slide, key technological indicators. For any hardware, including obviously ours, real hardware is always imperfect. It does not matter what anyone says. It does not 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 are getting are so low that you are going to get the right answer, which is ultimately what we all want to do. 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 as characterized as decoherence, so the loss of coherence. We all have decoherence. It is 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, also the sucky thing about our approach is we can just sort of say, we do not have all those other things. We just have one source of errors that we need to overcome, and that is what we suggest everyone here focuses on, is loss reduction. As it says there, in Xanadu's photonic architecture, nearly all these imperfections reduce to one primary challenge, photon loss, whereas other approaches has a whole lot of them. Still solvable, to be fair, but it is nice to sort of think you just have one. We understand that, then it is a case, well, how are you going to deal with loss? How are you bringing it down? There are two ways to reduce loss. It is through hardware improvements and through theoretical architecture improvements. The hardware improvements is essentially wherever light goes, it has a chance to lose photons. When you start the laser and all the way to the end, some of the photons get absorbed, they get scattered. That is a problem because the photons encode information. That means you are losing and absorbing information, which is not good. 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. 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." You see, you are reducing loss by the hardware and improving the loss balance. You are trying to reduce this gap, and then you want to get that gap to one, and then you have everything you have 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. You can think of it as if you have too many elements on the beam, on the wafer, on the chip, then you have got more loss. If you can reduce elements while still maintaining the same functionality, then you can actually have a lower loss system. 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. Every few weeks, we are getting back chips and wafers. Then you go down to the testing. You will hear about our new facility soon. We can do test and measure there. That is really important. You do test and measure for us to get more information. It is like, well, it is more lossier than what we thought, so we will have to alter it based on this feedback. We have a dashboard and sort of repository where we have all the data information. Basically, you can kind of see this iteration process happening more and more. This is related to the hardware side of things. I cannot 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 have seen the benefit of AI, significant benefit, is using LLMs for the chip design and bringing down the loss there. It has been huge for us, and I think we are 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 could not show any forward-looking statements, so we had it from 2023- 2026. Here we have, think of it roughly speaking, as loss. It is more of a loss gap you are trying to close. When you hit one, you have closed it. Along here, we have the timeline. 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 is overlapping features there. There are harder things to do on this one. That is the first thing. The second thing is our optical system has approximately 17 different optical elements associated with a quantum computer. We have loss where we are today and loss where we need to get over the coming years. We have aggregated them in such a way that instead of having to track all 17, we have an aggregated line for that. That is where that comes from. The other thing, it is really important, this is what we think everyone should judge us by is this dotted line. Remember, you want to get down to one. I think roughly, you can see this log scale here. Roughly, this is 2. 1.5 is break even on this scale. So break even means that you have hit the threshold. Now you need to go below. As soon as you go below, you start racking up 10 to the -3, 10 to the -4, 10 to the -6, -10, and so forth. Fairly quickly after that. The key is getting to that. In this case, it is normalized to 1.5. The other thing that I would kind of stress is that this is a midpoint. If you think of it could be above or below. It is like a sort of a sleeve that is error bars that encompass it. The key point is making sure it is headed down and making sure it gets to one. It is also not something that happens every day. Every quarter, there could be an announcement. On average, you are heading down at the end of the year. So far, if you look at, say, this year, we are on track to hit the target for end of 2026, which is here. We are on track for that. Okay, so crossing the threshold, we need error reduction. In our case, it is loss. Everyone has their version. We need error correction, and that equals a logical error rate. I have already talked about a lot of these. So threshold driven performance. The thing that everyone needs to start questioning and asking is what is the error rate? What is the logical error rate? That is the key thing. Not really now, but over the next coming years, that is going to get more and more important for folks. 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 one, then the error correction turns pretty good hardware, the physical qubits, into almost never wrong logical qubits, the good qubits. That is that ratio, in our case, 100- 1 now. The other thing to keep an eye out, that applies to us too, we have said we have got 12 logical qubits. I think maybe QuEra says they have got 35. None of them are equal. It is not apples to apples. It applies to us too. So not all logical qubits are created equal. They only become equal when you also compare the error rates. 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, you can compare apples to apples. That is where the devil is in the details. It does not matter how many logical qubits you have. That is not the whole story, but the error rate completes the whole story there. The error rate, as mentioned, is how long can you compute before the state decoheres, before it becomes crap? 10 perfect qubits cannot solve a useful problem. 1,000 that fail after 10 operations solve nothing. There goes the extreme of either of them, so you want them both coexisting. To sum up a lot of stuff, key indicators, loss, key indicator, logical error rate, key indicator, logical qubits. In between them, you need error correction. We have a whole family of error correction codes, unlike others, that we can choose from. 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 is in a server rack or a chandelier, and then you need hundreds of these things, and you need to network them together. We have solved the networking piece. This is very much a blueprint for anyone doing quantum computing. How are your errors? How is your error correction? What is your logical error rate? How are you doing with networking? How many logical qubits do you have? It is kind of putting it all together. Inception, as mentioned on Friday, we announced CAD 195 million to build, among other things, our new facility, which is called Inception. We moved into Inception. It is a huge warehouse, 160,000 sq ft. There is nothing in there yet. It looks really cool. It is huge. We will be using that money along with our own money to populate it out. There are a few things we will be doing in this facility, and it really is state of the art. There will be some of these machines that we buy that no one in America even has some of these tools. They are that state of the art. It will be the best in the world in terms of low loss for our architecture. Some of the things and the capabilities, test and measure. I mentioned that flywheel. You get the wafers back, you have to test them, you have to understand, like, is it a performer? Is it not? Get some data there. We will be able to do that ourselves. Heterogeneous integration, that just means two different types of chips essentially glued together. We will 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 will be doing that. How do you start assembling some of these things together? It does not matter if you have got them all individually, you have got to start putting them together. 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 Erie, and they were, I think, in Ohio. It would take up to three months to actually package it, and that was fine. But now that we're doing it in our CAD 10 million one as a precursor to this, it was done in eight hours, three months to eight hours. 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. Obviously all of this helps with loss reduction as well, which is our key challenge. Okay. I think this is the summing up slide here, yeah, before I hand it over to our COO, Raf, to talk about software. This is kind of the take home. This is our roadmap. We have time along here 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 break-even point. You've got these numbers from the plot. Logical error rate. This is the first time we're talking about Xanadu's logical error rate. This is what's really important. There's a couple of things that I would say as 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 and 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 -12, something like that. We're far lower than anyone else. That's the beauty of the photonic based approach. The other key thing is we've announced today, over 1,000 plus logical qubits in 2031. Previously, we announced up to 500 in 2029. We said 2030, and up to 200 before that. This is our roadmap, and it comes back to what we've been talking about this morning. How many logical qubits? You're seeing no physical qubits there. There are physical qubits, but the key thing to judge on is logical qubits and the error rate. I don't think anyone is claiming that they can get to this. This is the great, amazing thing about photonics, because the network ability, you keep adding machines, and the error rate goes lower and lower as adding extra hardware. That's really exciting, and then we can break these phrases up into less 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. Then start building the quantum data center in 2030, 2029, 2030. In order to achieve loss reduction and start making sure we achieve our goals, we need to open up a facility, Inception. That was opened 2 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. Thanks. I will assume that applause was for me, not for Christian. Thank you, Christian. 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. PennyLane 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 is an issue in quantum computing right now in that there is a large amount of investment happening into the discovery of various quantum algorithms and applications across many industries. But fundamentally, we are 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, 1950s of digital compute, thinking about breaking codes and solving chemistry and physics simulations, but nobody is 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 is 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 is 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 are looking at NVIDIA CUDA-Q, you get tied to NVIDIA GPU accelerators for your workflows. There are 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 provide applications for utility-scale quantum computers. To give you an idea, today you are dealing with handfuls of qubits deployed for tens or hundreds of gates if you are running on NISQ-era hardware that is publicly available. If you are going to be running on a utility-scale quantum computer, you are going to be working with hundreds to thousands of logical qubits, operating billions of gates over thousands of repetitions, so trillions of steps. These programs today are not able to be captured by the tooling that actually exists. Then finally, adoption does require effort. Nobody wants to pick 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, 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. NVIDIA did 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, commodity compute, that was able to provide huge amounts through a software layer of value. This is exactly what we're looking to do with the entire PennyLane family of programs. Not just PennyLane on its own, but also Catalyst and Lightning, 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. These range from existing ideas that we have around materials and chemistry, out to pharma and drug discovery, to cryptanalysis and broader optimization in places like finance systems. This is really built on top of PennyLane as that software development kit, for our universal fault-tolerant quantum computers. Over this year, we've embarked on an enormous effort to actually raise PennyLane from something that was previously targeted at NISQ 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 continued 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 of our examples, we were able to take a program and reduce the computational resources by a factor of 10, just by applying 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. 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. We've deployed those with Lightning Simulators that are now available as part of PennyLane as well. The really important part too, that I'm sure everyone here knows, is that PennyLane 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, already for superconducting, trapped ion, neutral atom, and photonic. We have a number of publicly available devices that have built-in plug-ins within PennyLane, so you can access those. Also on the classical compute accelerators. We have longstanding 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'd mentioned already, 30% of quantum developers out there have adopted PennyLane within the last year. Over 1,800 publicly available software projects reference PennyLane 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 PennyLane, Catalyst, and 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 PennyLane. 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 on all modalities, Catalyst and just-in-time compilation having the huge performance boosts. Christian identified the importance of error correction, and this is something that is fundamentally built into PennyLane at its core level. The other big part was originally when PennyLane was being built out, it was considered to be a quantum machine learning software stack. This was, I'm going to say some of the researchers at 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 PennyLane 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 funnel that's allowed developers to come into our software stack. According to the Unitary Fund, over 50% of developers within the quantum space cite 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 close to 600,000 codebook submissions. This is our coder size 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, but 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, though, that we released recently and we're able to talk about is our partnership with Lockheed Martin Corporation, 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 Corporation's not our only partner, and education's 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 the computational fluid dynamic applications, with folks like Mitsubishi Chemical Group on next generation photoresists for semiconductor fabrications. 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 Braket, PennyLane 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 PennyLane, 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. Then there's a wide range of companies like Agnostiq, QunaSys, and Menten AI, that are quantum startups that are actually fundamentally building their businesses on top of PennyLane 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 and the broader community, allowing them to contribute back into our code base. PennyLane itself has over 2,500 scientific citations, over 75 outside contributors that are pushing code, and improving in PennyLane 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. This just drives 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 PennyLane. 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 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 PennyLane to be able to provide future products. There's many ideas that we're currently following, it's just getting an understanding of when is the right time in the journey, especially as hardware develops, to be able to deploy those. 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 PennyLane that could be deployed on our quantum computers. That, of course, will be a first-class citizen within the PennyLane ecosystem. With that, I'll pause there and I'll hand it back to Christian for a quick summary. Thanks, Raf. Great. Thank you, Raf. We are talking about four different ways of commercializing. This is also from our Analyst Day, it is a repeat of that. 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 is a key aspect of what we do as well. Really the world's leaders when it comes to that in ultra-low loss. As Raf was just mentioning, the other revenue market that we are after is commercializing PennyLane as well. Michael, is this your slide or no? I think it is quite self-explanatory. CAD 686 million of capital sources. We have a synthetic ATM, which we have mentioned before. I think everything is Canadian government funding, that is CAD 195 million from the federal government. That is a non-dilutive loan. We have the synthetic ATM as well, that is ongoing. Some of the key takeaways. The first pure-play publicly listed photonic quantum computing company. Deeply technical leadership and experienced board. Category leading technical position, particularly with the Aurora quantum computer. We solve the networking side of things. That is key to how you actually scale up quantum computers, not just ours, but nearly all else, all the others. Raf went over the PennyLane. It is really one of the leading software stacks out there. Targeting meaningful end customer commercialization by 2029, 2030. That corresponds to our large-scale quantum computer or quantum data center. As we have mentioned in the past too, the top 2 priorities for us does not 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 have built a large-scale quantum computer, and secondly, some partnerships with some great companies. We announced AMD 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. As mentioned, available funding that we have is CAD 686 million, and we also have a synthetic ATM. We will leave it there. Thank you very much for everyone's attention. Thank you. Turn it. Chair. I'll pass on to you. Thanks. Someone's running mics. I got it. There'll be mics that are going to be. Can you guys hear me? All right. That works. All right. We have a lot of questions. Excellent. This is a Q&A section. We have mics moving around, so if you could just start with John. Closest. Thanks for doing this, guys. 10 to the - 16, super impressive. How are you going to get to that kind of error rate? Could you elaborate a little bit on your confidence there too, Christian? Roadmaps and confidence. We're in the deep tech sector, everyone in quantum computing. There's going to be small error bars. That should be taken into account for us and anyone in the industry. We're 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. I didn't mention there, but 10 to the - 3 is where our threshold sits, just for the way it's been normalized for our system. We got to hit 10 to the - 3, and 10 to the - 3 is related to where we are with this loss reduction or this closing of the gap. 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 the loss targets. Then the more you keep adding, the lower that it actually goes in the error rate. 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, normalize to one. But you're still at that 100 to one physical to logical at the 10 to the - 16? Yes. That's correct. That doesn't change. Those numbers will be pretty fixed. It's proportional to that, but yes. You referred to this as copy-paste, I think, to me in the- Yes That's the evolution there. That's right. Thank you. Yeah. Yeah. Why don't you just pass down? Thank you. Yeah. Harsh Kumar, BMO. 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. This is the mechanism of error correction. I'm trying to understand, how do you know what's 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, it's going to be quite small, but it's for argument's sake, let's say the chip is this big, for argument's sake. A fiber optics goes in, a fiber optics goes out, and you need to pump or put laser light through, and it propagates along this distance here. What we wanted, and let's say there's no optical element, it's just propagation loss. So how much of the light it actually makes. Like you said, some of the light gets absorbed or scattered as it's going from A to B, and 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, like a traditional laser. And then you know you can calculate how much light you're expected to go in. Let's say if 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, B. Riley Securities. No offense, Christian, but I'll direct mine to Rafael. Although it may be a question for Michael. I really appreciate all the information on PennyLane 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's going to work and how we actually monetize it. Is it as a service, one time, seat license? How's that going to work? Yeah. I think in the long term, it really depends on how the industry does develop. What I can tell you right now that, in, for example, the educational offering, the appetite from our partners is very much as part of broader engagements and 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 PennyLane. That is the push. Because what we want is not, 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. I think great question. Something that we're continuously testing, discussing with our partners, internally and externally, but not a major driver until that utility scale quantum computer lands. Anything you would throw in there, Michael? No, I think that the third to the last slide that Christian presented, which was also from our first Analyst Day, covers it. 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 like to think of that as the starting line. But I think that slide does a good job of laying out our thought process. Great. Todd Coupland from CIBC. How does your position now with your technical spot in 2026 stack up with DARPA's Stage C expectations? If you're not there now, where along that line would you need to be to satisfy Stage C? Thanks. Yeah. The easiest way to sort of say it 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 cannot mention more until we find out how we're doing. But so far, so good. 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 will 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 start of the year, end of the year, seeing that go down and making sure ultimately we're hitting that value of one. Yeah. Joseph Moore, Morgan Stanley. Just a question on the software side. I want to understand something you said about CUDA-Q. 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 co-processor and simulation side. But what am I not understanding about that? Why would people be afraid of NVIDIA locking on CUDA-Q? Yeah. 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 a 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 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. It does look like people often think only of the simulation side, but there's an 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 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 plugin architecture that can support rapid development of those types of elements that you're able to compile down into. Yeah, thanks. Nehal Chokshi 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 is carefully thought out with respect to particular improvements in components, particular components. Given that context, what is that one photonic component or material parameter that is going to give that big boost, especially going from 2028- 2029? It really is the 17. There are maybe one or two that we are 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 is good, and then you need to go to the next one or the one after that to improve it. The way I would look at it is the one thing has already been chosen, that is 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. You can kind of say, "I have made more progress than I thought here," and that will allow the other one above it, for instance, to tolerate more. It is really hard to sort of narrow it down to one. Another way to look at it is, let us say you had 16 of them amazing, and one is really, really bad. You cannot do anything. So they are all really interconnected, and you really need to make sure that you are lowering the loss on all 17. All right. One other question, maybe quick, maybe not. Talked to you about this before, but it is hard to wrap my head around it. Why can Xanadu tolerate any photon loss at all? Because there are some other photonic quantum computing players out there that are thinking about, "Hey, we got to be able to do single photon detection, no loss of photons at all." Can you talk to that? The single photon ones, yeah, they will have loss. In that case, they're losing their whole carrier. 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. 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. To answer your question, ultimately, it's just the type of encoding that one approach compared to another approach has taken. We've taken 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. 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. Sorry. Are you the only photonic quantum computing approach that is leveraging the GKP model? Photonics? That's correct. Yes. Okay. Yeah. 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 repetition rate for theirs. But for the most part, yes. There are some in superconducting. Nord Quantique has it. Oh. The mic. Yeah. Thanks. David Williams with Needham. You talked about the 17 components earlier, and you've got Inception that you're building that's for a couple of different purposes, but 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? 100%. Yeah, for sure. Packaging, like you mentioned, is one. I gave that example where it was taking three months, and with last year we announced a CAD 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. It's for test to measure, which you can measure the loss and then iterate to bring it down. Packaging, you 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. It propagates through fiber optics, which is less of one, but it's still losses there. Now, if you put them on the top, compared to that, there's less propagation risks 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 making sure that you've put it together in the lowest way possible and you're optimizing it from an architectural point of view as well. Yeah, the answer is yes. It's a huge yes there. Kingsley Crane, Canaccord. Thanks for doing this really helpful update and encouraging advancements over the past couple of days. 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, and if that could be a revenue driver in advance of 2030. Yeah, it's definitely something that we're continuing to 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 our IP and a dedicated product. 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 picojoules 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 with somebody in order to be able to do that? This is just what's happening internally. I can tell you we've been progressing on that thinking, so it's something we're going to definitely continue exploring, but still a priority, and still a topic internally. Thanks. Yeah. Yeah. Thanks. It's Ruben Roy from Stifel. I know you guys are calling this the technology roadmap, but Michael, maybe just high level on the funding. Congrats on the CAD 195 million. How much of this is fully funded? Any changes to the spending forecasts or how you're thinking about spending against the new roadmap? Yeah, no, I'd reiterate some of what we shared. As we talked about on the last earnings call, you're certainly going to see R&D and CapEx increase in the second half of the year. 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 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. When we're ready, happy to update on that. Yeah. Thanks. Yep. Hey, guys. Thanks for putting this together. Jesse Sobelson with BTIG. Just curious, why the range in logical error rate guidance for 2028? Is it all just loss reduction, or is there any dependency on the error codes being used or any other factors? Oh, the range was more about you got to pass through all those 10 to the minus numbers as you're reducing loss. 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 range of the error rate being reduced. So it's just that in that year, over that 12 month period, we'll be passing through all those error rates. Thanks for taking the questions, Paul Treiber from RBC. Just on, you mentioned building the qubit factory is probably more challenging than reducing losses. Can you elaborate on that? What's the challenge there, and what's the path to reduce, or to create the qubit factory? Actually, it's both. The qubit factory is the most difficult part of the system. The biggest challenge there is loss reduction. They're the same thing, is another way to look at it. 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. 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 roadmap, logical qubits going from 200- 500 to 1,000+ implies slower growth. Is that intentional? Then longer term, what's the rate of scaling like? Is it even relevant to think about the scaling of logical qubits beyond the timeframe? Or does that become the competitive advantage over time is the scalability of the growth and- Definitely scalability, yeah. 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 then scaling up more, that's really just a function of the loss reduction, and the error rate above it in the row above it, bringing that down faster. Atif Malik, Citigroup. Christian, you mentioned you solved the networking problem. 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 challenging because the idea if you want to have hundreds of these quantum systems and you need them to talk to each other, light is the natural choice 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 systems. For us, the same photons we're using here are the same photons that are being distributed through the fiber optics to the other side to compute as well. 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, allows you to, whatever you're doing here, to funnel it through. It's not really scalable. Others are actually going to connect using photons. The challenge there is that you've got to convert from electrons to photons because you're distributing it, 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 note is most other approaches are going to have to have their own loss metrics as well, which is interesting. The other thing is probabilistic. 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, and just keep pressing until it happens, because it's not a deterministic event. 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. 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. You shouldn't be thinking about this as a data center where you have really good computation on a CPU, but the moment you put two together, there's some bandwidth limitation. That's definitely true for 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 all to all connectivity goes across the modules. It really is a truly modular architecture, not a kind of independent systems networked together. Thanks. Harsh Kumar, BMO. A couple of smaller, like, housekeeping type questions. You mentioned system sales as one of the avenues for commercialization. Can you elaborate? Would you stick to research organization universities, or do you plan on selling your computers to corporations? Yeah, I think very naturally, given how these systems will roll out, 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. 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. Got it. Most of the other companies we talk 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? A couple of hundred. We have a number in that 100- 500 logical qubits. We have now a good handful of applications that our commercial partners have identified as multi-billion dollar problems for them. That is really what we are focused on. There is the hope that there will be a breakthrough that will move that to a smaller number. But yeah, hundreds of logical qubits, billions of gates is where we believe that is. Sorry, my last one. We have been sold this vision that quantum computers will sit right next to traditional computing, and then work together harmoniously. Is that your vision of how quantum data centers will be, or do you envision standalone data centers with technology such as yours and others? They will be standalone, but it is because the coupling will be a lot tighter than you expect it to be. One of the really powerful things of PennyLane is that heterogeneous compilation. Right now, if you go to a data center like Leibniz Supercomputing Centre in Germany, they have bought a quantum computer, they have it, they have a network cable running from the quantum computer to their data center. That is useless. That connectivity is going to be incredibly close, incredibly low latency. The user is not even going to feel it. They are going to take their program, they are going to compile it, pieces are going to go to the FPGA, the GPU, the CPU, the QPU, and they will come back. But ultimately, that whole thing is what we are going to call our quantum data center. Yeah. Hey, guys. Kevin Garrigan from Jefferies. You gave some metrics around PennyLane. Software is obviously going to become very important in the long run. But you also have everyone saying that they are a full stack company. Just what are the biggest KPIs that you want us to track to really let us know that PennyLane is continuing to lead in the software race? Yeah, I think user adoption, and it is a little bit difficult to track that, but we have the number of downloads right now publicly available source through PyPI as the package is open source. The Unitary Fund has a survey every year. It is a little bit limited in scope in terms of who it attracts, but it is still a very good signal. University engagement, so I believe we are now over 150 universities. That number is higher, but I think that is the one that we have 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 PennyLane in order to drive applications right now. I forgot to say, John McPeake from Rosenblatt Securities before. Non-Clifford gates, can you talk a little bit about universal gates and how you plan to deliver those over the timeline, and whether they are still at that same 100 to 1 ratio? Same ratio. Yeah, that is just across the board. The non-Clifford gates in our case are non-Gaussian gates. 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 our case Gaussian gates along with T gates and magic states were another way to create the non-Gaussian or non-Clifford 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. It is kind of getting a bit old now, but the thing that really brings it alive is loss reduction. All the parts for Clifford and non-Clifford gates, they are all there, they are all working, but they are 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. Thank you. 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. 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. 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? Is 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 PennyLane. To what extent are those two really independent roadmaps versus there being interdependencies between the two? If there are interdependencies, what are they and what should we be looking at as things you need to execute there? I think they're definitely interconnected. Because PennyLane, we often just think about it as very roughly speaking, the operating system for the quantum computer, the 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. PennyLane itself is a full stack where you've got the kind of higher level stuff that maybe people are more associated with PennyLane. 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. 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 build, 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. I don't know if you've got anything else. Yeah. No, I think that's really valid. 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. 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. 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." 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." Then everybody's model fully breaks apart. 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. That's really powerful. To your first question, it's been a mix of both. 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 type of people we want to work with. We build those relationships. Others, like Lockheed Martin, had been using PennyLane for six years prior to a commercial engagement with us. Some of them we know about while they're using it because they come out, we see them on our forums, we see them through our other platforms as using the software. Other ones are complete surprises. Sometimes somebody shows up at our door and says, "You guys broke this feature in PennyLane. It killed our entire stack." We're like, "We didn't know you had a stack." Right? Let's talk about how we formed a partnership now. It's always a great surprise, but we've been definitely surprised with not only how many developers are out there using PennyLane, but how many enterprises out there have actually built internal product on top of PennyLane and now are coming out to form partnerships with us. Cool. Thanks. Adrienne Colby, Citigroup. Thanks for taking the question. You touched on the scarcity of quantum talent in your remarks, and I am interested in how we should think about the headcount that is necessary as you scale as a company, and if that is a bottleneck. Yeah. So, luckily, I think we have hit a bit of a transition point with that Aurora paper. It was actually a pretty big milestone where I am 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 us say, quantum optics people in the past. On the algorithmic development, it is still definitely a thing where there is a challenge within the industry as a whole. I think we are in a unique position both where we are located geographically and that we are 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 is not grown in our backyard, between the Institut quantique in Quebec and University of Waterloo and IQC. It is always something we keep in mind. We have been incredibly lucky in being able to attract the top talent so far, and really have not too much worry that will not continue into the future. All right. Any other questions? Nehal. Yeah. Thanks. The 10 to the negative 16th error rate is indeed very impressive. I think telecom standards are known to be very rigorous, and 10 to the - 12 is sort of the bit error rate that is required, I believe. What applications are you expecting that will benefit from getting all the way down to 10 to the- 6 error rates relative to the next closest competitor talking about 10 to the - 12? Well, maybe I will let Raf Janik answer the application side of things, but my biggest excitement about quantum computing is the applications we do not know about yet. There are still so many out there that we do know about. We are focused on material design, quantum chemistry, and things of that nature. Pharmaceuticals will be one. But the idea for the stuff we do not know, lower error rate is always going to be better. Presumably, the lower you go, the more applications can open up that we do not know about either. So that is important. I would say more about the other companies is it is just potentially, it is harder for them to get to those error rates, and it is much easier in principle for us to get to them, as I mentioned before. I just do not know how low they can actually go. I have seen also others, as I mentioned, 10 to the - 12, I think, is the lowest I saw with roadmaps for a given time period. Other ones are 10 to the - 10, for instance. So we are very aggressive with our 10 to the - 16. It is because we can actually get there. We see the pathway to getting there. The other take, too, that I know not many people say is no one really knows, honestly, no one really knows what the error rate you need is. It will depend on the algorithm. As I said, with algorithms, you have no idea, but you need to get lower. But there is even some skepticism whether 10 to the - 10 is actually low enough or 10 to the - 12. So why not go as low as you can? For the reasons I mentioned and the fact that our photonic architecture and hardware actually allows you to get there. Yeah. I think probably the most famous one is breaking RSA, right? Where you start needing to push that. Personally, it is my retirement plan to take a third of all the Bitcoin. That is why 10 to the - 16 is important. More seriously, 10 to the - 12 is right now just on the beginning of those applications. You mentioned that 10 to the - 12 is a standard in telco, but if you had your CPU operating with an error rate of 10 to the - 12, you would throw it in the trash bin, right? You would not be able to do much of the stuff that you do. So in compute, we expect error rates much, much lower. I do not know what exactly the error rate of a CPU right now is, but I can tell you it is much, much lower than 10 to the - 16. So on the compute side, like Christian said, the applications begin in that range of 10 to the - 12 to 10 to the - 16 of what we understand right now, but you definitely want to push higher because it will open up bigger and bigger capabilities. Great. That is 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 up to a CAD 895 million project cost. Is that CAD 895 million project cost effectively the same as the CAD 1 billion that you guys talked about earlier in the year to build your fault tolerant quantum computer? No. No. Different. Yes. Okay. All right. Then obviously that billion is much more encompassing and that project there is just a part of it. What part of that Canadian-funded project does that represent of the overall billion then? It is a big part of getting through the next few years. It is our new packaging facility, it is the research that we are doing to get to the data center. As Christian laid out, the slide that either of us could have presented, there are numerous various sources for us to get to that data center. This is just one of many sources that is going to help us get to that point. Is that CAD 895 million inclusive of the R&D personnel cost? I actually do not know the original source of 895. Yeah, the project government submitted a CAD 893 million proposal. Sorry. Yes. Yeah. Yeah. But it is inclusive of the R&D work that we are doing. Yes. It is? Yes. Okay, great. Thank you. Going once, going twice. Oh. Oh. Back row. We were so close. Hey, guys. Thanks for doing this. Julian Frost with Wedbush Securities. A lot of investors think that simulation, like chemical materials, are probably among the near-term applications that will be most accessible as we get into the hundreds of logical qubits. Does Xanadu share this view? Do your early partners have certain application areas they are 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? Yep. It's one of the earliest areas that we 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, photoresists for semiconductor industry. Those are 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. 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. We have a number of partners, I think FCAT is a public one that we work with on that area as well. Got it. Thank you. Super helpful. Going twice. I'd just like to take this moment to thank you all for being here. We'll be around to chat with you after. Thank you.