IonQ, Inc. (IONQ) Earnings Call Transcript & Summary

Welcome, everybody, both to those of you who are here in-person and to all of you who are joining us live for IonQ's Analyst Day. Ever since we went public, I've been looking forward to this. We were cheated by COVID, so we didn't get to have an actual full-scale Analyst Day in-person, and we do have that now. And this is where we get to talk to you about what we do here at IonQ. It is all things that you have heard before, but with more color and where we get to tell you the details of what makes IonQ, IonQ. So please, buckle in and enjoy the show.

Hi, everyone. We've prepared some cautionary notes. I am not going to do the full earnings call routine and read them to you. So instead, we advise you to please take a look at our website and review the cautionary notes, especially in advance of making an investment decision. We have a packed agenda for today. You're going to be meeting many members of the IonQ team, across many different functional areas. To be fair to everyone online and in-person, we're going to try to stick to the agenda promptly, and so we will do our best to make sure that every session, we hit on time. One note is that the lab tour will be on-site only. With that, I will turn it over to Peter Chapman, IonQ's President and CEO.

Thanks, Jordan, and welcome to everyone. My name is Peter Chapman, I'm the President and CEO of IonQ. I just wanted to say how excited I am about today because normally, you hear from me on earnings calls and you ask me questions. But today, for the most part, I get to sit down and you guys have a chance to meet the rest of the team here. And they really are the people who are making this happen. I just get to be the spokesperson most of the time. So you get a chance to ask actually the experts, who are actually working on building these quantum computers today. So without further ado, let's get started. So the next revolution, we believe, is quantum. And probably you do as well, if you're here and listening, especially on this podcast. The shorter answer is, we think that quantum is going to be the next big thing. With a little luck, this quantum thing has the potential to be as big as the Internet or maybe even the PC itself. Quantum computers will not be good at everything. For certain applications, they have the potential to rival the world's largest supercomputers. But quantum computers are definitely strange beasts. A quantum computer struggles to add 1 plus 1, but at the same time, maybe it's going to be very good at solving certain differential equations. So with that, we're going to talk about quantum for the rest of the day. And I thought what I'd do next is actually just to find what we think it means to be successful in quantum. And you likely have heard expressions like Quantum Advantage and Quantum Supremacy. I wanted to read you Wikipedia's definition in quantum computing, quantum supremacy or quantum advantage is the goal of demonstrating that a programmable quantum computer can solve a problem that no classical computer can solve in any feasible amount of time, irrespective of the usefulness of the problem. And I find the last part quite entertaining. So I can tell you today, we do not care about Quantum Advantage or Quantum Supremacy. These are really academic terms. Arthur C. Clarke, who is a friend of my father's, had been -- had said, if an elderly but distinguished scientist says that something is possible, is almost certainly right. But if he says that it is impossible, he is also very probably wrong. And I think that really is the crux of part of the problem with these definitions of quantum supremacy and quantum advantage. It's very difficult to prove something can't be done. So what do we care? For us, the test for me is, can I solve a customer's problem with a better mousetrap at a better price? If I can, I'll make the sale and IonQ will be successful. We don't actually care about the academic discussions about quantum supremacy or quantum advantage. So our goal here is to build quantum computers that solve problems for customers. And while quantum computers might not be good at everything, interestingly, many business problems can be translated into a problem that a quantum computer can run. Quantum computers seem like they will be very good at optimization problems. And luckily, many business problems can be converted into an optimization problem. This then means it can run on a quantum computer. So this is a slide that we showed you early on, I think, pre-IPO. That was the technical roadmap for IonQ for a better part of the decade. This year, we hit our technical goal 7 months early, #AQ 29. When we created this slide, we didn't have a lot of track record at that particular point, but now we've shown we can deliver and more importantly, we can deliver early. The tech team is now working on #AQ 35 and #AQ 64. #AQ 35 on Forte and #AQ 64 on a new barium system. So I show you this slide because in 2025, we expect to hit #AQ 64. That will be here in a blink of the eye. So for us, all this work has come together to build this remarkable machine, and we expect it to be a better mousetrap and solve problems for our customers in the very near term. So I want to talk a little bit about #AQ 64 and why we're so excited about it. You've heard from us and others, that every time you add a usable qubit, that you double the computational power of the machine. So at #AQ 64, that's equal 2 to the 64. You can type that into your browser right now and see what that means, but it's equal to 18 quintillion. I had to look up what that number actually meant, as I never heard that before. So it means that in 2025, we will have a machine that is capable of exploring our computational space of 18 quintillion different states in a single instruction. So now we have to unpack what quintillion is. It's such a large number and almost none of us have heard of it before. So I'll try to put this in perspective. If #AQ 5 was the tip of a marker, then that's what #AQ 5. #AQ 29 will be roughly -- before I was to use #AQ 5 in that tip, would be about the size of a basketball court. And at #AQ 64, it would be almost -- will be larger than the land mass of the United States. So this is really the next machines which are coming from IonQ. Another way to look at it, just trying to look for places where you can see quintillion is that at Oak Ridge National Laboratory Frontier, the world's largest supercomputer, can calculate roughly 1.2 quintillion different floating point operations per second. So we're talking about doing 18 quintillion different computational states in a fraction of a second. Just for fun, the other place -- the only other place I've heard this word was actually with Tom Cruise talking about in the latest Mission Impossible, that it can consider a quintillion different possibilities. And so maybe quintillion in the human lexicon will become more and more popular going forward, although I wouldn't spend a lot of time there, because if you just add a couple more qubits, then you're going to need to learn the next thing after quintillion. And so in fact, actually, we will very quickly get to a point where mankind has not come up with words yet for the kinds of numbers that we're talking about. Because at #AQ 256, that's 2 to the 256. At #AQ 120, the machine can consider the same number of possible states, as there is Adams in the known universe, all 13.8 billion light years across. And in #AQ 256, it's a number which is just unimaginable, and if you looked at our roadmap by the end of the decade, we're talking about having 1,024 usable qubits. So it's 2 to the 1,024. And that's just a number, can't be calculated. Actually, that number -- it can't be calculated on a PC today, just that 1 number. But my guess is if you could calculate that number, it would probably take something like 32 gig of RAM, just to store the 1 number as to how big it actually is. So these are just unimaginably large numbers kind of going forward. So at #AQ 64, this really is a huge milestone, not only for IonQ, but also for the quantum computer industry. When we started -- actually, when I started, what experts told me was that it roughly 65 to 70 good enough qubits -- is that's the kind of goldilocks where suddenly quantum computing is really useful. At about 35 algorithmic qubits, you start to leave full simulation behind. And so now you're going to want to start to run it on a quantum computer, rather than on a classical machine and doing simulation. And all of this is now not too far away. And we just announced our first sale of one of these systems, actually 2 systems to QuantumBasel in Switzerland. So next, I want to talk about, how is it that IonQ pulled ahead of the rest of the competition. So the very first thing is that, we chose a qubit that mother nature provided for us. There is kind of in the quantum world, there's 2 different kinds of qubit modalities, one which is man-made and one which is using atomic particle that mother nature. And so that's what we did. We're ions and it's naturally quantum. It meant that we didn't have to tackle the problem of creating a man-made qubit. And second, we're piggybacking on mature technology on several areas, such as atomic clocks. Our Ion traps and an atomic clock have a lot in common, and that is very mature technology that is being used in -- with GPS systems and fairly common. Actually, an atomic clock is a chip nowadays, that's only about 1.5 inch across and fairly easy, I believe, to get. So this, our ability to choose a technology. We've said in the past where we didn't need to have a breakthrough in physics. We didn't need a breakthrough in manufacturing, yields, material science, any of those things that gave us a huge advantage, which allows us to be the first to this next phase of the market. The next big thing, which is people are going to start to realize is the connectivity, and you already know, fidelity matters more than physical qubit count. And so what you're seeing here is a picture of the connectivity of an #AQ 29 system, is that every qubit can talk to every other qubit directly. So you don't -- and you can see on the other side, kind of a typical nearest neighbor system from some of the competitors. If you wanted to have this qubit talk to this qubit, you would have to use the intervening qubits in between to be wires, and you use an operation called a swap gate, and it happens to be one of the noisiest operations in quantum. And so you're basically using your qubits not for computation, but for connectivity. And this is not a huge issue when you have a small number of qubits, but as you get to a much larger set of qubits, suddenly this connectivity will really matter to you in a big way. And so we expect the world to start to realize that, especially developers kind of going forward, the advantage that we have. Of course, the second part is the fidelity itself. It is Ion traps. I don't think it's controversial at this point is that Ion traps have the best native 2 qubit, average 2 qubit gate fidelities in the market. And so that fidelity then controls the size of the circuit that you can run. And we'll talk about that as to what kinds of things you can do with #AQ 64, coming up in part of Dean's speech. So for me, this is -- this next section here is really interesting, maybe the big announcement of the day in some sense is, we showed you this slide previously and it was based on a BCG data, and it talked about the 3 different phases of quantum. The very first phase here was the NISQ-era. This noisy qubits that we were doing computation with. The second phase would be to do -- have higher fidelities, and to be able to get to using error correction. And so that would enable much larger programs, things like #AQ 64 to be able to happen. And we reported that we thought we had shown that we have done error correction with an overhead of 1 in 16, meaning 16 cubits to yet another 9 out of the average 2 qubit gate fidelities. But what's happened in the last 1.5 years is that we think that we have a good shot of getting to #AQ 64 without error correction. And that's really large for us. We're working on both an error corrected approach and a non-error corrected approach. We're working on both. And how do we think now that we can do that, is in the last 2 years, we found something else called error mitigation. And error mitigation was basically a statistical approach to go through and find some of the systematic errors that are in the system and remove them before we deliver it to the customer. And so -- and this was particularly important for our systems, because much of the noise that you see in our systems is -- comes from the control software itself. And so we can mitigate that through software. And so we don't know yet, but we're -- our intuition is that we can get here now without full error correction. And Dean is going to talk about that coming up shortly. So that allows us to enter this new phase. We're now talking about #AQ 64 in Phase 2. And instead of calling an error corrected -- correction phase, instead, we're going to say that it's an enterprise-grade error for quantum, because at #AQ 64 is we believe that now we can start to build that better mousetrap and deliver value to customers. So -- and I think I have not heard personally any other company talking about getting to a point within the next 2 years, where they will have easily manufacturable systems that have a sufficient power to be able to deliver real economic value to customers. Today, what you see in almost every case is proof-of-concept projects with enterprises, they tend to be small and low value. And we now are actually moving away from those things, and now working on full-blown applications that will take advantage of #AQ 64. I mean we're on now only 2 years away from that and this is just like any other software development, takes time. If we were going to go out and create a social media application, we decided altogether, we were going to create a company to do that. It would not be an unreasonable expectation that, that would take 1 year or 2 to develop. And so we're just at that right phase. Now where we're starting to create software with customers that will take advantage of those systems coming up. So this is an exciting time for the company, kind of going forward, especially in the next 2 years, you'll be hearing a lot more about this. And you'll hear about some of these applications that we're building today, the number of qubits required to be able to run them. So the next kind of thing that gives us an advantage, is when we started the company, when I was first, I think there was 35, 40 employees and Chris and Jungsang, myself and a lot of students from Chris and Jungsang's labs, we're kind of the early employees of IonQ. Over the last 4.5 years since I've been here, we have been moving the company from an academic organization. We're proud of our academic origins, but we're moving to an engineering-driven organization. And you're going to hear from the leaders that we've hired in the last several years today. And you can kind of see some of their backgrounds and they'll tell you about that as well is, for the most part, these are people out of industry. We still have one more transformation to go, which is to go from an engineering organization to a product one. And so that's kind of the next phase, I would think, over the next several years for IonQ. One other advantage is, we're early on, we're available on all 3 clouds, and we're unique in that way. Having a customer, meant that we had to build a product that met an SLA. Strangely, the cloud guys want this thing to run 24/7. Go figure. And so we had to be responsive to customer demands. And our customers have pushed us to build better products. They've made us a better company, but we aren't done yet. Without customers, though, we wouldn't be where we are today. And so that early first customer contact in our life cycle, actually pushed us to build better products. When we first did the very first deal with Amazon, the computers would only run for a couple of minutes, and then they would have to go into a calibration. We famously joked at that particular time, where you needed a bunch of physicists to turn a bunch of screws with some screw drivers to get it to work. And I said to them, everywhere, somebody out back turns a screw to calibrate the machine. I want to have a step remoter there, and I want the operating system to control it, and I want it to be the one that's calibrating the machine. And so today, what happens is the machine is continuously calibrating. It's basically between jobs, it's going through and tweaking the machine, where 4 years ago, that was done by humans. And so we have made great strides in producing a product. I like to joke, no humans were harmed while we ran your particular job on our quantum computers. And that is increasingly so. We're not done yet. The next phase is actually, to put these into customers' data centers. And of course, we don't want to have full-time people -- actually they're working to be able to keep them up and running. So we're now focused very much so on how to be able to do that, and also how to do the next-generation systems, how to do field support. Today, a power supply might be in the back of the quantum computer if it goes down, I might have to tear the damn thing apart to be able to replace the power supply. So I need now in the future to make sure the things which are likely to fail, that I can easily go in and a field support person with limited knowledge could pull that part and get a new part in. Okay. So another advantage to the company is that we have ever increasing revenue. And of course, we get to apply that financial stream back to the product itself. And so it gives us an advantage, compared to others who don't have these kinds of sales. And then, of course, everyone has talked about this, but we have quite a bit of cash. We have a large war chest. And this simply allows us to invest in ways that others can't. It allows us to hire the best, invest in building cheaper systems, more manufacturable systems and that, as I mentioned, systems that can be supported at a customer site. And that's exactly what we're doing. People have asked us, with all that cash, are we going to be doing lots of M&A? We have done one transaction, and we're always looking for a good deal. But we are really focusing our resources on how to build cheaper and smaller quantum computers with our vendors. But to achieve this, just to be clear, it doesn't mean that we have to buy our vendors to be able to get to cheaper ones. So we're spending our dollars, largely with things like paying for MRE with vendors to be able to get to a smaller, cheaper product. So the next thing which you also already know about is this is a picture of the Basel facility. We signed a lease earlier in the year. It took a while to get building permits, you probably heard that before, especially if you own your own house. And we went through with architects and large construction firms, little things like reinforcing the roof with structural steel so it could handle the weight load of new air handling systems, so that we can build a very clean environment to be able to assemble these quantum computers. So I'm proud to say that we are on budget and on time. And in this fall is we will start to take occupancy of the building and start manufacturing the next generation of quantum computers. And then the other piece that allows us to do things that others can't, because largely our cash position is our expansion internationally. And so you can see here, we did acquisition in Entangled Networks in Toronto. Of course, you're here in College Park. We just talked about Basel. Basel, by the way, is about 65,000 square feet. Just to give you a sense of scale, that's about twice the size of this entire facility. So just to kind of give you why you're traveling around today. In Basel, we will open up a small facility to be able to house our -- the quantum computers and also sales force and application development for the European market. We opened subsidiaries in Germany and also in Israel and then started with hiring there as well. So all of these things are possible because of our financials. So I just wanted to talk a little bit about the life cycle of the hardware products here, is that at the very beginning, it's all about research and development. These are kind of the early lab experiments, then you start getting customers who start pushing you to build better products, then to get to manufacturing and get to a point where you can actually easily build these things, strangely little things like -- and you build the same quantum computer twice. And do you have engineering change orders and all those kinds of things that real companies do. And then lastly is, can you support the product in the field. And so all companies have to run through the cycle, and IonQ is kind of the, I believe, the first that I know of, that's actually getting -- doing exactly these things. So this is kind of that same slide that we talked with at the very beginning about the 3 different phases. And so we think that we're about to enter this enterprise grade area with #AQ 64 within roughly the next 2 years. And what does that mean? Well, it means that we're going to have first mover advantage, and you've probably seen this slide elsewhere in other markets, that say that the first movers capture 90% of the value in any new technology. I believe there's been studies that showed this. And so we think in quantum for both our customers and for IonQ itself, that we will have first-mover advantage. And then my last slide is just to kind of talk about where we think the company will be in the future. And if we're successful, I think IonQ has the chance to be one of the leading companies of our time, that if we do what we think we can do that this -- if we were to look 20 years down the road, that IonQ might be in the same class as many of these large companies that are the most successful companies today. And that's why we show up excited every day, and I think why our investors are investing in the company and the belief that maybe we have a good shot at getting there as well. So with that, I'm going to open up for questions. And then the other, just to let you know, the rest of the day, I've kind of previewed what we're going to talk about today. But the rest of the day, there's going to be the experts who are going to dive in on things like how do we get to #AQ 64 and all the rest. So I'm happy to answer any questions, but you really are going to get a chance to meet the experts today. And then for some questions, it might be better to ask them than me. So with that, I'll open it up for questions.

Sure, Peter, you touched on #AQ 64 and whether it will or will not have error correction, which way do you think it goes?

I think it goes without error correction. It would be my guess. But in everything that we -- I guess maybe not everything, but many things we do out back, we are investing in multiple paths, so that we have multiple shots on goal. So we're working on error correction, just as much as we are without it, but I think the current belief is we can get there without it.

And would there be a difference in usable time if it's error-corrected versus non-error corrected?

It's -- yes, I mean, the error correction itself takes time to run, so it takes away from your computational time, which it would be better, but it's just easier. It would turn out to be a lot easier, because I wouldn't need hundreds of qubits to put onto a chip. It means I would need only a little more than the 64 to be able to get there. Any other questions? Then in that case, I'm going to turn it over to our next set of speakers. Thank you.

Okay. We will now turn it over to Dean Kassmann, IonQ's VP Engineering; and Pat Tang, our VP of R&D.

Hello, everyone. It's a pleasure to be here today. I get a chance to talk about something I love doing. So I'm Dean Kassmann, I run our engineering here at IonQ. I joined IonQ in 2021. I oversee all hardware and software development at the company. So this kind of ranges everything from cloud down to firmware, as well as kind of like ions out to our enclosures. Before IonQ, I worked at Blue Origin where I ran all research and development for the company, started there many years ago when the company was very small, as part of the original team that did the very first soft powered landing from space of the New Shepard Rocket, led many teams while I was at Blue Origin, eventually took over the Applied Sciences organization and kind of started the initial design of the New Glenn Orbital Rocket, eventually then ran the -- and kicked off the R&D department and started that from scratch. And so in 2021, I joined here and started a little bit on the R&D side, but then quickly moved over into engineering. So today, I get the lucky pleasure of talking about kind of our path to #AQ 64, and some of the ingredients that are required to get there. Before I do so, I want to kind of -- first kind of do a little bit of back up and talk about AQ itself in the metric. So the AQ metric is an application-based metric, right? We've defined it specifically to be a representation of the kind of circuits that comprise workloads or kind of the building blocks that are part of what we would normally consider in kind of the user application problem spaces. We have 6 metrics that kind of comprise the overall benchmark, and so when you read the overall benchmark chart, I'll be talking later about depth and width. And so that represents depth being the number of kind of 2 qubit entangling gates that we can achieve. For #AQ 64, that's around a little north of 4,000. And so for width, that is the number of qubits that we end up using in the benchmark, different benchmarks, different components in that benchmark push different pieces. Some of them require very high-quality qubits, but a fewer number -- sorry, you have fewer number, but very high-quality qubits. Others require a much larger number. And so the benchmark came about, as a result of kind of a collaboration and through QEDC, so as many companies contributing. In addition to #AQ, we do run component and subsystem level benchmarks as well, it's not our only measure of system performance. But I wanted to give a little bit of background here, because it becomes important as we start to push to #AQ 64 and trying to kind of define the boundary of what we're trying to shoot for. So Peter talked about this a little bit before, in terms of what happens as you increase the computational power with every additional #AQ. And so the computational space for an #AQ 1 system is 2, right? It's with a paper clip double-sided coin. As you start to move up that doubling of the computational space, makes a huge difference in terms of the kind of problems that you can solve. I don't want to belabor this, but Peter kind of covered really well, as he kind of talked about just the size of the computational space for #AQ 64, which means that, that entire space is accessible to the algorithm or the circuit. Now right now, Peter also kind of talked through the overall technology roadmap that we have. This is the same slide that Peter showed. I want to speak to this from kind of an engineering viewpoint a little bit. So we've been making continued strides over the years, in terms of the things that go into our systems, the things that are driving the #AQ 25 and #AQ 29 kind of deliveries that are ahead of schedule. I'll talk a little bit about the #AQ 29 and what went into that. And then I'm going to spend a lot of time on kind of the 35 and 64 ingredients that go forward from there. Just as a reminder, kind of right now, our #AQ 35 development work is on ytterbium, where #AQ 64 will be pivoting over to barium. I'll talk a little bit about that as well. So for #AQ 29, there is quite a few different components that went into that. We moved from our #AQ 25 architecture to #AQ 29 to use acousto-optic deflectors or AODs. That gave us better beam steering capability, and it allowed just overall better beam quality at the delivery of the ions that resulted in better fidelity. We introduced air mitigation techniques more formally and kind of invested more heavily. I have a little bit more detail on that later. We've been continually improving kind of our hardware, in particular, our hardware control electronics. And so being able to get very high performance, control electronics to drive the wave forms that we use in executing gates, has been extremely important in trying to increase fidelity. And we've been continually improving our compiler. Now for #AQ 64, there's a lot of new things that we have to add to the plate. And so as I mentioned before, we're going to be moving to overall barium. There are some benefits that I'll be talking about. We're going to continue to leverage air mitigation techniques. We're going to be talking about Multi-core and RMQA. There's new trap technology that we're introducing for #AQ 64. I'll cover some of the larger kind of software improvements that we have across the stack, and then there are some of the other items that kind of fall along with that. The continued investment in the compiler pieces, there's going to be multizone operation, which adds additional parallelism. I'm not going to touch on #AQ 64. That's where I'll hand things off to Pat, and he'll kind of describe a little bit of things beyond #AQ 64. And so, but there's a tremendous number of kind of exciting investments that we're kind of adding to the pod as we move forward. And so I want to first talk a little bit about barium, we've kind of briefed this before. But barium has a fundamentally lower spontaneous emission limit. That results in a higher fidelity ceiling that we expect to achieve in barium. It also has a visible spectrum as opposed to ultraviolet, that gives us access to basically just a wider set of optical engineering. We can piggyback on telecom with some down conversion and other techniques to be able to drive the kind of a wider technology selection. And so that gives me more engineering freedom in how we think about the designing of our kind of our optical subsystems. Now one of the things that we are looking at the barium gives is, it has a longer lived kind of internal atomic structure, there's longer-lived states in that structure, and that allows us to basically lower the overall SPAM errors. It also allows us additional options in the atomic protocols that we employ on barium, to be able to kind of do things that are a little bit more exotic that we hope to be able to talk about in the future. There's other things, in terms of kind of single isotope approaches that allow us to both do cooling as well as computation with a single atomic species. And so those are all kind of some of the, I guess, leverage points that we're looking for barium. We have a lot of investment in barium to date. Some of the original barium work that we've had, focused on very long chains, kind of high-performance long-chain operation. A lot of the investment that we have since kind of we talked about barium in the past, is now looking at scaling barium to kind of what we need to do to go to multi-core and then multi-QPU. So we've increased our overall investment in barium over the last several years. So Peter talked a little bit about error mitigation. So error mitigation is a technique that we use to kind of remove some of the systematic and some of the stochastic errors and engineering design. Our overall approach is to be able to first design the errors out. Second, for those areas that you can't design out, you need to mitigate. At some point, you can no longer mitigate the errors, and you need to use error correction for those techniques. So right now, the main source of errors in our system for kind of our stochastic and kind of the systematic errors, are surrounding around our 2 qubit errors, they're the bottleneck. Our 1 qubit errors and our SPAM errors are well under control. And so while we've been investing in error mitigation techniques, it's allowed us to hit #AQ 29, and we'll be continuing to be kind of a key ingredient as we push to #AQ 64. The debiasing and the sharpening techniques that we introduced, kind of allow us to resolve the signal out of the noise for the overall quantum computation to a much greater degree. And there are other techniques that we'll be adding kind of to the mix as we move to #AQ 64. And so the overall belief right now, as Peter mentioned, is that to be able to hit #AQ 64, we do not need to employ error correction techniques, but we'll be able to double down on some of the error mitigation. It's not to say that we're not investing in error correction, that's continuing. But for the purposes of the current #AQ 64 drive, we believe we have everything kind of in play. So one of the things that we need to do though, is move to multi-core. And so RMQA is IonQ's kind of overall approach to a multi-core system. So this allows us -- a single chain is kind of limited by our ability to kind of deploy high fidelity entangling gates across it. At some point, through whatever error sources you get limits on the ability to execute circuits. It's not only due to fidelity, there are other error sources that prevent overall application and circuit performance, and so you need to be able to deploy those gates. And so at some point, you hit a natural limit to what a single chain can support. You then need to move to multiple chains. And so what we have is a RMQA architecture, a multi-core architecture, where you have multiple chains or subchains or cores and a single eye on trap, in which we do shuttling and merge operations to be able to combine and separate those cores. This also means implicitly that we have multiple operational zones to be able to execute gates in parallel across those different cores. And so this will become operational as part of our #AQ 64 effort. To date, we haven't had to use it. We've been able to get the performance that we need in a single core in a single chain. As we move to #AQ 64, we'll need more ions and we need to go to multiple cores. And so it's a huge push. Now to be able to drive a multi-core architecture, our current trap supported, but we're looking at additional investments, and that's kind of where our trapped development and investment comes in. And so we've talked very briefly on past earnings calls about the MGT or the multilayered glass trap. We have kind of an evaporated metal on glass trap right now or EGT, which has been the workhorse. It's right now deployed in all of our development systems and research systems, and we'll be continuing to kind of use that for many years to come. However, we've been investing over the last several years in the new technology, MGT that kind of is a multilayer design. It allows denser routing for more electrodes, more electrodes mean more ions, more ions means more performance. And so the overall development is a kind of multilayer routing. We have more robust manufacturing techniques in MGT. So we have higher yields that come out of that. We also have more quantum zones, like I mentioned, all of those drive the multi-core capabilities. There's other items that are part of the MGT that kind of are co-designed with our overall optical system design and architecture. And so right now, the MGT is in its third generation of design characterization and fab. And so we have additional generations to move to as we move to our #AQ 64 system, but this will become our workforce moving forward. I haven't really talked about and IonQ hasn't really tremendously kind of dove into the software side of things either, but I want to talk a little bit about some of the software changes that we're making, and kind of improvements across the stack to drive to #AQ 64. So IonQ is a full stack software company. My team runs software from the cloud all the way down to firmware. So we cover all of the integrations with our cloud providers, but we're also doing the low-level real-time control for gate pulses and everything else. And so if I look at our overall software stack and think about kind of #AQ 64 and what's needed to get there. If we start at the bottom with kind of our real-time control. And so for real-time control, we'll be introducing micro calibrations. And Peter talked a little about kind of the automation and calibration aspects, but being able to do micro calibrations in real time, to be able to keep the system kind of at peak performance throughout its overall kind of uptime, will be an important improvement. We're also looking at look ahead and pipelining techniques to be able to get more throughput, and as well as just better streaming of kind of real-time instructions down onto the hardware and to our gates. If we move up at the OS level, we've had an ongoing effort as an overall kind of a rework of our OS, and so that's being kind of a new generation of operating system will be deployed as part of our #AQ 64 work. At the compiler side, we're going to be continuing to invest in kind of an optimizing compiler. It's an art to be able to take a very large circuit, and then compile that down across cores as well as across down to the hardware to be able to figure out and map to the individual hardware. IonQ's overall position is that you cannot use it just a generic compiler, being able to take advantage of the hardware and understanding of the hardware topology, enables us right now to have the best compiler on our hardware that we've ever seen. And so we're going to continue to invest in that for the #AQ 64 pieces. Our SDK and API pieces, there's going to be additional improvements that we have for end user tooling, to be able to support kind of hybrid workloads as well as just submitting and retrieving jobs, right? Just kind of end user experience is important to us. It's also going to be improved, to support kind of an overall hybrid fabric, where you're looking at GPU, QPU and just normal CPU kind of operation. On the other side, we have, like I said, ongoing investments in our automation and calibration work. And so we need to keep the systems at optimal calibration and automation. And so the idea of being able to take our commissioning times and drop them down, being able to drive our overall, I would say, kind of automation and kind of operator workloads down, that's just part of bringing a product to market. And so we have to drive those improvements on the software side so that software just works. It's almost an appliance, right? It will take some time to be able to get there, but it's a big investment that we're making across the entire stock. So with that, I'm going to turn it over to Pat. He's going to focus on now some of the things that we also have in work for things beyond #AQ 64. Great.

Thank you very much. Thank you. Welcome to IonQ. My name is Patrick Tang, and I was once upon a time quantum physicist in the semiconductor world, and took a detour in consumer electronics in Apple and Amazon and so my last stint was an engineering VP working on Kindles, Echo, FireTV, very different world to what we have here. But it's immense privilege for me to be here to come back full circle into my quantum mechanical routes to be working at IonQ. So thanks for your attention. I want to dive a little bit into the physics of what's behind this power of quantum computing and what's leading us to #AQ 64. And so what we have here is a diagram of what's comprised inside our quantum computer, a qubit and -- which holds the information and our laser system, which manipulates this information. So we're all familiar in the classical world, where information is held in binary in 1s and 0s. But here, what we have here on the qubit sphere, you can see on the diagram on the left, is that other than just one zero on the north and south poles, we have the quantum states, which act on the surface. So each state on the surface of this qubit is an extra quantum computing state that we can manipulate. So the -- what does this mean? It means that when we're computing, each state on this sphere holds a possibility or probability of being 1 and 0 simultaneously. So this is nature's first gift or super position, which is behind quantum computing. And then we control this immense capacity to hold information with lasers, which brings us to this diagram on our right, which is a representation of our 2 qubit states at the north and south pole 1, 0 in the Ion state, and then we can manipulate transitions of this state using 2 lasers offset in energy by precisely this qubit energy itself. So by manipulating the intensity, and the phase and the exposure time with these lasers, we can transition to any state on this qubit sphere. So in terms of hardware, this is how quantum computing is done at IonQ. So it turns out that that's very nice theoretically. But in reality, our lasers are quite noisy. And you can see here that our orange and blue lasers form a very noisy come both in intensity and frequency. And we have to find pairs, individual pairs of the orange and blue spikes, right? In order to match the qubit energy. It's a very messy affair, and the match is never perfect. So we end up with extra intensity, extra frequency, which acts as noise, which disserves our quantum system. Much in the same way that heat kind of disturbed a CPU and degrades performance. The technical feat that we've achieved recently is produce a laser with 2 pure tones. And this technical feat was done with a combination of cloning a laser at low frequency, and offsetting them in slight energy. Frequencies converting them up into higher frequency, in order to match the qubit energy exactly. So that we have all the energy dumped into quantum computing, as opposed to excess noise to disturb the quantum system. So what we've done in effect is analogous to moving away from a noisy distorted sound of electric guitar, to a tone which is pure than an acoustic guitar but at the same loudness. So what it means in terms of computing, is that we've increased our fidelity by at least tenfold with corresponding increase in quantum computing power. But quantum computing power is one thing. How do we scale up, right? Beyond #AQ 64, right? And we do this by interconnecting our different quantum computing units. So what you see on this diagram on the right, is a constellation of individual QPUs, which are interconnected and they're interconnected by entanglement. So entanglement is nature's second gift to us in quantum computing, and it ensures that information is connected and correlated to each of the individual QPUs. And the point to really kind of drive home, it is the number of interconnects, correlated interconnects, which contribute to quantum computing power, rather than the number of individual cells themselves. And this is what leads to the quantum computing power that Dean was referring to. So in IonQ, we are going to be interconnecting 2 of these nodes. We're working towards that as our first step to creating the truly interconnected distributed computer, much in the same way that a CPU has multicourse connected to give it extra power. So we talked about increasing compute power, but it's also important to think about scaling down in footprint, right? So -- just so that we can make quantum computing more accessible to others, right? So this diagram on the left is the design of our next-generation extreme high package vacuum package. It will hold a vacuum, similar to that of the hadron -- large hadron particle collider, with the same similar vacuum, but obviously at a much smaller footprint, and in order to manufacture and assemble this package, we have to build a much lower vacuum we call an ultra-high vacuum, which is on this right, to assemble this package in. And this would be coming online very soon. So this is how then we're going to be scaling as one part of this quantum computer that Chris will show you, we're going to be reducing the vacuum to a deck of cards, right? Sizing to a deck of cards. So I'd like to just conclude this brief talk with one last thing, to show that this is a reality, and we're driving this home. So I have here a prototype of this vacuum package, that are welcome to pass around and for you to observe. Thank you very much for your attention.

Here, I guess, a question for both of you, maybe more for Pat on your slides there. How are you leveraging the current research and industrial base for lasers today, versus making something that's entirely new? Or I mean you talk about clean frequencies, are those things like the comps business has tried to achieve in the past? You're trying to do different things and therefore, have to find new suppliers and new approaches to get these things done.

Definitely, new approaches to do this done. So just to reiterate, I kind of missed out on a point that this frequency combining had to happen at lower frequency, was technically what I just said could not be done at the frequencies which are usually practical. So we had to do that at low frequency, offset them and then frequency convert into high frequency. So it is novel from that respect, but we're leveraging a lot of off-the-shelf parts to do this, but we combine them in a way which is unique to IonQ.

Okay. So is it difficult to get the frequency that you need? Or is it the noise part? Or what other elements are you trying to do that haven't been attempted or at least focused...

We have to correct for phase noise, and we had to ensure, obviously, during the transfer, the frequencies upwards that we're maintaining intensity, phase, stability as well in order to hit these ions. So quantum systems are very delicate. So it's very important that we're hitting these lasers as accurate of phase, amplitude as possible, right? So that was a uniqueness in the IonQ work. It's really the combination of parts which you can get. And to your -- to answer your question about collaboration, yes, obviously, we have collaborations with the academia to just really stay upfront, state-of-the-art in terms of laser control, and also our computational space as well. We mentioned error correction. That's a big space as well. So we're obviously invest in that area as well.

Okay. Just my last question, just to understand how much of this is ongoing inside of IonQ? Are you developing your own lasers or leveraging outside suppliers that cover a number of optical comms companies? Somewhat familiar with a lot of the companies out there, are you doing this entirely in-house or leveraging what's outside?

We're leveraging lasers from outside, okay? But I think the way we combine these lasers is most unique inside IonQ, in terms of the optical path.

Just wonder if you could talk a little bit about the effort that went into and maybe the design cycle and the shrink here for this prototype that we're passing around. What is the heavy lift there? And do you feel like you've achieved all you can? Or can you do even better than this, maybe further reductions or from a cost perspective?

We could always do better. I think part of the hard technical feats involved in this. We have to preserve polarization as a light coming out. So there's a large material science effort going on in here, especially around combining windows, right, to this package. That is not trivial, because you want to be able to maintain the vacuum, but also preserve the light polarization at the same time. There are 2 challenges. So as a material science challenge we're facing right now, to how to attach these windows to the vacuum package. And at the same time, we stand at this very pressure as well, right? So there's quite a lot involved, but we have some good paths to get there. So -- but we're pretty confident that we will.

One of the things I found interesting is in the vacuum package itself. You see this, what looks like a little glass window, and it turns out that hydrogen will just float through glass, and so it will destroy the vacuum. So our everyday experience with how things work, we kind of think we can hold a glass with water in it or whatever. But what we're doing here, you're actually having such an extreme vacuum, and it doesn't take much to disturb that vacuum. So as we're -- so now the question is, what's the other materials you could use besides standard glass, now we found some that to kind of stop those kinds of things from happening. But when you first do it, who ever thought that hydrogen would just flow through a glass window, turns how it happens. So those are the kinds of crazy things that we're finding and exploring. I'll just say something too, it's interesting and from a software point of view, and there used to be an old interview question back in the 1980s that said, if you were originally tennis balls had a little valve on them like a basketball or football. And back in the day, every once a while the tennis ball would fall on that little back on that little valve, and then it would bounce funny. And people who played tennis thought that this is bad. So they came up with a way to produce a tennis ball that didn't have that little valve on it. And the interview question is, how would you design such a system and for software engineers strangely? And the answer turns out, just in case you get interviewed as a software engineer in the future, so you know the answer, is what you do is you take the machine that builds the tennis balls and you put it in a pressurized environment. And now the tennis balls are pressurized, and then when you take them out of the building, there's pressurized -- now the tennis balls are pressurized without having to insert into it. We're doing exactly the opposite. So it's a new interview question for, I guess, for quantum engineers in the future, is the machine that Pat showed was a vacuum that we're assembling the vacuum chambers on. So the exact opposite of what the tennis ball, which is in a pressurized environment.

He tells me that question as well.

Yes. it seems really important for trying to figure out how to do our vacuum. So anyways.

A question for Dean. You highlighted a number of areas you're working on to achieve #AQ 64, the barium, the error mitigation, multi-core. If you had to sort of rank order, which is the most challenging? Or where do you think you still have the most work to do among those 4 or 5 things you highlighted to get to #AQ 64?

So in all of the quantum engineering design, it's generally the optical systems that are the drivers of performance and the most notoriously difficult on the design side. There's a lot of design constraints that we work against. Pat talked about some of them in terms of like getting the kind of basically the beam pointing and everything through there. We have had success with our overall AODs, but we're looking at multi-core work now. And so I would say the optical system design is generally the -- will continue to be the bottleneck and kind of the biggest push up to do.

This may be for Dean as well. I guess, why wouldn't you be using barium qubits now? And what are some of the main challenges you see in implementing barium into your next system? And also, do you expect your competitor, your main Ion Trap competitor to switch to barium as well?

So there's 2 questions there. Let me try to do the first one first. So right now, we have barium in work, in our development systems. So when you go on the tour later, some of the systems that you'll be looking at are running barium as we speak. That development activity is continuing. As I mentioned, we've been investing in kind of the high-performance long chains, we're looking at what we need to do to go to the multi-core and kind of longer pieces. And so to date, the main reason is that, at least on our public offerings, like Forte that we've talked about, we just haven't had to. And so the simple answer is that, I don't want to over index and make the -- anything more complicated if we already have a high-performing kind of a ytterbium system. And so the ytterbium systems are going to be our workhorse through #AQ 35, right? And so the additional investment that we've made on barium will pay dividends when we get to #AQ 64. The other options, we haven't really talked a lot about, but one of the other kind of objectives of that system that Peter touched on earlier, was kind of the fact that we are also taking a manufacturing mindset, the trapped reduction in size like the MGT, those are all kind of manufacturing product-focused investments. The other move to barium in terms of the visible light aspects and kind of the lower maintenance costs associated over UV, all goes into kind of why we want to try to push on barium moving forward. In terms of your second question, they do use barium, right? But they use a multi-species barium and ytterbium. Some are cooling ions, some are computational ions. And so it's just a different architecture, but they need to go to a dual species approach where we're -- for our near term looking at a single species approach.

I just wonder if you could talk to, sort of the size of your guys' R&D workforce and you mentioned the importance of software, can you talk about how many of those people are focused on software versus systems versus more theoretical PhD-level science?

So let's see. I don't want to get into numbers, but like right now, between Pat, myself and Dave Mehuys who will be talking tomorrow, we all touch hardware in the company. My team, engineering is the largest by multiple factors. I would say Dave's team is next, followed by Pat's. And so Pat kind of owns within R&D, kind of a lot of the lower TRL development, Technology Readiness Level development across the Board. He is working both push and pull technologies, things that need to be kind of say, Dean, I need you to really think about on ramping this into your engineering development. There's other questions that I ask Pat, can you kind of deliver that? And so that's where most of kind of the more kind of applied research is occurring. I'm really trying to drive the engineering work for it. And so that involves all the kind of classical software development, kind of firmware development, FPGA development, higher up to stack. Right now, we tilt heavier towards hardware than software, if I think about the overall balance of my organization, but there is a fair number of integrators, right, where we are looking at people who are doing the hands-on work in either theory work or kind of the hands-on integration work associated with the systems that you'll see on the tour. And then Dave's organization takes kind of the engineering artifacts, the kind of technical data packages that my team creates, and uses those to try to drive and inform tooling infrastructure for kind of the low volume manufacturing work.

Just a question from our live stream audience. You mentioned that there's a large amount of the IonQ team sitting within the R&D and engineering organizations, what motivates these people every day? What are the things that you talk about in your team meeting that get people excited?

Perhaps I can start what motivates me. So what motivated me to come to IonQ is, I believe in the quantum mechanical scheme that we have, to get to scale is the most elegant physics out there. So up to this day, I'm still enamored by the elegant physics, which drives this. So I think in the long view, I think that's a very good motivator for us. And the fact that we are beating what we promised as well out there as well. So we have a metric elegant physics. And I think that really kind of drives our organization.

So it's interesting. I have a slightly different feedback from my team and kind of just where I stand, right? I come from Blue Origin. I'm used to having very audacious goals set in front of me that you have to basically figure out how to deliver on, regardless of kind of their difficulty. And so one of the beauties of what we're working on, there is very complex hard physics at work. There's also just a lot of engineering and a lot of block and tackling and a lot of company building that we have to do. A lot of the people that are at IonQ now, one, enjoy the challenge, right? We are setting a kind of a sort of goalpost that are audacious, right? And so that kind of brings -- gets them out of bed in the morning. The second is that just there is an elegance to the work that we're doing and kind of the theory and everything else in terms of kind of the atomic and [indiscernible] physics that's at play. At the end of the day, we're looking at -- trying to build computers, not to build computers, but these are a tool to basically change an industry and change the way we can solve and look at problems. And I think that higher mission is really a big part of what gets really people excited. It's what partly what brought me to IonQ.

I have a question on the terminology. So familiar with the term error correction from the storage industry, but you guys use the term error mitigation. I just want to understand the difference. It sounds like it's proactive versus trying to react while this is running, if that's the way you're doing it or other teams that are separately focusing on would you call error mitigation, I'd love to hear what you're including in that versus error correction, which I'm familiar with. Just wanted to get that terminology cleared up.

I'll take a stab at this and maybe then pass it off to Pat or even Jungsang. But we do have separate teams that are looking at error correction, error mitigation. And so the error mitigation work is something that is not dynamic. It is something that happens prior to compilation and after compilation. It's generally algorithmic, in the sense that you're looking at how to modify the circuits or how to do post processing to kind of elicit kind of a better signal to noise out of the results, right? When you run a circuit, you normally run it many times, you get kind of a histogram of outputs. And there is a real value somewhere, in the very few circuits are kind of a one shot. You get a single answer. It's multiple runs of the actual circuit to be able to eke out the answer. There are some mitigation techniques where you will employ what are called ancilla qubits to be able to augment the circuit. Those are kind of smart instrumentations of the circuit that allow you to kind of once again, eke out better signal to noise or understand kind of some of the error sources. It's not the same implementations as kind of the error correcting codes, but they are there to be able to, without needing to go to very large kind of error correction ratios, which require -- you're looking at thousands of qubits for like a very large #AQ system, error corrected system. So we have an ability to with a smaller number of operational high fidelity qubits, really kind of push the envelope.

Not sure if this is an appropriate question for you, too, but since you're on the R&D side. And then, Pat, you mentioned the elegant physics of Ion Traps, could you maybe spend a minute looking at the other qubit modalities and say, what do you think the biggest challenge is to superconducting QSR or photonic? What are the challenges you think, that those modality space to scaling to a quantum advantage system or fault tolerant system over the next decade?

I want to be careful about not to be critical to my friends like that. But in quantum computing is a balance of fidelity, speed and scalability, right? So each scheme has its different advantages. And I think we have very, very long coherence times here in IonQ. So I think we have our own roadmap to get there as well. So I think it's hard pressed to kind of do Apple storages comparison between these different schemes. But just to let you know, it's a balance of these 3 factors, right, between these technologies.

Yes. Thanks for the question. So at the end of the day, we're trying to build computers that solve problems. And some of that component matters, but architecture matters, tools and software matters and eventually the application matters. So if you look at it from the overall perspective, we focus very much on the hardware and qubits. But I think, as Peter mentioned earlier, this connectivity and the architecture, how to actually build systems that can actually tackle real-world problems. All of that matters, right? So I think it's very important to see that. We have a very strong quality in qubits, because these are add-ons are all individual atomic clocks. All of these are foundations of what makes qubits -- we have quantum mechanically. So we start from that foundation. But we also took an approach where all the qubits are connected to each other, that makes the algorithm implementation extremely effective. So you should look -- and that's what allows us to get to high #AQ numbers. And then the error mitigation is when you see errors, error correction is actually a very expensive way to fix errors. But if you understand what the errors are, you can typically fix them, way before you actually throw a lot of resources. So these are very natural ways. If you look at communication systems, you have like cell phones and optical communications. We do a lot of error mitigation before we get to forward error correction. It's a very expensive process. So I think there is a lot of kind of common knowledge and the foundations that we build these complex systems on. So I think we should look at it from that perspective. Now I know all of our competition is also looking at all of their challenges, and I'm sure they're being extremely innovative. But at the end of the day, its ability to build machines that can solve problems.

I want to ask a follow-up. My perception of gate models is that you -- the programming through laying out the circuits. Is that done by the customer? Or are you guys also now helping to do the error mitigation? Is that kind of becoming a blended effort? Or how is that going to end off, I guess, because I heard that in your explanation of error mitigation.

So what we see right now is both. So we have, as part of our compiler team, as part of our architecture team and system performance, individuals that are focused on kind of these error mitigation techniques, some of the compiler optimizations. We also have our kind of solutions and applications team interacting directly with customers. And so they have an ability to understand the problems, and if there are specific tailorings or error mitigation kind of customizations that can be added, that's the beauty, like working that problem space and those problem sets give us learning that we then roll back into the kind of the larger software base. Not all error mitigation techniques are applicable to all problems, right? There's -- it's not like a universal fault-tolerant error correction code, right? Some of the error mitigation techniques are worked really well for a given problem class, and so you need to kind of understand which compiler settings you're setting, right, or just how to think about your problem. And so -- but we have people, kind of both within my organization as well as within Jungsang's organization, kind of both thinking about this and working through that. And then we collaborate on a regular basis, trying to drive our new capabilities that are developed kind of in the kind of the base software stack to our solutions team, as well as them bringing that kind of knowledge back and kind of informing the software development.

I'll just mention just an interesting relative to a CPU versus a QPU. And CPUs today, like when I was at Amazon, we built micro services. What you see is Amazon.com is no longer a monolithic application, it's an application which is spread over thousands of servers with little micro services. So there might be one little service, which does taxation and another one that does another thing. They're all glued together, and that's what you see as the final application. But there is not a compiler technology today for a CPU. They can take a monolithic application and then spread it out over a number of CPUs and GPUs. Instead, we require software engineers to figure out where the boundaries are, as to what things should run on which machines. Like at Amazon, we would say, well, this will be the taxation component. The compiler didn't figure that out, a software engineer did. What's interesting in our compiler is, we're now going to a multi-core system, and we don't want the end user to have to go through and divide up the application to figure out what runs on different QPUs. Instead, what we're doing is taking a monolithic circuit, one large circuit and then the compiler is going through and saying, Oh, I think this part of the circuit should go over here, because maybe it has the least connectivity that's needed for another piece that's going to run on a different QPU. And so it's going through and breaking the circuit up over multiple QPUs for you, instead of having the customer actually having to do that. If we're successful, those are the kinds of things that customers shouldn't have to be able to think about, and I can say as a software engineer, boy, I wish I had that in the CPU world, where I could take a monolithic application and just somehow magically spread across all like available compute resources. So we're really excited about the compiler technology that we're developing here at IonQ.

Just another question from online. Can you speak a little bit more about the vacuum and what that means, how you measure it and how it compares to other vacuum systems and space, things like that as well?

Is certainly cleaner than space near earth, I can say that. And I stated that it's going to be as good as the vacuum in our kind of particle colliders, and measuring -- actually measuring the actual vacuum turns out to be a huge challenge. And so we're having to employ really metrics on our ions themselves, right, as they've been trapped in order to kind of measure the vacuum. So that's the level of vacuum that we're getting to, right?

Just to do a riff on that. The problem is that measuring the vacuum that we're in right now is being kind, doesn't have tools that allow you to get to that level of vacuum. So the vacuum is just so damn good. How the -- there's just not good enough tools yet, to be able to measure the vacuum that we're trying to create. So we're doing better than the best measurement technology that's currently available to mankind.

And then last one from online. You talked a little bit about the pathways to get to #AQ 64 and beyond. Are there any hurdles that you foresee? And how confident are you in getting there in 2025?

So I'm very confident. I mean, I guess, the scientist in me knows that anything can happen, right? We are dealing with atomic physics and everything else than the kind of optical engineering. But the engineering me is basically, we have an engineering plan, now it's just a matter of execution. And so right now, the biggest hurdle to 2025 is simply execution. And so -- but it's fully within my grasp.

Okay. If there are no further questions, we will thank Dean and Pat. We will now proceed to our lab tour. The lab tour will not be live streamed. So everyone online, we advise you to keep your browsers open, and we will see you back here at 11:45 Eastern for the next session after the lab tour. [Presentation]

Thank you for everyone who's been waiting patiently online. We're going to continue with Jungsang Kim, our Co-Founder and CTO, talking about how quantum applications work.

All right. Well, welcome, everybody, to IonQ and our Analyst Day, first, in person. My name is Jungsang Kim. I'm the co-founder. We -- Chris and I founded the company technically in 2015. And I've been thinking a lot about kind of what it takes to start from in these trapped ion, which was more of a physics experiment, all the way to commercial relevance. And I think we're at the stage where real-world applications are being looked at. And just as Peter mentioned earlier today, the field really started from Science to now Engineering, and I think we're eventually migrating to products. And I think on the application side, a lot of the academic community has been focusing on the question of quantum supremacy, right, which is what are the applications and algorithms where Quantum can do much exponentially better than classical. That's been the main focus of the question in the early days. But I think things are shifting now. Now that we actually have a real world of quantum computers with significant AQs that can run relatively complex algorithms. We've actually, over the last couple of years since we have introduced Aria. We've actually started engaging with a lot of our partners and customers and clients to really think about the real-world problems that the academic community really has not been questioning, right? These are real-world practical applications. So what I'd like to do in the next maybe half hour or so is just to outline, give you some examples of -- basically, I'm going to give you 3 examples of the algorithms and applications that we've been thinking about, just to give you a sense of what Quantum computers are capable of doing. So -- and this is really kind of headed towards what we call the enterprise-grade Quantum applications. And this is where Quantum Solutions will actually be better in some commercial sense, whether it's cheaper, faster, more capable than classical solutions that are out there. Hopefully addressing -- with the goal of addressing real-world use cases that the commercial world will benefit from. So having these high #AQ machines where these types of approaches can actually be developed, tested and validated. And with also a very clear projection of what the advantages are into the future as we continue to increase our #AQ is one of the areas where I think the most exciting development has been in the last few years. So we -- IonQ and its partners lead in quantum application development which has a huge market potential in the future predicted by BCG as shown here with all the numbers. So today, I'm going to give you -- walk you through 3 different examples. First is we've been working with Airbus on cargo loading optimization, and this is a canonical example of what's called an optimization or logistics problem. The second is we've been working with Oak Ridge National Labs, and we've actually managed to simulate the benzene molecule. It is, again, another canonical example of molecular modeling simulations. And then the third one, the last one is Quantum machine learning. And machine learning is a very diverse field with lots of -- lots of applications so is Quantum machine learning. And the one that I'm going to discuss today is for image recognition and classification problem that we've been working with our partners at [ Yenda ]. All right. So with that, I'm going to walk you through some of the more details of the problem setting and so on. So this will be a little bit of a more technical discussion. So the first one is cargo loading optimization with Airbus, and this is what the problem looks like. So again, this is somewhat of a simplified problem. Right now, I think when you have a bunch of cargoes that have to go within airplane, there's a very -- there isn't a very systematic way of optimizing how that's loaded. So here, we think about the problem into a package and bin. So we have a bunch of packages that has to fit into some finite number of bins. We simplified the problem by thinking about there are three -- the bins are actually categorized as fixed sizes, and then we have 3 different sizes of packages. The first package fits into a single bin. The second package are half-size bins, which means that you can put 2 of them into a single bin. And then the third is a package that's big. It requires 2 bins to fit. So we think about these 3 types of bins or packages that have to fit into an airplane bin. And then the airplane is now divided up into the segment of bins, one through end, and making sure that we can load the packages with a lot of constraints that actually makes the transportation work. So that's the challenge and a problem. And as the number of packages have been increases, this becomes a very, very expensive and challenging problem to solve. So let me then dive into why this is important. So the task here we work with Airbus was to develop a proof-of-concept quantum approach for this problem for airplanes. And of course, if we can solve these problems efficiently or effectively, the potential impact will be -- you can lower the operating costs, you can reduce the fuel emissions and, of course, the increase of loading productibility and logistics so that there's a lot of business benefits to optimizing this problem. So there are some constraints and the constraints here makes the problem hard. If the problem is not constrained, then the optimization is actually not too hard. But there are some constraints that imposed. First of all, you can't actually overload the plane, so the weight kind of over exceeds what is limited for the aircraft. That's the first constraint. The second is we actually have to load it in a way that the plane doesn't tilt too much, meaning if you put a lot of weight on the front or the back, then the plane will tilt. And therefore, we actually have to make sure it's balanced. There are some sheer limits of the aircraft that must be respected, meaning we can't -- we have to be -- the structural strength of the airplane has to be respected. And then we -- I told you about the 3 different types of packages, right? One that fits in 1 bin, one that actually -- you can actually fit 2 of the packages in 1 bin and the third one that will take up 2 bins. And then the total volume of packages can't exceed the volume of the bin. So these are some of the constraints. And now we actually have to find the optimal solution, making sure that these constraints are respected. So we have to set up a quantum algorithm that actually resolve this issue. So typical problems like this, as you have a bunch of packages on the left and a bunch of bins on the right, you can draw a line of which package goes which bin. And the number of lines you can see here can grow very, very quickly as the number of packages and the bins grow, okay? And then of all the possible lines we can draw, we have to -- we have to find ACAT where each package is assigned to each bin, but in a way that all of these constraints are satisfied. So in order to actually solve this problem, we are not only running the optimization algorithms, but we also have a mixture of some machine learning algorithms that actually help identify the right solution set -- subsets that satisfy these constraints. So as the number of elements increase, the possible combinations grow very, very quickly, and this becomes a very difficult problem, very rapidly. So the way this quantum computing works is we actually think about all possible assignments that actually satisfy these constraints. And then we actually compute the cost function. The cost function is how optimal this is. And then we actually find a variational approach to actually optimize this thing until we find a good solution, and what we have done with Airbus is solved a very concrete problem that utilizes up to 28 qubits. In this example, we think about loading 7 packages into 4 bins, and that problem seems to be a relatively small one. But nevertheless, the actual procedure, end-to-end procedure for setting up the problem, satisfying the constraints, running it on developing the quantum algorithms running in our quantum computer and validating that it works was done on a 28 qubits for [ T system ]. And this actually was the largest optimization problem utilizing the largest number of qubits that we know of to date. And of course, if we can actually -- we're certainly working on scaling this to a larger set of problems. We're continuing to innovate the methodology, so we can actually tackle larger and larger problems as the quantum computer size increases. But the potential benefits of this loading optimization at scale is the increased efficiency, fuel cost savings and labor savings and more optimal operation of this aircraft problems. So that was our first example. I'd like to move on to the next example, which is molecular modeling simulations with Oak Ridge National Labs. And I'll try to illustrate why some of these chemistry problems are also extremely challenging. This is actually one of the earlier contexts from which quantum computing was proposed in the '80s by Richard Feynman. So let's think about a simple molecule like water. The water has 1 oxygen and 2 hydrogen. And you see this little dots we put on the oxygen molecule. Those are the orbitals, meaning that's where the electrons reside. And then when O and H connect, that is where 2 electrons, 1 from hydrogen, 1 from oxygen, actually not hybridize, and that's where the molecular bonding happens. So that line is like a 2 orbital and each of those dots is an orbital. So here, there are -- the number of orbitals that we think about -- orbitals is where the electrons can live, and then the electrons actually occupy those orbital states. And then now they -- the electrons in each orbitals can actually interact with each other. And then depending on how their interaction is, their energy is lowered and then the lowest energy state is what's the most stable, and that's where the molecule stabilizes, okay? So with the water molecule, it's relatively simple. There are 6 electrons on oxygen and 2 on the hydrogen that we think about. And we have done several simulations on water molecules with quantum computers at IonQ. The next molecule that you can think about is a little bit more complicated. This is a carbon in the middle, 3 hydrogen and 1 fluoride. That's called the methyl fluoride. You can see that the number of orbitals is now quite a bit larger, right? There's 4 so-called covalent bonds and then there is 6 additional orbitals that are localized to Flourine. And this kind of molecule is actually relatively simple, but we can actually still simulate this, and we've done that simulation here as well. Now the next one we're looking at, which is the subject of this specific study is a benzene molecule, and that requires 6 carbon and 6 hydrogen. And this benzene has an interesting history where people knew that there were 6 carbons and 6 hydrogens. But there are so many hands or these orbitals from carbon that people didn't know how structurally this was done. And I think one of the German chemists in his streams, we're thinking about -- he dreamed about a snake chasing its tail. And then he realized that there is a configuration where you can actually have 6 carbons and 6 hydrogen and make it very stable. So this actually is the structure of a ring. And within that ring, a lot of the electrons are shared across the carbon ring with spokes that are connecting to hydrogen, and you can see that there is a lot of symmetry in this. Symmetry in here means that if you take that benzene molecule and rotate it by 60 degrees, then it actually repeats itself. There's a lot of symmetry. And you can actually utilize that symmetry to simplify the problem, although there are many, many orbitals that are interacting -- interacting electrons within that molecule. So this benzene -- I'm going to show you that these benzene molecule simulation is something that we have very recently done. It is actually one of the largest molecule that was simulated using a real quantum computer. But there, the innovation really was kind of utilizing the symmetry to reduce the problem, making your quantum circuit as efficient and compactly compiled as possible, and then running the highest performance quantum computer to actually execute on that. So those were the 3 combination of efforts that were -- that enabled us to simulate the benzene molecule. Now of course, the real holy grail is now going to a much more complicated molecule. In this example, I show you caffeine and this is something that we all drink every day. And you can see that in Caffeine, there is this ring. There are a couple of rings that you see. So they're not all carbon rings. There are some nitrogen rings and so on. And by the time you get to this kind of molecules and Caffeine from an organic molecule point of view is actually not a very complicated molecule. Some of these organic molecules have thousands of atoms and very, very large number of orbitals. But even this caffeine molecule with these many atoms are relatively simple, has enough orbitals that it is impractical or impossible to simulate this kind of a molecule using classical computers. Okay? So I think the trick is if we get something that's substantially bigger than benzene, we get into a regime where classical study of these molecular dynamics become very, very challenging, and actually very soon, it becomes impossible to do this on a classical computer. So just like the optimization problem, as the number of orbitals increase and the number of electrons that are interacting increases, the computation of power that's required to consider all of these electron interactions within the molecules actually flows up exponentially. And this is where Richard Feynman said, okay, these quantum systems are interacting very strongly. It becomes very quickly intractable with classical computers, and that's where the first concept of quantum computers were introduced. In this example, we use what's called the variational quantum eigensolver, which is we actually come up with a quantum state which we -- it's called an [indiscernible]. It's actually a model of quantum states that reflects the actual interactions within the molecule. And then we actually treat the variation of numbers, parameters to see if we can get to the lowest grown state, which actually matches the real known energy states. And you can see here a history of the molecules that we have simulated over the years. You can see that there's a couple of water simulation we've done, right? There's one in January and one in November -- One in January '23, another one a few years ago. And then we have this hydrogen 10. That's a hypothetical molecule people introduced to benchmark the complexity of quantum chemistry. And then you see that the lithium hydrate and lithium oxide, those are actually relevant molecules for battery chemistry that we've studied in the recent past. And of course, by the time we get to benzene, you can see that these dynamics have utilized some finite number of qubits, some are in this example, somewhere between 2 qubits to 12 qubits. But as you can see, as we get to more and more complex molecules, the number of entangling gate operations or the depth of the circuit actually increases very, very quickly. So in order to study this benzene, we were able to utilize the symmetry to compact the problem and then come up with a very efficient [indiscernible] or the guest functions that will get you to the accurate answers. But utilizing this to simulate benzene was the most complex quantum chemistry simulation that was performed on a real quantum computing hardware today, okay? So we're actually at the forefront of this. This is really a -- in order to get the right answer is a combination of innovative algorithms, optimization and high-performance hardware to actually execute on all of them. All right. So that was the chemistry simulation. Now as we discussed, if you look at the problem size as a function of the wall clock time to solve it, the blue line, you can see that the Y-axis, the vertical axis is now plotted in low scale. So it goes from seconds to Millennia very quickly, right? And then as the problem size increases, estimated classical wall time to solve this problem grows exponentially, meaning you can go -- you can increase the problem size a little bit and it will go from days to years to Millennia very, very quickly. And that's kind of the challenge of exponential scaling. Now exactly where that line lies depends on the classical computer you use, right? Obviously, when you use a laptop versus like Frontier at Oak Ridge, that line will move. But the fact that, that's a very steep exponential line that only moves very slowly with -- as a function of the classical computational power you throw in, that fact doesn't change. Now if you look at the red line, the Quantum solutions, you can see that the slope is extremely, extremely mild. It's only a logarithmic timescale. And that's because -- sorry, it's exponentially faster in this comparison. I mean that is actually what gives you -- gives quantum computing the power to simulate these problems. So at some point, if you think about on the order of days to weeks is a reasonable time scale to solve a pretty complex molecular problems, and there will come a time where these 2 lines intersect. And then even if you throw a much higher classical computer power, and that line is not going to move very much. So I think getting to this molecular simulation methodologies that scale efficiently on quantum computers is going to be one of the potential wins that Quantum computer can bring to the table. All right. With that, I'd like to then move on to our last topic of a Quantum machine learning for image recognition. This is a collaboration we've had with [ Vicente ]. And the first problem that we actually tackled and solved is this problem of image recognition or the road signs, right? So this is a picture of German road signs data set. And there's 43 different types of classes of images that we looked at. And the question is, can you use a machine learning method to identify which road sign you're looking at. And of course, if you think about self-driving cars, autonomous driving and so on, in this type of algorithm execution to identify road signs, to take the input from the road so that you can act on it is a really important element of that technology. So what I would like to do is we actually created a little video of how this quantum machine learning for image recognition works, okay? It's a pretty extensive process and very counterintuitive. But hopefully, this video that we're going to show you next is going to walk you through that process. And then after that, I'm going to take some questions. [Presentation]

All right. I hope you enjoyed that video. Just to summarize, I think what I would like to convey to you is we are actually looking at problems that are of real world relevant today with quantum computers. And in all of these problems, in many cases, quantum solutions have not been explored. But every time we dive in with our customers and explore, there are very, very interesting and impactful ways that quantum algorithms can impact and influence a better solution. So we believe that, that activity of application development will enabled by the powerful high #AQ machines that we develop is going to be an area where there will be a lot of very, very important and exciting progress will be. All right. With that, I'd like to open up the floor for any questions.

I think it was the benzene simulation where you used 8 qubits and I think 69 gate operations. It seems like Aria with 29 -- or sorry, [indiscernible] 29, #AQ would be able to do more -- handle more qubits and/or gates. And so what was the limiting factor in that benzene example that kept you only 8 qubits and 69 operations?

Yes, that's a great question. So we actually started looking at benzene, which typically people thought we need a lot more qubits and a lot more gate tabs to simulate. But I think one of the biggest innovation is we can actually now take that same chemist co system, and we were able to compress it down to smaller and more efficient circuits so we could get a good answer. I think what that leaves is if we took a step at a bigger molecule, and then if we can actually do that, same compression and same innovation and deeper circuits, we should be able to look at a bigger molecule that way. I think that's the lesson. Here, I think our goal is to take a target molecule and find the most efficient way to get to the most accurate answer possible. So we were able to do that. So I think that was a progress.

So you weren't sort of hardware limited in that case?

We were not hardware limited in that case. No.

You had talked earlier on the bin packing and I believe it's been packaging one, but you said something about the lowest energy level in finding that. Did I hear that correctly?

Yes. So in all of these variational algorithms and it turns out that optimization, the chemistry problem and machine learning, they all have a very similar structure where we come up with what we call an [indiscernible] model with a bunch of parameters, and then we tweak the parameters. Now in molecule that figure of merit that we look for is the lowest energy because that's where the molecules actually stabilized into. In terms of optimization, we compute what's called the cost function. The cost function is basically for any optimization problem, we want to minimize the cost of doing whatever that constraint problem is. So we have to know how to compute that cost function and make sure that, that's minimal as we find better solutions. In machine learning, we use what's called the loss function. And this is actually how accurately can you predict the answer that is also parameterized, right? So these are the final goals that you want to minimize, whether it's energy, the cost function or the loss function, that actually tells you that you have a better solution. So depending on the context of the problem, it's called different things, but they actually all do the same thing, right? When you want to optimize or find the best solution, you have to have a target that actually improves that number.

Yes. I just thought it was interesting, annealing is the only term I've ever heard someone referred to that lowest energy level. And so I was kind of surprised to hear you mention that. Is there any correlation there? Is it the same, similar? Is it just maybe terminology, that's a difference?

Yes. It is a similar concept, and the terminology is a little different because when we do optimization, we look at cost of that optimal solution rather than the energy. But conceptually, I think they do very similar things.

Just wondering, since you're giving specific customer examples, how are they approaching this commercially? I mean is this -- it seems like to the extent that they're doing things that they could do classically, but obviously, the temptation here is to scale it down the road. Like just can you talk about people like Hyundai, how people like Airbus are thinking about their long-term investments here?

Yes. So when -- so many of these engagements that we have published results started a few years ago. So this is where people are saying, okay, can quantum computers solve practical problems. That's kind of the way we got started. What we have done is after looking at this, we actually got a lot of insights on how, more specifically, Quantum can actually give you the advantage. And one of the examples we talked about is the model is more efficient, we can actually have a fewer parameters, which means that they train more efficiently and so on and so forth. So once we learn these things, and then now we want to go and look at the very specific use cases where that will be optimal. And of course, today, at #AQ levels that we have in the labs, you can actually simulate these things with classical computers. But the question is, how do those opportunities and advantage of scale as we approach #AQ 64 and things that are now quantum advance capable. So those are kind of the big questions we're asking and then the methodologies that we're developing and looking at the projection of the potential advantage is kind of where a lot of the current activities are. Yes, Peter has a comment.

Just to add a little bit to that. So what customers are doing right now is doing the algorithmic work. To be able to come up with the algorithm knowing that when they need an #AQ 64, sometimes even a bigger system than that to be able to run these algorithms. So they're kind of saying, okay, I know when you're going to have an #AQ 64 system. I don't want to start development that day because then I'll be behind. So I'm going to start it beforehand and do a much smaller problem, but I've now got the algorithm. And so if I've got the algorithm and it's ready to go, all I need to do is wait for you guys to build a bigger system. And now I can run that in a production environment on a quantum computer, and I can see my ROI. So people today are not expecting in these things to get an ROI today. They're basically saying, okay, 2 years from now, I should be working on the software today to get started to make sure that when the machine is here, on ready to go to be able to take advantage of it, and I can use that in a competitive environment against my competition. So that's really what's going on. Some of them too are not maybe -- they're maybe a little counterintuitive. Like why is Hyundai working on image recognition for a quantum computer? Do they think that there will be a quantum computer in every car, and the answer is no. But we're creating the model on a quantum computer and doing better than what you can do classically, but then maybe the inference for the model that we create would actually run on a GPU or a CPU, right? So -- but the model is actually better on a quantum computer. So -- but maybe 50 years from now, I'm sure every car will have a quantum computer in the glove compartment. But until then, probably the inference side of these things will run in a classical environment.

This may be difficult to answer, but in some of these examples, maybe the machine learning, how do they go from sort of the -- what you're trying to achieve to coming up with the gate model to implement the machine learning to optimizing the parameters. I mean, is that the -- is that really what's going on in terms of when you say algorithm development, it's coming up with -- hey, we're going to have this sequence of gates and the sequence of gates achieve the outcome you're looking for. I mean, I don't know if there's a way to expand on that, but.

Yes, I think that's what -- it's -- but it's an end-to-end solution. For example, when we do an image recognition, first of all, we have classical images. And then we actually have to -- and then traditionally, they process this image classically and create some models that can actually differentiate the two, tweak the parameters and then you go. Somewhere in between, we actually have to inject quantum. And we can inject quantum in many different ways. We're starting small, where we're only replacing the model with Quantum, but that still requires a classical image to be loaded into a quantum state at some point to run the quantum circuits to actually evaluate the model, and then we tweak the model. And what we're finding out is the quantum models are more efficient, meaning we have fewer parameters, we can train with your data and so on and so forth. We can also start to expand the tasks in that end-to-end chain where Quantum can do more, and those things are all being explored. So again, there isn't a single solution to this. There are many, many different quantum approaches and new innovations actually add to new solution approaches that are happening very, very rapidly.

I'll just add. This is actually -- image recognition is something that's actually out of my past because we worked on optical character recognition with [indiscernible] as well. And so what was interesting is that when I was doing optical character recognition, there were about 29 passes of preprocessing that happened on the image. You can imagine in these, for instance, in these images we look today is maybe one path would be to go through and do edge detection of the sign to remove the trees and everything outside that. So a bunch of classical approach would be to go through and to preprocess. Like as an example, maybe the camera image is not 100% onto the image, so maybe it's skewed. So maybe it's got like a keystone shape to the sign. So I need to deskew it before I run the actual thing. So when we were doing it for optical characteristic, there was 29 of those. Maybe the text is sitting on a curve surface. So now before I start the OCR, I need to take the curve surface and bring it back to flat. Or strange things like there's a flash going on. And so at the center of the image, there's a bunch of white pixels, which is the flash coming back to me because I'm on a glossy surface. 29 of those steps happened before we actually got to the actual image recognition OCR, what I find fascinating here is we didn't do any of that. We didn't go through and do any of that preprocessing. The Quantum system did it all as part of the quantum process. And as we added more qubits, it just got better. But if I was to look at this problem more than likely, I mean, I haven't done self-driving cars, but my bet would be that the engineers would be going through and trying to figure that out and say, okay, we need a special case for rainy days. Because I need to be able to take droplets on the lens and take those out somehow before I hand it to the image recognition. Quantum didn't need any of that. It just seemed to get better as we added qubits. And that's really interesting, too, because in this problem set, classically, as I approach perfection, the amount of software engineering became exponential into itself. We got to about a 98% accuracy where we recognize from a software point of view to get the last 2% was going to be at least the same level of energy that we had already put in for the 29 because we probably have to come up with another 29 special use cases. What's fascinating so far is quantum doesn't seem to need that. It's just adding some qubits to it seems to improve the results without me spending a lot of energy to go through and actually engineer a better solution. So when I look at kind of the work we did with Ray versus the work we're doing here, it's a completely different approach. The classical approach required lots of software engineers to be really smart and look at the data and figure out what does the snowy picture look like? And how is that different than a sunny one? And how can I preprocess those both to the same day. So anyway, it's a really interesting approach here. And then at the end of this, we'll produce a classical inference that actually will run on a car.

Yes. Actually, if you look at the end-to-end process, in order to do a good recognition, we actually have to do all of these processes, right? The question is what chunk of it will be taken up by Quantum and what is the novelty. But I think in some of these early examples, yes, we did quite a bit of preprocessing in the classical domain. But we're certainly moving into areas where many of that task can be done by Quantum as well.

[indiscernible] is a model that maybe heads you a parameter, where you deploy your [indiscernible] engine instead of having 1,000, 10,000 or 1 million, you have, some order of magnitude, lower parameters of smaller model, faster.

Yes. So that efficiency of the model is something that we, but also a more broader quantum machine learning community starting to really appreciate. So those are opportunities where more efficient models with your parameters that can be trained with less iterations and fewer data can actually come into play.

So I had a quick question on Airbus or even Hyundai. Can you talk -- walk us through, maybe the customer interaction from when you first began discussions until you kind of ran through a solution? What does that timing look like? And what is that heavy lift? Are these applications that you can present and become almost a solver that you plug into, and just kind of the graphical user interface, just trying to understand the resources dedicated to that?

Okay. That's a great question. And it's a journey. And I think we're actually building as we go, right? So when we first interacted with these customers, they bring their business problems. And they're not necessarily saying, "I know there's a quantum solution. This is just a problem I have. Can you actually help solve it with Quantum." so we actually -- the initial interaction is we have a lot of quantum experts, but not necessarily domain experts. They have a lot of domain experts who doesn't necessarily have quantum solutions. And there's a lot of mutual education. And we come up with various ideas and validate that those quantum solutions will actually help you tackle these problems. Now we're actually accelerating on that, right, compared to the very early stages. And now the next question is, how do we actually bend them, put this onto a more production scale solution. And of course, we have to project that into the future as more Quantum Advantage capable machines comes into play. So the next question is, what are the platforms and tools that are needed? And I think Dean mentioned about -- earlier about that platform and tools. So if you can actually start to really -- we're working on figuring out how to package those things so that they can operate better on a production scale. And all of those are necessary so that when we actually meet that commercial value, then we are ready to deploy the scale. So we're really going from an exploration stage and now into kind of more of a -- how do we package them in terms of production environment. So it's a spectrum. And I think as we accumulate more experience, it becomes more and more efficient to actually iterate that more quickly. So that's kind of the advantage that we have.

Excellent. Hello, everyone. Welcome to IonQ to those who are here, and welcome to IonQ for everybody who is joining us remotely. My name is Ariel Bronstein, I lead product for IonQ. I joined IonQ a little under 2 years. I actually joined the same day as Dean. We are in the same cohort. And prior to IonQ, I came in from Google where I led some of their advanced technology projects, AR, VR and a few things that I can't actually talk about. But what brought me to IonQ is the privilege of working on the next generation of computing. It is truly a privilege and I think the responsibility. So that's me. Rima?

Hi, everyone. I'm Rima Alameddine, I'm the Chief Revenue Officer at IonQ. I joined 9 months ago. Previously, I was VP of Sales at NVIDIA, where I built from scratch and led the enterprise sales AI business for the -- for half of the country and then our transition to lead the Americas enterprise sales AI business for a number of verticals. And I'm thrilled to be here at IonQ for many reasons. And I see a lot of parallels from quantum today and the early days of AI. And I believe it's going to follow the same path, but just be even much more impactful. So thank you for joining us here today.

Good afternoon, everyone. My name is Margaret Arakawa, I am the new Chief Marketing Officer for IonQ. This is my seventh work day. So I have the same questions you do. But as far as background, I worked at Microsoft for almost 20 years, and I worked on every commercial product that Microsoft sold because I started as an NT product manager, from security, networking, developer tools database. Everything I started selling on-premises, then I've sold hybrid and I sold the cloud. And that journey, similar to what you said about the AI journey, I love just that journey, and yet still a lot of people have not moved to the cloud. But all of the first workloads were the ones I worked on 25 years ago. The last job I had at Microsoft was I was running the Windows client OS business in the United States as revenue accountable for scaling that business. It took me 2 years when we got to finally launch Windows 10, and the primary reason for its success was we listened to customers, we deployed with them and we scaled via partners. That is literally all the mistakes we made before that we made sure not to do for the Windows 10 launch. I've also been at start-ups and small tech companies, where I learned how to sell and market within the AWS and the Google Cloud infrastructure and ecosystem. What was great about that is I was now a buyer of their services and a competing buyer between Google and Microsoft and AWS as well as a partner. So I learned very closely how to grow a business with AWS, Google and Azure. I'm excited to be here, as I said, I feel like my dream job is here because I love the journey. I have patience, I have grit and I love the resolution. And one little tidbit is I helped launch the very first gap.com, you know T-shirts, gap.com. It was in the mid-'90s in Silicon Valley, and I had to call on the gap to convince them that Internet was going to be big. We had excruciating slow dial-ups. There was no SSL, there was no TSL. There was no encryption of credit cards. It was just going in clear. Everything was slow. Everything was copper wires. We didn't have fiber optics. The first fiber optic cables that went underneath the Pacific Ocean I think were in 1996. I had to convince the gap.com, launch your first website. Hello world, we are gap.com. People will buy on the Internet. There will be security and the speeds are going to be astronomically fast. That journey brings me to this company where I believe in the journey, and I can't wait to be part of that.

Very nice. So I will advance here. So I'll start with the product, introduction to our products. And I'm starting actually in the past. IonQ, thanks to 25 years of academic research by Chris and Jungsang, started working on practical quantum computing in 2016. So we are already at it for the better part of a decade, and the company took a very intentional position of making sure that everything that we do touches the world as customers use it and provide feedback. Usually, in the beginning, it's kind of a mixed bag, and that's okay because those nuggets of mixed bag actually make our product better, and that is true for our hardware and our software. We constantly touch the world, constantly work with customers, get feedback, and make it better. As a result of it -- we can see here in this slide, this is sort of a high level -- is that an echo? No. This is a high-level view of our products and services. At the bottom layer, you can find our physical infrastructure, the quantum computers. And you can see multiple generations and will go into those in the next slide. On top of that layer, you have the software, what we call the Quantum platform, that enables access to all of our systems and enable all any feature, any capability of these systems. The quantum cloud allows 2 types of access. There is shared access to our quantum data center, and there is dedicated access. Dedicated access can either be in our data center or on-prem at a customer's facility, right? So we're trying to accommodate different customer needs. On top of the Quantum platform, we offer algorithms and applications that should help our customers achieve commercial value as fast as possible. And as we know, as a need of a nascent market, we also offer consulting services to help our customers get up to speed, bring their workforce to the level necessary to do the integrations and the development and the innovation that we know is possible. So let's start with the first layer, the physical hardware that we have. So at the moment, we have 3 commercially available systems that you can access: Harmony, Aria, Forte. There were multiple generations ahead of it or before that, as some of you have seen in this tour, we have generations that were not made commercially available. So we're starting here with Harmony. And we've also disclosed 2 additional generations of systems through commercial deals that we have yet to announce. As you can also see, we have pushed the performance of the system, generation after generation. And as explained by, I believe, both Peter and Dean, we measure that -- or the proxy for performance is what we call algorithmic cubic. And we pushed it from 11 with Harmony, to 25 to 32. Just as a reminder, AQ 32 is, compared to 25 is, I believe, 16x more powerful. As we move to AQ 35, we're talking 64x more powerful than the previous generation. Going to AQ 64 is 536 million times more powerful than the previous generation, which is AQ 35. So that's not a small incremental step. That is an industry-wide revolution of what will be possible with these systems. So going the layer above that, we're talking about the quantum platform. As I said, we offer shared access and dedicated access. Shared access is preferred by customers who are looking for a very flexible plan that gives them access to all of our latest technology. So they're still experimenting, they're trying different things, they have sporadic access needs, while they choose dedicated access when they want uninterrupted access to a specific system that they're betting on, or it's an on-prem installation, which gives them those benefits on location. Regardless of their choice, we keep a long list of common benefits that they get through our platform, regardless. And this is, of course, a very sort of summarized list. And we maintain as many features to be common across these different modes of access. That Quantum platform was also envisioned from day one to support multi-region data sovereignty. So we began with the U.S. data center that offers both dedicated access and shared access. And we even have international customers who are accessing our U.S. platform, and that's perfectly fine. But we have to think ahead to the needs of customers who do need to comply with regulations about data sovereignty, GDPR and others. And for that, we build a platform that can accommodate those needs that are regional. Our first international presence is in Switzerland, QuantumBasel, our partnership there, that is the beginning of a regional data center that would serve as Europe. We definitely envision additional data centers that could come up in different regions based on their regulatory needs and investments by our partners, and ourselves. On the right side, you will see a unique situation where some of our customers have the rigid requirements for data to never leave their facilities, and you can all imagine what are the scenarios for that. And for that, our Quantum platform can reside within our customers' data center and within their network, and data can completely be retained within that environment. Last, with the sequence of slide, is the fact that we recognize that high AQ and incredibly innovative algorithms is not everything, meaning that is what enables the ability to create an application. But to run an application, you need a production-ready environment, an enterprise-grade solution that covers an entire stack that Dean explained earlier. And that allows us for reliability, flexibility, security. It's very important we have seamless integration into the customer's own production stack. And that is part of that contact with the world. We learn about what is the common production stack, what are these needs. So by the time we get to production environment in terms of AQ and algorithms are being discovered, we are ready to serve them. And then it's all about rapid development and rapid deployment in order to extract that value for our customers. And with that, Rima will take you through the go-to-market.

Thank you, Ariel. Okay. So we all know it's an exciting time to be in quantum. Its revolutionary capabilities will address and help us solve complex problems that we cannot solve with classical computing. As you could see here, Boston Consulting Group estimates that quantum computing will generate end user revenue of up to $3.5 trillion. And here, you could see the inflection graph, and as you could see, machine learning will be one of the first approaches to actually generate commercial value for customers. And today, we're already seeing value in machine learning, but for smaller problems, like Jungsang shared a little bit earlier. But as you heard from Dean and others, we are actually working on growing these machines. That same algorithm that you developed that runs today and shows value on a smaller problem, you can run it on a larger computer and it will have much more value. So as you heard, we are increasing our cubits regularly. Actually, we're adding an average of 1 algorithmic cubit every month. And why is this important? Because every time we add 1 algorithm cubit, we are doubling the size of the computational space and the capacity of our machines. And that allows us to run much more -- much bigger problems and have a much larger impact. Here at IonQ, we are -- as you could see, the first phase is the experimental phase. We here at IonQ are -- exiting the experimental phase and entering the enterprise-grade pace. And that is the term coined by BCG. And then the phase after that, that starts in 2025, that is the phase where customers will start generating revenue from these algorithms that they've built. And as you've heard from many of my colleagues, we have a road map to actually accomplish this vision. And from what we see, we are the only company with a road map that aligns to this vision. One thing we know is that customers are laser-focused on solving business problems. They want to solve them faster and more effectively. And quantum computing is just another tool to help them solve these problems. Hundreds of use cases are being developed across many different verticals in these 4 different areas: machine learning, optimization, simulation and cryptography. And we're working with customers across all these different areas because they all want to take advantage of this explosive opportunity. And machine learning is furthest along than we expected to be the first to start generating revenue for our customers. So we all know that timing is key when it comes to the value that can be captured from disruptive technologies. We've seen this play out in AI. McKinsey estimates that the first -- the first movers are poised to capture most of the economic gain from AI. Actually, these are the numbers. They project that 120% gain for early adopters. Only 10% gain for the fast followers, and over 20% loss for the non-adopters. So the numbers tell the story. And when you think of Quantum, BCG actually believes that gains will be much more pronounced where the early adopters will capture 90% of the value. So basically, if an enterprise waits, they will be left behind because most of the value will be captured by the first movers and the early adopters. And many customers are realizing that and starting to develop their algorithms today. So to capture these opportunities and to grow our business and help our customers with this paradigm shift, we have 4 strategies that we're focused on. First, is quantum economies. And that means helping build or drive economies where Quantum is at the center. The second is commercial value creation. And what that means is working with customers to solve their pressing business needs. Third is government enablement, and that's where we're working with governments to help keep them at the cutting edge and solve their vaccine problems. And fourth is growing through partnerships and working with our partners to help them work with their customers and accelerate the adoption of Quantum. And as you could see here, we're already working with many customers and partners to help them capture new opportunities and enter into new markets. So what is the quantum economy? A quantum economy is a quantum technology hub that is focused on driving economic growth through attracting a highly skilled workforce, innovative start-ups, and collaborations with industry, government and universities. And this is a large opportunity for us, and it's a global opportunity. And it's also diverse with many different approaches. The commonality between these different quantum economies are 2 things. One is are mission-driven; and two, is that they are focused on making sure that they build an economy and drive economic growth in their space and they partner with the right entities to do so. This is a great example of a quantum economy. The University of Maryland started early and partnered with us to build a quantum lab. And the reason they did that is to actually prepare the students for the future and attract an ecosystem of start-ups and also partner with government and industry to build use cases in the quantum space. And we're thrilled that you'll have the opportunity to join us for the opening -- the official opening and ribbon cutting of the Quantum Lab this afternoon. The next quantum economy example that we're so excited about is our partnership with QuantumBasel. So who's QuantumBasel? They are an innovation hub in Switzerland. And they're focused on creating -- coming up with quantum solutions that will benefit the world. And so they invested in 2 generations of our future computers to build these algorithms today, like we've been talking about, building these algorithms today and running them on our future computers so that they could solve these big vaccine problems. And especially in Switzerland, they are at the center of the pharma business and the financial services business, and so they have plans to work with industry to actually solve a lot of the most pressing problems. We're also really excited about this because it brings our IonQ computers to Europe and helps us -- helps us offer data sovereignty capabilities to our European customers.

We are very looking forward to welcome IonQ as our addition to our global partnership system for quantum computing here at uptownBasel. IonQ's technology, IonQ's R&D will attract more business partners to invest into quantum computing, will attract more start-ups to join our ecosystem, and furthermore, will attract future workforce to collaborate with ours. We are looking forward to this partnership.

So as you could see, Damir Bogdan, he's always at the forefront, and he shared with you his vision for the innovation center in Switzerland. Now let's move on to our second strategy, and it is building commercial value and helping our customers create the algorithms that will help them solve problems. As you could see, we're already working with a number of customers to help enable this vision and help them solve their problems. I'm going to share with you an example to give you a perspective of how this comes to life. We all know that correlations are used for a lot of decision-making. They're used for, like, trading decisions and portfolio optimization in the financial services space. They're used to interpret MRIs in the health care space. They're used to understand reliability metrics in the engineering space. So there are a lot of great applications for correlation. So in this example, what we did is we worked with a large U.S. brokerage firm. And what we did is they wanted to understand the correlation between 2 stocks. And what we did is we analyzed the correlation between Apple and Microsoft. We are -- model learned the correlations, and you could see that in the first -- you can see the target distribution in the first block. And then we used generative adversarial networks to generate outliers. So in the first example of what we did is we did it on a classical machine, we used a generative adversarial network and we generated the model on a classical machine. And as you could see, the model missed many areas of outliers that are very important for a trader analyzing their portfolio. And they did that with -- after going after 20,000 iterations of learn to build that model. Then what we did is the next 2 plots were done on IonQ machines. The first one used a quantum generative adversarial network. And as you could see, with only 1,000 iterations, it caused a lot more of the outliers. And then the third one used a circuit born machine, which is a type of generative adversarial network leveraging entanglements. And when you look at the results, they're astounding. The plot is almost identical to the first one. It actually was able to generate a lot of the outliers. And it did that in only 26 iterations. So why is this example important? For 2 reasons. One is when you use quantum computing, it actually is more expressive. So what that means is it's able to share with us insights that you cannot find on a classical machine. And as you could see here, if you use only classical machine, it did not generate these outliers that are important to a trader trying to analyze the risk of his or her portfolio. So one, it's able to share insights that are not possible today on classical machines because of the expressive nature. Two, it is also much more efficient. And what that means is it could do it in less iterations. 20,000 iterations versus 26 iterations. So it's much more efficient in how it does that. And this is just one example, as you heard from Jungsang, we're working with many customers to develop these algorithms, and prove value today that they could run on a larger machine. Actually, this exact model, we've published a number of papers about these techniques. And we estimate that this model will scale and will start providing production value at 50 useful cubits, which is just around the corner for us. Our third strategy is government enablement. And that is where we are working with governments to support their core missions. So what does that mean? One, we're working with them on innovative projects to push the boundaries of what is possible technologically. Second, we are developing quantum algorithms with them. For example, here in the U.S., we're working with Oak Ridge National Labs to -- on a project that's a grid modernization project. And third, we're helping them build the workforce of the future so that they could take advantage of these capabilities. So just a few words about our partnership with the Air Force Research Lab. We actually started working with them last year, and we're focused on computation and network research and building algorithms to help them stay competitive and to advance our national security interest. So as you could see, I actually went through 3 different strategies, 2 of them were mission-driven: the quantum economy strategy and government enablement strategy. And what we see is that these mission-driven customers will gravitate towards investing in systems and working on developing algorithms on these systems. And then I covered our commercial value-creation strategy. And those customers are ROI-driven. So those customers are working with us more on accessing our services and our computers via the cloud and working with us to build the algorithm that they can run on their future computers. So with that, I am going to next turn it over to Margaret, who you've just met, she's been here 7 days. But the reason we wanted her to cover the fourth strategy is because we want to give you all the opportunity to meet our new Chief Marketing Officer. We are thrilled that she's on board. And with that, I'm going to turn it over to Margaret.

All right. The last strategy, the fourth strategy that Rima talked about is about scaling through partnerships. That's one thing I learned over and over and over again. Ecosystems take a lot of work. The slide that Ariel showed that talked about the hardware layer, the cloud layer, the tools, the APIs, the apps, quantum computing needs economies and governments to help with that and so do we. One surefire way to scale and -- the 1 thing that we thought we could do in the old days is democratize computing power. I was at Microsoft when we put PCs on desktop. How do you get quantum computing, the most leading edge of all compute technologies into the hands of developers? You do that via the biggest cloud providers on earth: Azure Quantum, Amazon Bracket and Google Cloud. IonQ is the only company that actually has -- is available on all of the major clouds. And as you know, every high-performance compute-intensive customer needs, all of those customers are on those 3 clouds. There's not a single customer that is not working with those 3 vendors. And as hyperscale cloud vendors and partners with us, we're excited that we get to get broad access to a lot of different customers. We also partner with leading consulting software OEM and integrators. It was funny because I looked at this slide and I thought, oh, my God, I worked with every one of them for years and years and years, on compute, whether or not with on-premise, hybrid or cloud. Only place I haven't gone to, or I will, I'd like a trip to Sweden or Switzerland, to go to QuantumBasel. But a lot of these integrators, it takes time for them to get on board. We got them on board. And what they have access to, much broader than we do, they have access to all of the commercial customers. When Accenture says, you should do something now customers listen. They will do the proof of concept. They will do their early work and that's why we needed these customers, working with Dell. Dell is actually both a services provider and the hardware provider. But even though they were classically hardware, they are all in with IonQ. And they are looking to develop services with customers and with us. So with that, I have one small announcement, which is actually not on the slide, but I get to -- I don't know. The next era of quantum computing is here. I had to say it that way. I feel like that's an Iron Man moment. But actually, the Iron Man moment or the next era of quantum computing is going to be discussed more, not just here, but next week, Wednesday. Mark your calendars. It's Quantum World Congress: ionq.com/livestream. We'll have an incredible event where we get to discuss more in depth in front of the world's quantum leaders as well as you, who will always, obviously, be live streaming that event as well. But definitely take a look at that. Please don't miss it. We will remind you and we will invite you again because it's an important event for us. It will be seminal. So I invite you to join us.

And now we're going to Q&A.

We can't leave, not off the hook. You remember, right? The call to action. You remember the Iron Man moment? And then you're all going to do it.

Maybe to start, one question for Rima, from -- that was submitted online. Why are customers coming to IonQ and buying quantum compute? Why aren't they buying simulation of quantum computers today?

Yes. So the reason is you are not going to be able to simulate for very long. The estimates are somewhere between 35 and 40 algorithm cubits. After that, you cannot simulate. So if you have not been running these algorithms on a real quantum computer and learning how to use a real quantum computer, you're going to be left behind. Before you get to full tolerance, there is a ton of value that is going to be created on quantum computers, and you will not be able to do that on a simulator.

We were that clear. We left no questions unanswered.

Hi guys. Have you gone through the excise of sizing how many on-premise computers, quantum computers might be demanded by the market and how to think about that, at least, if not an actual kind of element? And then, of course, there will be a cloud element here, which might obviate the need for the on-premise. Maybe just give us a framework for that as we think about that.

So we have, but -- we have thought about it, and that's -- like if we look at the forecast of what we're building, it's based on how we're thinking about the market and when we think different -- when we think different things will hit. It's something we look forward to sharing more about with you on our earnings call, fourth -- especially our Q4, especially our Q4 earnings as we talk about next year.

But we can say that BCG's reforecast, which gave a much more aggressive view about the progression, we're in alignment with them. We think that their optimism is well warranted.

Obviously, we think the future is strong. We're building a manufacturing plant to build quantum computers to be able to hit expected demand. But in terms of actual numbers for next year, Q4 call. We've all -- listening to -- for Thomas to get next year's numbers. We told them all, the analysts are going to keep asking you this question. Some way they might be able to eke out like how much toilet paper do you plan to use? How much toilet paper do you use per quantum computer? And that would somehow leak into an estimate as to what we thought for next year. So we are not giving any of that information out today. Good job.

Just if you kind of think across the ecosystem and the different quantum players, it seems like there's a pretty consistent group of customers that are working here. And I -- talking about Accenture or Boeing or any of the other car manufacturers, they seem to be playing across multiple different quantum strategies, right? And that's fair. How do you think you capture that business? And where -- what are you seeing in terms of -- and maybe it's too early, but -- but do you find them coming back? And how do you think about just capturing that business relative to your competitors?

Yes. Actually -- thank you for the question. The customers that we work closely with, and we're building algorithms together, we're getting repeat business from every one of those customers, because what we're doing is we are also mentoring them and helping them learn how we're building these algorithms. And they're getting results. They're seeing value. So they're continuously -- continuing to give us new use cases to build. What we find is when customers on their own go and try out things, a lot of times they get frustrated, and it's hard for them. Actually, because of that, a lot of our partners have now asked us to offer services on their platforms, and that's what we're doing. We're going to start offering services on their platforms so that customers can, on different platforms, cloud platforms or with integrators or other partners, so that they can leverage our application development services and continue to be a repeat customer.

I want to bring it back to the concept of AQ. The benchmark of AQ was designed to express customer value. So it's not something that we created as in esoteric some performance metric of -- as some of our competitors are coming up with some weird things that are borrowed from silicon world. We simply created a benchmark based on commonly used algorithms that derive, bring value to customers. So if you simply compare AQ performance across systems, you can see why customers flock to our systems, because they can simply do more with these systems. So it's a very simple equation of what value you can get at which stage. There are algorithms that are not ready yet, meaning in terms of AQ, and will mature as AQ increases. But that is the most useful tool that we were able to come up with to help our customers make these decisions. And it seems to be working in that regard.

Yes. So it's the algorithm and the hardware. And both of them together bring these great results.

I wonder if you could talk about the cloud hosting relationships that you guys have. What's the state of those? How actively are those cloud companies marketing these kinds of capabilities? And can you give us any proof points on how that might ramp up?

Yes. So we work with all our cloud partners. And it's at different stages with different cloud companies. But actually, we are working closely with them to put additional services on their cloud because they realize the value and they want to make sure that their customers are happy and they have repeat customers. So actually, our engagements are becoming stronger and deeper, and there we're getting engaged even more closely because they want us more engaged with our services on the platform, not just access, to the earlier question, so that we can provide even more value to these customers. Does that answer your question? Okay.

I'll just jump in as the newbie that went to all the websites and tried to sign up myself. They're all very different kinds of companies, right? And what's interesting, if you go to the -- you can look it up right now, IonQ and Google, IonQ and Microsoft Azure, IonQ and Amazon Braket. What's great is you will find us on every one of those pages. We are listed first. We are a partner that's been at the leading edge of partnering. But they, too, are learning from us. But what's nice is they're allowing people to use our computers on their cloud to do simulation, to use the Aria computers or to use the Harmony computers. It's not [ obfuscated ]. It's very clear. The Microsoft page reads -- it's in the learning base. You actually have to go through and it will tell you all you need to do to get on board today. Amazon AWS is incredible at that as well. They built a marketplace and an ecosystem that accelerates that. They're great at that. I would say Google is the cloud that is the youngest, and is getting there. But go to their web page, they are the only one that has subscribed right under the name of IonQ and Google Cloud. And that's when you know that you're democratizing access. You're lowering the bar so that people can get up there. Because that's what AWS did brilliantly, right? They allowed young, innovative developers to make up things that we never knew that needed this compute power to do it easily with trial and with a lot of help.

Right. And when we talk about contact with the world, how valuable it is to us, they are the gatekeeper to the largest pool of compute hungry customers in the world in history. And they are working with us to figure out how to convert these users into quantum users. Therefore, they are incredibly valuable to us, and we're seeing great returns for that investment. It's an ongoing investment, but it's very productive.

And I'll just add a little bit to it, which is we are working with these guys to come up and innovate for new problems, new solutions and those kinds of things. So it's a very active thing. They -- back and forth between the 2 of us. There's weekly meetings. We have engineering tasks that we're busily doing, that they're busily doing, so that we can announce new products together going forward. So it's a very active partnership. So it's -- these things are not -- it's just not one and done, you throw it over the wall and hope that it's going to work. We go through. We recognize, for instance, right now, [ Q Times ] on the public clouds to get to our systems, doesn't enable a certain kind of application. So what can we do together to make that easier? So we look at these things. And then there's an active work stream going on to be able to fix some of these problems.

One thing I would also note because I live minutes door-to-door from my house in Kirkland, Washington to our location in the big Seattle offices and the big factory, we're located in the cloud capital of the world. The big 3 clouds are there, as well as the other smaller clouds are there. And every B2B company in the world visits Seattle. And we have access to them. And that's a great reason why we're opening an office and factory there.

You've said that quantum machine learning maybe one of the first commercial applications. And you went through some partnerships and Jungsang went through the use case with Hyundai. But wondering if you could spend a minute talking how are you engaged with folks like NVIDIA that obviously a leader in AI or maybe some of the AI model companies or those engagements or partnerships you're seeking? Is there some sort of feeling that there's competition between classical and quantum? Or can you leverage those platforms for that part of the ecosystem?

Yes. We see NVIDIA as a partner, and we see the companies that are building AI models as partners because actually every single application is going to be hybrid applications. Jungsang described some of the work that we're doing. Every single one of them is going to be plugged into a classical computer. Actually, Peter was describing how powerful these computers are, but they still cannot do 1 plus 1. So we envision the world as a hybrid world, and integrating into a current workflow is going to be really important. This will be the solver that goes into larger application that customers run. So whether companies that are building models, NVIDIA and others, we see them as partners that we all need to actually bring quantum computing to the stage where it's actually delivering value for everyone.

And they all see the end of the ability to simulate quantum. So there is a window in which they can do it, and that's great. It's valuable. But there is an end to it.

Yes. And it's close. It's like around 40 algorithmic cubits.

That's why we all joined now, because it's work to be done, but it's in 2 years. It's not 10, it's not 5. It's 2. So we've got to get a lot of students to become quantum computer experts. We've got to raise government's awareness. We got to do all of that. But that's...

That's the economy, right?

Yes. But how many times you're -- can you see that? Can you see it? Can you feel it? Is it close?

This might be a CEO question, but just following on from that. I mean Intel and NVIDIA have quantum investments themselves, but they sort of seem to see it as like 10 years from now this is something we need to be aware of. If it's really in the next 2 years, shouldn't we see that soon? Shouldn't we start to see M&A activity? Shouldn't we start to see that activity kind of go into overdrive from those guys?

It's funny, over the last 4.5 years, I've seen lots of people, competitors and such, talk about time lines. And they -- it is very different and it's based individually by company. And so you might have one company that maybe won't have a working quantum computer until 2030. So they tend to have a point of view, which is Quantum will take off in 2030. Somebody else might not have a working quantum computer until 2040, and they think, what do you know, strangely, Quantum won't take off until 2040. I wish everyone in the quantum industry would say that I think quantum will take off blah, blah, blah, and at the end, add just a few words, for my technology. But instead what they do in the media and people pick up is somehow that that's something which applies to all quantum technologies, every cubit modality. So where we think right now is that what you're hearing is now we're within 2 years of kind of the promised land of what everyone wanted to be for quantum. And so -- but not everyone shares that point of view because they have different time lines. And so for Google, I believe they said they won't have a commercial quantum computer until 2028. And I'll take them at their word for that, right? And others will be even longer. And so -- but IonQ is now finally at a place where it's not 10, 15, 20 years. That's been said by many companies for years, that quantum is going to be forever. This really is our ChatGPT moment. We're -- and probably just like Sam Altman, I imagine if Sam was to run around 2 years ago and say AI is coming, I bet he could have stood naked on a street corner with a sandwich board on saying, "I think AI is coming in 2 years," absolutely no one would have listened to them. And in fact, actually, you would find lots of people who said -- who would say that is never going to happen. And then sure enough, overnight, it did. And so we're -- we believe now with AQ 64 that we're finally there. And so we haven't achieved it yet. Still roughly 2 years away. We're working on that diligently. We published a road map years ago, and we've got a pretty good track record of actually exceeding what it is we said we were going to do. We haven't gone back and republished a road map or go through or we haven't changed it one iota. We haven't changed the financials one iota from when we were prior to the IPO. We just keep on hitting what it is that we say we're going to do. So -- and my guess is, even despite having today's call and things and all the rest, no one will believe us. And we'll be in a ChatGPT moment roughly 2 years from now. And then people will be saying, where the heck did that come from? I didn't see that coming at all. And so that's probably -- it would be my guess. And actually, I'm not sure how much as a company that we really need to spend a lot of time trying to promote this thing, because it's so close. I probably can't spend enough marketing dollars to convince the world. It's probably just cheaper to actually just go do it. So -- and that's basically the plan.

And actually, I just want to add one thing to what Peter said, which is like some of these people maybe are just waiting for full tolerance. But value is here before full tolerance. If you -- like you heard throughout the presentations with error mitigation, what we're doing, and how you're going to get value very soon, like Peter was saying, it's so close for us and it doesn't need error mitigation. And so that's another thing that maybe companies that don't have a QPU that's ready, they're like referring to like the nirvana state, which you don't need to actually deliver value from these applications. Yes.

I think IonQ is unique in the sense that we think that we should add in just the minimum amount of either error mitigation or error correction necessary to unlock the next stage of value. And I think sometimes some people -- we were down at a conference in Miami earlier and there was a young woman who got up on stage by another company, and said, they thought that you needed to get to 15 nines of accuracy before you could unlock value. So that's 99.99999, with 13 nines after it. And we look at that, and that's kind of like crazy talk from our point of view, because that's such a level of perfection. That really will be 15, 20, 25 years from now, to get to that level of perfection. But there's all that timing between where we can deliver value with systems which are -- have a scaled amount of noise that still allows you to do something useful, but it's not perfection. And so we -- our goal always was let's just do the minimum required to be able to get to the next phase so that we can win in that. The goal has never been let's immediately go to perfection. Let's go -- let's just get -- let's make sure we do the minimum so that we can, as quickly as we can, unlock the value for that next phase. And we keep on doing that, right? Then we'll keep on doing that going after AQ 64. We will go through and say, hey, what is the minimum I need to do to be able to get to 256? I won't suddenly jump and say, well, I need to get to AQ 10,000 as the next step. That would be another 10 years. So, anyways.

I think also to your point of customer value, if Netflix had waited for the Internet to be perfect as they transitioned from DVDs, we have streaming, because they certainly streamed at a very, very, very slow rate -- Amazon.com, I actually got to pick and pack books at Amazon.com and all these sold were books. They invited me into their distribution warehouse. What were they doing? They were creating an infrastructure for delivery. They were building the back end of AWS. But they couldn't have done it. And Peter is a prime example of the hard work put into something that became the billions of dollars of business. So we're trying to get the next Netflixes, the next Amazons to start now. So, all right. Thank you very much.

Thank you, everyone. [Break]

For our next section, we'll turn it over to Dave Mehuys, our VP of Production Engineering.

Good afternoon, everyone. Thank you for coming. My name again is Dave Mehuys, VP of Production Engineering. I've been here at IonQ for about 1.5 years. And in that time, we've been building an operations team to focus on scaling production and deployment. What does that mean? Operations team, it means a bunch of different things. It means supply chain. It means planning and materials procurement, means developing manufacturing processes, assembly and test. It means facilities, it means quality. It means integrating all those things together and preparing us to deploy in our own data centers as well as future data centers. My background actually is more from the telecom environment. So I did spend a little bit of time at a different quantum computing company called PsiQuantum, right before I came here, but I spent a lot of my career at a company called Infinera. So that's in the telecom space, and I was pleased to lead a bunch of different teams there in manufacturing, new product introduction, component engineering, systems engineering, customer service and technical support. So that will hopefully give me a nice background for working here at IonQ, and I definitely found that to be the case in the last 1.5 years. So this is definitely a journey. As Peter alluded to, we started off with [indiscernible] academic roots and making systems with successively higher performance and different capabilities and features, and we are in the midst of transforming that into an engineering company on our way to becoming a product company. Dean has joined us here, and I have to say I'm just super pleased to partner with Dean and Pat. I love that our backgrounds -- we overlap in one sense and that we've all worked with complex systems. And that's definitely something we have here. We also come from different spaces. I come from telecom, Dean comes from aerospace. Pat comes from commercial and other things like that. So I think we're amazingly like-minded and aligned when it comes to how we work on that transformation and absolutely a pleasure to work with these guys every day. So I want to talk a little bit about, I guess, just to open as sort of where we are in that process. We've done a great job hitting our technical road map, leveraging those academic routes and the AMO scientists that started here at the company into building incredibly fantastic demonstrated machines. But that's not all that we've done. We also, in terms of -- if we look at the last few generations of products, everyone is successively moving a little bit more towards something engineered -- purposely engineered, purposely kind of moving towards a more product form factor. So that work started actually already a few years ago. I was so pleased when I got here to partner with team on formalizing some of the business processes that we need to work on to continue this and to more intentionally steer that shift towards a product company. So I think I was here a month, and Dean and I sat down and we said, we need to work on a PDLC, Product Development Life Cycle Process that really organizes and unites our team to focus on the things that we all need to do to purposely engineer systems to be produced, to be deployed. We don't want to lose that great DNA that we have for making fantastically capable higher AQ machines. But we also know if we want to scale these things, we need to also add some other elements to our engineering processes and disciplines to make that happen. So we've already seen the fruit just in systems that we've been building here in College Park this year in terms of approving things like assembly and test. I mean those obviously aren't the only things you need for manufacturing, but they're key elements that move us in the right direction. And there's a lot more detail in the PDLC that being put together and we're collaborating on, that move us in that direction. So key elements of that, obviously, are design documentation and training. Even though the heart of my team's operation will be in Seattle, we'll talk about that a little bit. We've been hiring people here in the last year. We've been hiring people in Seattle last year. We've been working shoulder to shoulder with Dean's team in the lab, building our current generation of machine. That's important for people on my team to absorb the knowledge transfer and hopefully, we give a little bit back because of our backgrounds in terms of, okay, these are the things we'd love to teach and approve about how we design things for manufacturability and how those things get incorporated into our PDLC. So it's super pleasure to work with Dean on these things. And why do we do these things? Because we want to make our customers happy. We want them to get a quality product. We want them to get it faster. We wanted to deploy it with uptimes that make them happy. Meet and exceed their expectations. And right now, as you know, everything we've made to date is in the 4 walls of this building, and that will soon change. So we're excited to talk a little bit more about that. As Peter mentioned, we leased a building in Seattle. It's actually in Bothell, which is very near Redmond and Bellevue and Kirkland. So near a lot of great talent in the Seattle area. And it's our first quantum computing manufacturing facility in the U.S., and it will be our manufacturing hub. Not that we don't do any manufacturing here, we actually do do some component and subsystem manufacturing here in this building, and we will continue to do that as well. But Seattle is where we will do much more subsystems and where we will integrate systems together. So we're super proud about that. Hopefully, you've got a chance to see some of the storyboards outside on your tour. Probably 1 thing you also noticed on the tour is the absence of empty space to expand. So we're showing a picture here of empty space to expand. It is in Seattle. I had hoped to have a picture here for today because this is pretty much exactly the way it looks in Seattle right now, minus a few ceiling tiles and things that they're touching up. But we are basically ready to move in. So as soon as we get our occupancy permit, the early part of next quarter, we will be moving in and we'll be soon after starting production. So we are super excited about that. A little bit more about it. It's 65,000 square feet, so it's about twice the size of the facility we're sitting in today. As I mentioned, we'll be ready to start putting in all our equipment and starting manufacturing in Q4 of this year. We have a great team working on facilities, both our own internal team as well as our partners. We have great partners in Seattle in terms of our real estate brokers, our project managers, our general contractors, our architect, I mean from the point we signed a lease to getting this building designed, permitted and built, 9 months. So we're super proud about how fast that we've done that. Likewise, the team that will go and work there. Myself, Dean, Pat and even other functions have been busy hiring people there since the middle of last year. So we are on track to the facility. We are a budget to the facility cost. We are on track for our hiring, and we're doing that intentionally, because we don't think, strategically, it would be wise to locate manufacturing of such a complex system far away from engineering or R&D. So we are, in fact, strategically co-locating R&D and engineering and manufacturing in the same buildings because we know that getting these initial systems up and off the ground, will take a team effort, and I'm super happy to be part of the team that we have here. Beyond Seattle -- so Seattle, obviously, will be our manufacturing hub, it will also be where we plant our West Coast data center. So we have a data center here as well, which you saw today, which is where a lot of our R&D demonstrator machines go. We will have a data center in Seattle, which is where some of our production machines will go. And you've heard about dedicated access as well as sort of enterprise access for the machines that we build, and we hope to make a home for that here in our data center in Seattle as well as in other places across the globe such as our Basel Innovation Hub. Just like we worked with the team in Seattle in terms of designing the building, requirements for data center and other facilities, we are doing the same thing in Basel now. We have a great partnership with Quantum Basel. We have a third party that we're working with on the facility design as we speak. And so we'll be working on that. And hopefully, at the same rate of speed, I guess, as we did with Seattle. So in 2024, we will be occupying that and doing great things there for our customers in the European region. So we're also excited to do that. That does mean how we build machines from the way we've done them here, we'll have to change a little bit. And as I mentioned, we've already started that. The machines you've seen here largely built in place, largely built by the people that design them. That's not so scalable. Obviously, we're building a team of operations folks that will work side-by-side with the engineering teams, the sales teams, go-to-market teams, product teams to make sure that we can actually deploy these anywhere. So we will continue to build some components and subassemblies here, Seattle, we will integrate systems in Seattle, but we will deploy them in many different places, including Basel next year. So this time next year, hopefully, we will have data centers not only here in Seattle, in Basel, Switzerland, and we will have plans to deploy our quantum computers in those places. So we're excited for that opportunity. We know that means we have a fun journey ahead, but those of us that have done these kinds of things before, this is the fun part of the journey. And as Dean said, we're very highly confident, we see no reason why this can't be done. I'd love to talk to my team about the challenges that we have. I'm super excited to be here after 1.5 years, all the challenges I see, the regular building company challenges. They're not -- will the technology work challenges. And so we're super excited about the opportunity that we have here. Okay. A little bit more about the future in terms of how we in the production engineering department, partner with basically all the functional teams in the company, especially Dean's team, especially Ariel's product team. what are we heading towards in terms of a North Star. You'll hear more about that, as Margaret alluded to, I think, in the coming days and weeks. But we know there are best practices for complex systems that we can and should leverage for quantum computers like many of us have seen in other adjacent industries. Things like rack-mount form factors, make it easy to plan, easy to deploy, much easier to build, test and subassemblies integrate together. So these are best practices for systems engineering that we plan to leverage here as well. Modular design, that's another key thing. So ready to manufacture, easier to spare. Dean and Pat and I, we want to help each other. It's pretty obvious how Dean and Pat can help me. I'd love to be a great service partner to them. When we have a modular design that's rack mountable, we have a platform that will serve us, hopefully, for quite a long time. It will be easier for me and my team to help Dean and Pat by providing them things that aren't changing very much so that they can put in subsystems and components and things that do change and we could leverage that into successively higher performance. So hopefully, that flywheel that I know Peter likes to use that analogy. This is an element of making the flywheel turn faster and help us all get to where we want to go faster. So we're excited about that. Deployable and serviceable. I came from the telecom industry. We had an installed base across the world. We had a fantastic group of tech support teams and network operations teams that could monitor the health of systems across the world that could understand when things were perhaps going out of calibration or might need some upcoming support and we can be on top of that before they happen. So that's the kind of paradigm that we want to put into our product planning as well. Okay. So a little bit more about the future, in terms of where we're going. Some of the things we need to do, certainly in the production engineering context, but even in a company context, you have to build the right foundation. You have to do these things no matter what you build, if you want happy customers and you want a quality result. So I talked about 3 things here: Governance, Quality management System. Quality is everyone's job. It's not just a gate at the end of the process that you have a scorecard and go check, check, check, right? So we're excited to get that going here and make sure that we build quality into everything we do. It's not just manufacturing. Lean manufacturing is something we are designing from the get-go. We want manufacturing processes that have the ability to continuously improve, improve productivity, reduce waste, reduce costs, manage inventory in a responsible way. To do that, we also need some tools. And again, we're partnering with Dean and folks in the finance department to make sure that we stand up the right tools to help us plan effectively, to help us document our designs, to help us work with our supply base, so we can share those plans with them because they're effectively an extended part of our company just like our employees are. So we can do this in a way that helps us also make the flywheel turn more smoothly. And also ESG, environmental, social governments. I mean we're focused probably in my area more on the sustainability aspect, but ESG is something we know we need to do as a company across the board. So we're road mapping that as we speak. From a facility standpoint, we've taken the best of what we've learned here. We've brought in some other elements from other industries that are represented from the kind of DNA and the teams that we have to design flexible and scalable facilities. The manufacturing facility and rooms and the R&D labs, for example, in Seattle, they have exactly the same spec. Because we know we want to be flexible, we know we want whatever we do in our R&D labs and our engineering labs, we want to be able to replicate that environment and manufacturing and vice versa so that we can leverage that. It also makes the building incredibly flexible. We'll use a manufacturing standard for environment, that's the gold standard effectively for electronics and pharmaceuticals and medical industries and lots of talent there as well. We'll make sure we have critical infrastructure redundancy. So when the power may happen to go out, we've got generator backups, we've got UPS backups. We can ensure that valuable uptime for our customers. And finally, supply chain. I can't underestimate the importance of that. We have tremendously good partners. We will invest in additional partners as we go forward and understand what those new requirements might be. So we're excited with the partners that we have. We're excited about developing and expanding those partnerships. One thing we'd like to talk about here is TQRDC, which is something probably binded by HP, I think, way back in the day, technology, quality, responsiveness, delivery and cost. Again, we want to have sort of a common measuring stick for our suppliers, but also something -- a platform to talk to them about the expectations. We do have a diverse supply base. Some suppliers are exactly what we need them to be. Other suppliers are maybe not yet there, and they're more used to serving an R&D market. If we can talk to them in this language, and we are already beginning to do that, we can align on expectations in terms of what we both need to do to be successful. We hope what helps us, helps them. I mean that's the partnership that we want to have, and those are the kind of suppliers that we want to have. So with that in mind, again, both the planning and management tools, the integration of those tools, the sharing of key data with our suppliers are all things that we're doing as well in terms of adding the right fit layers of business process to that. So with that, I will stop and give you an opportunity to ask any questions.

Just a question about sort of sourcing and supply chain, especially on the optics side. Today, a lot of stuff is sort of off the shelf. But as you try to miniaturize, it sounds like there may be opportunities to either go to platforms like silicon photonics. I mean how are you thinking about sourcing those kinds of components, will you have to do more design in-house as you look to venturize or...

So we're exploring all options. I mean that -- my background in telecom, that's kind of the industry that I come from. And so I'm familiar with that kind of partnership with suppliers. We have suppliers that we've engaged today that we're actively talking to about doing those things. Size, weight and power will matter in the future in addition to cost. And so partnering with a supplier that can road map with us that we can even share our road maps and do perhaps joint development with is high on our list. And we definitely are currently engaged with suppliers that can provide that capability for us.

How do you think about just as you design the systems, are there subcomponents that you may be able to descend out and build elsewhere and then put those together and assemble or do you -- maybe envision building everything together at this facility?

I guess what I think is for a while integrating things here is probably something we need to plan to do. As far as the components themselves, I mean, we do have suppliers that we get custom components from, things that are designed by us and there now. Putting successive levels of integration at a supplier will make sense at a certain volume point at a certain, I guess, cost point and those kinds of things. So it depends on also where it is kind of in our hardware stack. If it's something that's very close to us, we'll probably keep more of it in-house. If it's something we feel we can comfortably outsource to a contract manufacturer, box build or somebody vertically integrated to do both, that's absolutely something we'll explore.

I'll just kind of -- this is Dean. I'll just leverage kind of what Dave indicated, there are areas that we have to vertically integrate. Our trap, for example, is one of those, right? There's a lot of co-design that is required of our trap and our optical subsystems. And so, those 2 are done in-house. And while we don't do kind of the low-level component fabrication right, that we send that out to fabs and other things. I mean you saw kind of some of the heterogeneous packaging that we do in-house. right? And so other items, we do not do sheet metal bending here, right? And so we have that all done out of house. But it's basically Dave's point, it's going to be partly scale. Part of it will be those things that we do need to have tight coupled co-design or basically things that we need to kind of drive vertical integration on.

Okay. No further questions, we will take about a 10-minute break before our final session of the day. [Break]

Hi, everybody. Welcome back for our final session of the Analyst Day. I realized I forgot to introduce myself at the beginning. My name is Thomas Kramer. I'm the Chief Financial Officer at IonQ and it is great to be here. It is fun, it's hard and it's invigorating. When I was listening to Margaret's story about putting up the first web page for the GAP in 1995, I was reminded that back in 1999, I co-founded an Internet company, an online registration company. We didn't check the statistics at that point, roughly 20% in the United States had e-mail. And we had just started a company to let people register over the web by using e-mail -- sending them e-mail. And that ties back to, yes, it will be really late for us to start a quantum company after you've reached whether you call it quantum supremacy or commercial supremacy, it's too late to start it after it's done. That little events company has now gone public twice and then taken private twice a couple of months ago, last time I backdown for almost $5 billion. And so vision is what's required when you start companies. Management is what's required when you manage companies. And we have a good group here. We're having fun. And I'll get into the material in a little bit. Jordan?

Good afternoon. My name is Jordan Shapiro, and I am IonQ's Vice President of Financial Planning and Analysis and Head of Investor Relations. I've been at IonQ for about 3 years, but prior to joining the company, I was an investor in the company at New Enterprise Associates, or NEA, one of the world's largest and most active VC funds. And so I've had the privilege of being along for the journey since some of the earlier stages of IonQ. And as the company was preparing to go public, I saw the opportunity to hop in and continue contributing to this incredible mission that we have here today. Like Thomas mentioned, it's a blast. We are continuing to have fun every day here, building the company.

Absolutely. In fact, I come from 2 decades of software background in SaaS. This is easily the hardest thing I've ever done also easily the most fun. And what we're going to go through now is stuff that you already know because you read our financials, but I needed to have a speaking part, so here we go. It's important for us to remember that we have several ways of going to market. This is an important money model. It's also important to understand our business model. This slide -- this exact slide was part of our pipe deck from when went public. The icons might have changed. Actually, I think they're the same -- colors have changed. But we haven't changed our business model. That's pretty unique when the industry didn't exist at the time. But the -- Margaret talked a lot about our cloud partners, and we started by going to AWS and being listed there. So we have revenue that comes through our cloud partners. These tend to be smaller, and there's less revenue. It's the smallest of our revenue groups. Today, we don't break out the segments, but it's a good way for people to get their first entry into quantum and anybody could just go to AWS or Google or Microsoft Azure and play around. In fact, you all should migrate to it. I'm actually impressed by that. So good stuff now. The largest part of our customers actually come directly to us, and they buy compute access, which here is called preferred compute agreements. However, most of them also by application co-development services or professional services, if you will. Because today, there isn't enough of a professional quantum development. Development society and people out there who can do it. So people come to us. We help them. Sometimes we help them get educated enough to do it themselves. And sometimes, we take their domain expertise and we translate into quantum and we help build algorithms. This is very successful. This is very sticky. People love doing it that way. And it's great way for us to get customer success and happiness up. We talked about when we went public that one day, sometime in the future, people are going to want to buy these machines. Like it just feels like it's going to happen. And we said it really fast. We didn't linger on it and people didn't believe us. We weren't sure, we believed in ourselves. We knew what happened, we just didn't know the time line. Then at the beginning of last year, we said, "Well, we kind of have to tell you that this is going to happen because we're seeing too much of it. It's just people talking. There's no contract, but we are seeing sustained interest from multiple parties." And so we started alerting the market to, yes, hardware will be sold. Quantum will be run on-prem. Last year, we got the first taste for that by selling a customized quantum computer to the Air Force Research Labs in Rome, New York. This was a computer, they're using to experiment with quantum networking. In reality, it was a quantum computer, it was a harmony in class computer. So we could have called that as a system sale. We didn't, because it wasn't intended as such and also it wasn't setting the right price marker in the market. And so we -- but we had a big sale and we announced it, and we were happy about it. A few months ago, we announced the sale to the Swiss Group Quantum Basel, where we not only get a sale in territory in Europe, we get to keep part of that capacity and use that as a base for our European operations. So this is a monumental development for us, and it pretty much marks the next stage of us going to market. So what is the market? We have 3 distinct groups that we think about. There's government, commercial, sometimes called enterprise and academic. Academic sometimes is followed as government but oftentimes, they're also separate. We had anticipated that government, defense and academic would actually dominate the market because with every revolutionary new technology like the Internet or computers or lasers, the government has paid a very dominant role. The reality is that for us, the largest sale that we've ever made was made to a commercial entity. That's Quantum Basel. And so we are already seeing that this development is moving really, really fast in terms of how you see technology adoption in markets and how they sell. The systems hardware options, I want to clarify something that Peter said on our last earnings call, when he said we're seeing a lot of interest, not just in systems, but in system used particularly for networking. And we have today 2 partners that we're doing this with. There's AFRL, Air Force Research Lab. And also, we're doing some work with UMD to capture a [indiscernible], which is -- I've been told the makings of quantum networking, but you shouldn't ask a CFO for engineering details. But we're seeing a lot of people having an interest in this, and we're talking to lots of interested parties enough so that, yes, there's money in it and there's near-term money that we've already proven. In the future, this could be its own revenue line because everybody is going to need networking. And we will be here for when they do. So how does this translate into revenue recognition? That's often the question that I get like the night before somebody is publishing something. But the easiest way to think about it is access agreements. They are essentially the same as SaaS. You just stretch it out between the service time in the contract. The -- there is a similar version of that, which we call service period-based. I will get back to it, not only because it's so hard to actually pronounce, but because it's easier to understand if I go by the way of percentage completion. And we arrived at percentage completion because of the final one, which we're not even doing yet. Revenue recognition for Best Buy when you sell a computer is just when you get the cash, you recognize it. At some point, when we have standardized quantum computers and we're selling them like hotcakes, we will do that, too. In between, like the work we're doing for the Air Force Research Lab, and we're customizing a computer, it is customized to a degree. So if they had just walked away right before we delivered it, we couldn't just give it to another customer. That means that you can't do upon delivery, also means you can't straight line it over the service period, you do percentage of complete the effort it takes to actually create it. We anticipate that we're going to have several of those sales, and we will indicate when we do so that you know how to think about the revenue recognition period. But essentially, it's just if there's customization, probably it's going to be a percentage of complete. Now we're going to go back to the service period-based. This is what we're doing for Quantum Basel. Quantum Basel has bought not just one, but 2 generations of quantum hardware from us. And the first one is expected to be delivered towards the tail end of next year. This is the AQ35 machine. Now, you would have -- you could have thought that well, we're building it, customizing a little bit, and therefore, we essentially complete after long discussions with our accounting fairies. They told us that, well, since you're bringing it back is because we're retaining portions of that capacity, the revenue from that machine will be the period that is in-service in Switzerland. Once it's up and running, till is decommissioned, but it will be straight-lined between those 2 time lines or dates. Same thing for the second computer, which is AQ64. In addition -- and this is what makes it a little murkier, of course, is that there is an access agreement so that Quantum Basel can use our machines here today while they're waiting for the machine that will be on-prem in Switzerland. That access agreement stretches the entire 5 years of the contract, so that when there is a maintenance window in Switzerland, that can use that same access line. That makes it a little bit more complex, but not insurmountable. And of course, we will get back to the details on the Q4 call in terms of rev rec for next year. So that's the -- that's how we think of revenue recognition. There is also a model which is usage based. That is not a dominant revenue factor for us. So we are not seeing like wild swings because somebody ran a lot of algorithms in 1 quarter, though it could happen, but it's not like how I would do most of my modeling. Gross margin.

So Thomas had the exciting job of talking about revenue. I will share a few notes on the other side, let's just talk -- to talk about margin and inventory and how we're thinking about building up and our cost centers. So on a margin basis, the first thing to understand is that like Thomas mentioned, we have a number of revenue streams. And we don't break out our revenue streams by segment today. And it's important to note that the industry is still developing. So we're still learning about our pipeline and learning about market demand in each of those segments, and they also have their own margins, whether that be us selling hardware or selling services or access to systems that contributes a different margin profile to the company. And you'll see that margin profile reflected here over the last years and year-to-date in 2023. The other thing that is important is that while the industry is in flux, we can continue to evolve our pricing based on our customers' needs. And Thomas mentioned that our pricing, for example, for AFRL might be different than how we set our price point over time as the exact nature of the system sales we deliver changes. That is also true for margin. We have this opportunity today with a healthy margin to consider where it makes sense to trade off market versus -- on margin versus market penetration. And we always like to say, for people in the room, if you want to buy 2 systems or is a special deal for you today. And we mean that in the sense that there are ways that we can consider our margin profile as it contributes to continuing to distribute quantum into the market today. Lastly, what we'll say is, this current evolving nature of margins, we expect will continue to stabilize as the industry itself matures and those revenue streams become more clear. So stay tuned for updates there. Next, I wanted to talk about an important element of our financial structure today that is relatively new, which is that IonQ is continuing to build up inventory. So there was a question asked earlier on our tour of how we think about building systems more quickly, especially with the supply chain. We like to say that most things that we build at IonQ, you can build the components that you buy off of amazon.com, that's not exactly the case. They, of course, include some specialized components and even common components like chips today sometimes take some time to arrive at a customer like IonQ. And so to that end, to start thinking about resiliency in our supply chain and in part, what Dave was mentioning in terms of preparing us to manufacture, we started building up inventory at IonQ. That allows us to mitigate risk in delivering systems to customers over time, really important as we see that demand increasing and customers wanting their systems sooner. And then also allows us to be more serviceable and to improve uptime. So if we have replacement components, for example, let's say, an optic needs to be swapped with a quantum computer, having that in-house through our inventory gives us more flexibility to serve the customer, serve cloud customers, et cetera. Lastly, we think about this from an agile budgeting perspective, in terms of making sure that we have flexibility in our budget to increase inventory if needed, if customer demand picks up, and that is important to us as the industry is still maturing, and we're getting better sense of pipeline for system sales, et cetera. What you see here on the right side of the slide is just a little bit of how we think about the inputs for inventory. There's a feedback cycle here. To simplify the equation, we look at what our forecast is on demand, i.e., what the market is asking of us. And what we have in-house, what our current inventory is and what we need to build. And that feeds into signals that far off and tell us what to buy. And then we continue to look at what our upcoming needs are and what we have in-house in a cyclical manner to build out our inventory.

Actually, I remember when I just started and one of our engineers came to me and said I need this machine. And the name was this long, and I couldn't tell what it did. It turns out what it does is, it measures surfaces at a microscopic level to see if there are any bumps in it. And then it moves itself over so that you can stitch together and get the entire surface in 1 image. And it was, I don't know, $120,000. That's an expensive machine. Can you just take the camera and move it over my hand. It turns out you can, but the reason what made me think of it was that, yes, we'd like to say we buy it off Amazon. But while we don't necessarily go to Amazon, a lot of the stuff is just from catalogs, from vendors and like, yes, we make this and you take one. And after I started, like, yes, we still have to get the best machines. We have to get what the engineers want where we can actually be more systematic about it and how we ask for discounts, how we group our orders so that we can get volume. And also, as Dave talked about strategic sourcing, knowing that our vendors will exist tomorrow. That the products will not break. And if they do, there's a plan for how to replace whatever it was that broke. This is a sign of a maturing organization. And it turns out that you can do this, you can get better outsourcing, you have things more easily available for lower cost. All you need to do is have somebody who focuses on it. And this is how we're professionalizing the entire organization. I obviously know a lot more about what we do in the financial world. That's why I'm -- I would bore you with it. But the exciting thing to talk about is our cash balance, which I think it is great. I mean it's good to have cash. Cash is king. But it's not necessary that the cash -- having the cash is important. It is not to need to go out and look for more cash. The fact that we have a good balance means that we're not all the time worried about fundraising. And in fact, that's the whole reason we went public was so that we would not be on the road every 12 to 18 months. That has worked well for us, and it enables us to -- at the same time as we are doing R&D on our next generation and the generation following that and the generation following that, we're doing that today. Simultaneously, we're also building out a facility that can manufacture these things to an exacting standard that means it will work in our customers' offices, not just here. And yes, we were asked about the M&A opportunities in the market. We evaluate a lot of companies. And the measure of how successful your M&A operation it isn't necessarily what you buy, but the things you don't buy. We've been very successful at not buying much so far. We've made 1 acquisition, and we made that after very, very careful consideration of how that matched with our needs. But the reality is that when you have a cash pile that means that, well, we will consider it as it comes along. We're not an M&A machine, we're not financial engineers but we're doing it carefully. One thing that I wanted to point out is that there is a function of us having gone public as a leaseback is that there are warrants out there. The unfortunate effect of warrants is that because they are priced to market every quarter, when our stock price goes up, which generally investors think of that's a good thing, the cost of warrants increase, and so we will have a higher loss that period. Conversely, if our stock price goes down, we might actually have a gain 1 quarter. I'm just asking you to take this into consideration when you're modeling. And it's also why we're focusing on adjusted EBITDA as a key measure right now instead of EPS or net income. This, you've all seen, it's there in case you want to look at it and have asked questions, and we're open for questions.

So as we kind of think about your different revenue streams, is there a way that we can disaggregate where your revenue is coming from today, maybe from the actual compute relative to maybe services or hardware. Just trying to get a sense of the revenue power of the systems today and kind of how that can change over time?

Absolutely. Because we have given a lot of visibility into our sales, you can actually find out a lot by reading our press releases and 8-Ks. And Also, it just is intuitive that if you sell a system for multiple tens of millions of dollars, which is the price tag on these things, system sales is going to be a major part of our top line. And to the extent that we managed -- get enough of them out there, we're getting enough people developing on them. Now over time, software will eat the world, also in quantum. But for the near to medium term, we will be making a lot of our revenue from systems. And then we will prepare ourselves for making the best software available and there will be revenue streams in the future from that, too. But we don't have those now. We have professional services, though and that -- there is an appetite for that. People are very happy when they get to work with our developers.

I'll just add a little bit to that, that sometime in the future that when we talk about applications and such. One would hope that sometime in the future, we're in logistics and batteries and drug discovery, maybe not in a direct way. We might not be actually producing batteries, but we're helping do the design of those batteries and getting some sort of royalty as people produce us. And the same thing for drug discovery and many other things. So it's -- I would think that if you would ask 10, 15 years from now, if we were -- if our only source of revenue was system sales, we somehow failed that we need to, at some point, be more than just the system sales or just running on the quantum computers themselves.

Thomas, I'll ask you a question that was submitted on the line, which is, how do you think about our cash position as that pairs to hitting AQ64 and IonQ entering the enterprise grid era?

So that's actually an easy question because we have visibility to 64, right? And the hard question would be like, what's your cash position in 2040? No idea. The reality though is that when you get to 2040, there's a reason you got there and so revenue streams will be available. But right now, getting to 64 does not require any natural acts. In fact, our road map as it currently stated, we should be able to get to cash flow profitability with the cash we have on hand. And so we will use some of that, yes. We will hire people. We will hire more people on the engineering and production side of the house than we will on the G&A and sales side of the house. We will probably also hire more people in the sales and marketing side of the house and the rest of the G&A because we don't have a lot of those already. And also I'm a cheap guy, and I don't want to have a lot of overhead. Because every time I spend too much on overhead, I can hire fewer engineers. And that means that the road map is -- takes incrementally longer. And this is about -- like you know how to say it's a marathon, not a race. It's both. This is just the longest race ever, and we're going to keep running really fast.

Yes. I guess 1 of the things Synnex bring up with regards to you guys is the related party transactions with University of Maryland. Maybe you could just give a chance to talk to that a little bit? And how sustainable is that? It looks like it's very profitable. Can you speak to that relationship?

Absolutely. So the related party transaction we have -- so we don't technically from a GAAP perspective, we don't have a related party transaction with UMD because it doesn't qualify in a GAAP. We keep listing them because we are not hiding the fact that one of our great partners is UMD, and we made a deal with UMD and Duke to give them 0.5% of the company so that we could get all of their patents pertaining to IonQ Quantum technology. Every few years or so, we renegotiate for future patents. But if you look at companies that have been spun out of academia, this is significantly cheaper in terms of the cost to the company than any other major deal out there. So that was just a good transaction. In reality, the biggest transaction we did was that we got our 2 co-founders from UMD and Duke. And these institutions produce a lot of our new hires as well. The fact is that since UMD produces so many computer scientists, they need access to quantum computing and that we sell them at an arm's length at market prices, which are well established across all of our other customers as well.

I was just going to add on the deals with the 2 universities is one, which is on -- we don't pay anything in terms of royalty back to the universities. It was a onetime transaction, which was an equity deal. And that's the part that makes it so unusual.

Okay. I think we're done for the day. I want to thank you all for coming today and also for those people who are listening online, hopefully, you found this interesting and worthwhile of your time. We understand everyone is busy, so we really do appreciate your time and coming here. And we will look to do another one next year, although next year, I think we'll do it in maybe the January time frame. And in Washington state at our Basel location. And so you get a chance to see the Seattle weather during the winter time. So just bring your rain coats with you. And so thanks, everyone. Really you appreciate it today. And with that, we will sign off.

Thank you, everybody.

Thank you.