 Hi, everyone. My name is Robert Hackett, and I'm a senior writer and tech editor at Fortune. Today we're going to be talking about some sciencey stuff. This is an issue briefing hosted by the World Economic Forum that's themed What's Next for Quantum Computing. We've assembled some leading experts here to talk about that with us. Here today are Dario Gill, the head of IBM Research, the unit that cooks up Big Blue's most cutting-edge tech. We have Freika Hyman, who is the founding director at Quantum Delta NL, which stands for Netherlands, and she is helping to set the national quantum policy for the Netherlands. We also have John Preskel, a Caltech professor who is one of the world's leading quantum scientists. He's also advising Amazon on its quantum strategy. And finally, we have Jeremy Juergens, the managing director of the World Economic Forum's Center for the Fourth Industrial Revolution, which looks at all the ways tech is reshaping our world. Let's dive right in. I'm going to go out on a limb and assume that most people tuning in here are familiar with computers. They likely own their own and are probably using one to watch this session right now. But today we're going to be talking about a different kind of computer, a quantum computer. John, I'd love if you could kick us off by telling us how is a quantum computer different from a regular computer? Well, when many particles interact with one another, according to the principal's quantum mechanics, then it turns out it's extraordinarily complex to describe what those particles are doing using ordinary language or our classical computers. And so if we can control how those particles interact, then we should be able to perform information processing tests that wouldn't otherwise be possible. We still have a limited understanding, though, of what are the application areas in which quantum computers will have a big advantage. As far as we can currently foresee, the most important application areas will be understanding how matter behaves with applications to chemistry and the discovery of materials and so on. And that will benefit society eventually in many ways through advances in medicine and energy production and other areas. But it's still highly uncertain how long it's going to take to scale up the quantum computing technology we have now so that we can run those applications. It's not that we understand all the science now and we just need to put the resources together to do the engineering. We're going to need a lot of innovation to get to quantum computers that can solve those very impactful problems. That's a great point. There is going to be a lot of innovation that's involved, a lot of hard work, a lot of engineering, a lot of science breakthroughs to come. Let's pivot to Dario, given the fact that you are so avidly working on building some of these machines of the future. Dario, where do we stand right now with the state of quantum computing? What is available and where are we headed next? One way to look at the current situation is you have to think about how to execute a roadmap from current capability to the very future that John was speaking about. What we have witnessed about five years ago, we were able to build a small quantum computer and make it universally available through the cloud. That kicked off a journey where we started exposing to a growing community around the world these concepts of quantum information science and quantum information processing and ways with which we could interact with this novel form of computation and bring a community along. Where have we come in the last five years? Since we put that first system up, we built over 30 different kinds of quantum computers that we've made available. Over 20 of them are available today if you go to IBM Quantum. Just to give you a sense of how the community has grown, close to 2 billion quantum circuits are run every single day. These are the instruction sets that allows us to create the superposition interference and entanglement steps that happen inside the quantum computer. As a consequence of all of this, what we're starting to see is a broad set of institutions getting interested on the topic, starting to form small teams to learn what the use case is. Maybe they're develop skills, get access to their technology. The philosophy I think we have is we've got to deliver every year better and better quantum computers and quantum systems that ultimately arise, I promise, but I think it's very important to bring people along in the journey. Rather than saying it, one day it'll be a magical computer that does all these things is let's come together and work through it together as we go and execute every year. You mentioned this roadmap and I'd imagine there is a lot of planning that needs to go into this, into mapping out the timeline over many, many years, but maybe you can tell us what that roadmap looks like and where you hope to get in, say, 3, 5, 10 years from now. There are different ways to build quantum computers in IBM. We use superconducting technology. At present, if you look at from a number of qubit perspective, the larger systems that we deploy are 65 qubits. This year, we're going to deliver the first machine that breaks the 100 qubit barrier and then in 2023, I mean 2022, we're going to have a system with over 400 and by 2023, we will build a machine with over 1,000 qubits and summarizing just saying the number of qubits, but behind the scenes, there's just so many things that you've got to get right in terms of the quality of the qubit, the control electronics, how you program the environments and so on. But yes, we think 2023, we will build a machine with over 1,000 qubits and then that very importantly, the dimension that is important after that to build machines with tens of thousands or even hundreds of thousands of qubits, is that you build a scalable module or architecture that allows you to continue to scale, but it gives you a sense of how much thing we're progressing, right? Five years ago, five qubits by 2023, a machine with over 1,000. So over 1,000 qubits and IBM has actually been one of the leaders in the industry who has been pushing to move away from kind of measuring these machines based on qubits entirely and to use other sorts of metrics that factor in their error rates because these machines tend to have a lot of errors involved. And so I wonder how error prone the machines will be that you mentioned will have 1,000 qubits in 2023. Yeah, that's exactly right. So when you build one of these systems, the connectivity and the errors are present on the qubits limit the performance, how long you can run the calculation and the fidelity that you can achieve with that. So there's metrics like we've introduced in the past, like quantum volume, right, that give you an expression of the kind of performance and capability of the machines. And that one is indeed related to their rate. The speed of the quantum processor is extremely important, right? To run any kind of practical applications, you often have to run hundreds of thousands, if not a billion quantum circuits repeatedly. So that's one of the reasons why we like superconducting technology so much compared to, let's say, IONS as an example because it does offer superior speed performance. But you're right. So is the capacity of the machine, how many circuits you can run, the quality with which you can run these circuits, all of those are extraordinarily important. And just qubits alone doesn't tell you the power of our quantum computer. Got it. And we'll get into that a little bit more. But I want to pass the ball over to Freca, because we've talked a little bit about the roadmap for achieving usable, useful quantum computers within a big company context. But you are actually helping set the national agenda for a whole country. So tell us what sorts of factors come into play when you are setting the national strategy for a country to get into quantum computing and to lead? Yeah, what we are looking at is not only the technological roadmap, but an ecosystem approach. So we are also looking at how do you get the right talent? How do you educate the right people to move into this field? How do you make sure new companies can start? So from the lab, in the academic labs, there's also the opportunity to start a company. There's enough capital there to invest in these startups, to attract also companies, for instance, like IBM, Amazon or Microsoft, to invest in Europe to be part of the ecosystem as well. Because we believe, and it was also already mentioned, we still have a long road ahead of us. And it's just still time to make sure a lot of science is done, innovation is done, and that needs a broad ecosystem to develop. And Netherlands is a small country, but we are also working very closely in the European context. For instance, we just signed an MOU with the French, because also in France there's a national strategy. And yeah, we just would like to see as much openness in the ecosystem as possible, so that people can connect, build new technology together. And you said you signed an agreement with the French government, an MOU. What is that? And what does that mean? An MOU is a memorandum of understanding, sort of an intention. The French also, just like us, presented the national agenda a year ago. They also want to invest a big chunk of government funding into this field, and to make sure that it's not the French doing this and the Netherlands is doing that, and there's no way to connect to each other, but our labs can work together. We have signed this memorandum of understanding and also to make sure it's not only high level policy or politics, it's also about concrete actions we launched job boards. So there's a website where you can find all the jobs that are in the Netherlands and in France, all the companies and universities if you want to be active in this challenging field. So you're cooperating with various governments. How cooperative do you find this field of quantum computing to be? How cooperative versus competitive is it? Yeah, this is a very almost philosophical question, I would say. It's competitive. Science is competitive and also, of course, companies are competitive, but there's also because it's still in its early days and it's still a lot about science and ideas and trying new things. There's also a lot of collaboration and exchange of people. It's still a rather small field. People know each other. Some people who graduated maybe at John Preskell's lab now are in a big company elsewhere. So it's also there's a lot of exchange of people and ideas. And to me, yeah, but that's my first, I strongly believe in collaboration. So my pitch is to work more together and break down barriers between institutions. I'll turn it over to Jeremy in just a second, but while we're on the subject of cooperative versus collaborative or versus competitive, I figured I'd also ask John, as somebody who recently joined Amazon and as somebody who spent a long, distinguished tenured career as a scientist as well, whether you are encountering any tensions when it comes to what remains proprietary within a big company and the sort of open publishing atmosphere that governs science. Well, I think we all have the same goals for the time being the companies and in academia. We all want to get closer to the day when we can run applications which will deliver value to customers. And it's a long road to get there. As came up in Dario's remarks, we have quantum computers now with over 60 qubits, but they're rather poor quality qubits. When we perform operations on pairs of the qubits, they often make errors. Nearly one time out of 100, there's an error, and that's a severe limitation on the size of the applications we can run for now. So we're all looking forward to much better performance, which we're hoping to achieve both through improvement in the hardware and through software methods for mitigating the imperfections of the hardware. And we'll probably have to do both. And success in that direction is going to benefit everybody in the industry and in quantum science. So I think we're all rooting for one another in that sense. We all want to see those innovations happen, and that'll bring us closer to the day when quantum computing can have a broad impact on humanity, and that's what we all want to see. So I think there is from that point of view a kind of spirit of cooperation, at least for now, within the industry and within the broader quantum community. Dario, would you agree with that? Completely. I think one of the things that unites all of us is we want to see a quantum industry thrive and be successful. We each place our bets, form our teams, and push our boundaries, and push our frontiers forward. But we're absolutely united in the appetite to, hey, make a convincing argument, which we've done in the past, in different governments to invest in quantum information science. We saw the example in the United States of the National Quantum Initiative. We're just talking examples of what's happening in Europe. You see it in Japan and so on, and we advocate for those investments. That's a good thing, right? An exciting endeavor. You train students, you do fantastic research. And then we're also seeing within a more sort of commercial setting, lots of companies big and small, you know, including venture capital, placing bets and taking risks and exploring new opportunities. And I do think that there's a cooperative atmosphere from the perspective of wishing the industry success and growing the industry. Now, we also compete, as we should, on the specific approaches that we take within it, what kinds of machines we do it, like how you do it, like an ecosystem around it. But I would still say it's a very sort of, like, positive environment where we join forces to create a new industry. Frankly, what's exciting about it, this hasn't happened in many decades, right? A whole entire new approach to computation at this scale. And I think we're all feel excited about this historical moment that we're witnessing. Let's drill down on that historical moment. I'm going to get Jeremy involved now. Jeremy, given your role sitting within the World Economic Forum, you are studying all of these big tech changes and how they're transforming the world in which we live, maybe you could contextualize for us what is happening within quantum computing, why it's so exciting, how that kind of fits with some of the other big trends out there, you know, whether it's, you know, happenings in cybersecurity or digital currency and everything in between. Great. Thank you. Thank you for that, Robert. No, this is extremely exciting. And I have to say, among all the different technologies we cover, quantum is definitely in the top three. And, you know, a number of the contributors here today showcased the extent to which the road ahead of us is uncertain. You know, and we're in an era of a lot of quantum hype. There's a lot of money in the market these days chasing different promises and contributions here. But what we see is that the journey will take us longer. And I believe it's Roy Amara who said, you know, we have a tendency to overestimate the effects of technology in the near term and underestimate them in the longer term. And if you think about quantum in this context and you think back to, you know, the last major paradigm shifts in computing, we're talking about room-sized computers. And no one would ever imagine that you would be having a supercomputer in your pocket, as it may be. Now, you know, I'm not sure how close we are to having supercomputers, quantum computers that have to be near absolute zero carried around our pockets there. But it is hard for us to understand how this will play out. And that's one of the reasons why we need to bring all the actors together here. In this end, we've established the quantum computing network. And so we're happy to have the participation of Amazon, of IBM, of Microsoft, a number of academic institutions and also national governments all working with us to explore what are the implications here. And it won't be done on technology alone, right? The technology is absolutely critically important. There's different, you know, beliefs on how that might evolve, what may emerge there. Even people looking at doing quantum computing on a chip. So there's a lot of different approaches. We're not here to try and pick the winners. What we are here to try and do is understand what are the skills we need? How do we make sure that the benefits from quantum can be extended across society and that they're being put to the most significant applications? And also how do we manage to secure the transition there, right? And in this sense, most of the computing infrastructures that we have today are not quantum secure. The roadmap of when, you know, you'll be in a situation that you might be able to break some of the current encryption with a quantum computer is unknown, but that day is coming, right? So we need to both harness the opportunities in front of us, whether it's in new materials, in health care or other domains, even as we also manage the risks and prepare society more broadly for the significance of this transition ahead of us. Jeremy, you said that you would rank quantum computing in the top three of big tech changes going on right now. What are the other two just for our... Yeah, you know, personally, I'm quite keen on synthetic biology and CRISPR. You know, CRISPR is less than 10 years old today, and yet we already see synthetic biology is even newer. And then I think everything around decarbonization and what we have to do to be able to sequester carbon is going to be a new emerging critical area that we're still in the early stages of, but will have profound impacts on our ability to adapt to the climate challenges. Do you expect quantum computing to have applications in those two other domains you cited? Definitely. John highlighted early on, you know, some of the applications in chemistry. So to be able to look at a range of compounds, assess their suitability in a much more rapid manner, and also to be able to bring forward new materials, right? So those materials might be used in, for example, renewable sector, might be applied in a desalination of water. So there's any number of different domains that we'd be able to basically accelerate our understanding of the, say, physical systems with which we interoperate every day. Got it. John, when you mentioned potential applications for quantum computers, you seem to linger on the scientific pursuits that are almost basic research and not even necessarily making, crossing the chasm over to having useful applications in industry. I wonder whether you believe there is potential utility there actually within big commercial operations, or if you're just being cautious in your framing of this development. Well, there are a couple of aspects to this question. One is how hopeful should we be about using quantum hardware in the relatively near term for running applications of commercial interest? The quantum computers we have now, as already mentioned, are quite limited, but they're sufficiently powerful now that they can perform certain tasks which are beyond the reach of our most powerful existing supercomputer. So we can start to explore whether they can run interesting applications. And people will be doing that. It's important to do it. We have heuristic ideas about applications that would be of interest to customers, but no particularly persuasive argument that those are going to pan out in the near term. In the longer term, I think the applications are still largely unknown. As one example, there's a lot of interest for obvious reasons in the impact quantum computing will have on artificial intelligence and machine learning because we see machine learning already having a big impact on society. And I think that is an exciting potential area. But as of now, we really don't have any fully worked out examples where quantum computing will greatly improve artificial intelligence tasks of the type that we're running now. I think probably there will be a rich set of applications coming out of quantum machine learning as an example eventually, but they'll probably be very different from applications of machine learning we see now. We probably won't use quantum computers to drive our cars or to play go. We'll use them in new ways that maybe we haven't even anticipated yet. Though the most obvious area for quantum machine learning is the types of problems that you would call basic research understanding how quantum matter behaves. But those are problems which will eventually deliver economic impact, but maybe in the longer term. So I think it's really important to understand that we have very limited ability at this stage to imagine the applications of quantum computing down the road. In the near term, I don't think it's particularly likely that we'll see a lot of economic impact from running applications on quantum computing, quantum computers in the long run. We will and there'll probably be applications that we haven't clearly understood at this stage. Got it. Dario, what applications excite you most that you are hoping come to fruition? Yeah, I mean, I would say the taxonomy first and foremost is, as John correctly alluded, is the application of quantum to modeling nature on all its form, applications, chemistry, and materials. So what you're seeing in there, for example, in the IBM quantum network, we have about 115 institutions who are part of the network. And you see large industrial companies, I mean, in addition to universities and national labs, but you will see large industrial companies like, you know, Daimler and Exxon and many others who look into the opportunities to develop team skills, methodologies, and approaches to be able to do modeling that they do today with high performance computing that they do today with generative models in AI, and begin to understand what algorithms will be used in that space. So you see that's category number one, right? And, and I think that will extend to the lab sciences, even though those are more complex molecules, etc. A second category is things that have to do with like linear algebra, things of this sort. So an underlying fundamental application on that is of course, you know, can you apply this to things like machine learning, right, as an example. And I think there's been exciting recent work on quantum kernels and things, you know, that are beginning to provide at least a hint of the direction that we may go with practical applications. But as John alluded, you know, there's ways to go and create a lot of creativity to happen there. And the third is around the area of like quantum walks, right, like traversing graphs or, you know, be able to, to, you know, explore things that have like a network, sort of like topology and to what degree quantum algorithms help us. And I put them sort of in order of like hierarchy, like a lot more clarity on the chemistry material science base, interesting and fascinating exploration machine learning, and the third one, which has to compete obviously with the classical inclementations. So those are three areas that I think are the most exciting. And in terms of what industries I'm seeing like really engaging, I would say industrials. And the other one is financial sector. So, you know, when I look at Morgan Chase, Goldman Sachs, many others are, you know, part of our network, and they are very interested because their relationship between computation and their business is quite clear, and they're willing to invest ahead to have enough skills so that when it comes to age, it is ready. So you mentioned investments. Freyka, you are helping to identify and foster sort of a startup ecosystem around quantum technology. What aspects of the technology are you looking at? What sorts of businesses are you most intrigued by and interested in supporting? Yeah, well, we look at the entire set, but from the public side, the most interest is going into the hardware side of things, so the deep tech, because there the capital needs, the investment needs of these startups is fairly high. So in Europe, we have to do a bit of effort to make sure that there's enough venture capital available to fund these startups. And it's really exciting to see that that's really happening now. So we are, for instance, in the Netherlands, we now have around 10 startups, which is fairly small, but we aim to have 100 in the end of the program in seven years. And yeah, we try to attract the venture capital also from the US, but also from Europe with public funds, and then match it in co-funding constructions with private funds. And you see it happening now, and it's really great to see that these young people who have really a tech background also getting interested in entrepreneurship and building a business and getting business cases. And that's, you know, it's a different ball game, but yeah, you see that happening now. It's great. What is the biggest threat that you might identify to quantum computing? You mean the impact of quantum computing? What could be a threat? Or the threat, well, for me, the biggest threat is that governments and companies build walls to protect their IT and their people, and that we get in some sort of lock-in, and that the talent and that the knowledge, et cetera, doesn't flow. Because I really think it's important in the years to come that we need this flow of ideas, knowledge, and capital throughout, at least I would say, the Western world as we call it, because we should work together to make sure that the promise that this technology holds will be used for the good of this planet. Got it, yeah. And that is what I meant. I'll phrase it better in passing it over to John, which is what is the biggest threat to progress in quantum computing to actually advancing this technology forward? Well, I'm not sure I'd call it a threat, but I think it's important to emphasize that we're still in very early days when it comes to building quantum hardware. There's been a lot of progress, but it's important for now to continue to explore a wide variety of ways of encoding and processing quantum information. We have a few technologies now which are the most advanced, like using Adams's qubits or superconducting circuits, but those like all approaches face big challenges in scaling up to the applications that will really be broadly beneficial. So it's important for us as a field and as an industry to keep focused on the long-term goals, because we're going to need much, much better hardware than we have, and we don't know exactly how that's going to be realized. And let's not become overly focused on what we can do in the next few years as opposed to reaching the potential of the field in the longer term. I think we can all appreciate that call for sort of diversification of avenues and pursuits of research. But I wonder if you had to place all your chips on the table on a particular line of inquiry or technology, what would you go for? That's a question for me, I guess. Well, I don't really know, nobody really knows what will be the best approach in the long run. For now, I do like superconducting circuits, but I think we need to think more broadly about how to make use of superconducting technology in quantum computing. We've been using pretty much the same qubit design for 15 years now, what people call a transmon, and it's continued to improve and IBM has been responsible for some of those advances. They've got pretty good transmons now, but the transmon might not be the future of superconducting quantum computing because it has intrinsic limitations. So we need to be thinking about if we're going to use superconducting circuits, different ways of encoding and processing quantum information using those technological tools. Dario, I ought to give you an opportunity to respond, given that IBM is heavily invested in superconducting qubit research. Yeah, well that is our bet. I mean, obviously within a research organization, we also collaborate with universities and others to explore different alternatives and make sure that we're always grounded and that we continue to validate what is the best path forward. And we support very strongly that there's alternative approaches and that people invest. But our bet is on superconducting technology and we don't see any reason. We see lots of things we don't know, but we don't see any reason why we cannot build extraordinarily sophisticated quantum computers using this technology. And we have a very strong roadmap that we've laid out and that we continue to execute on that. But I mean advances on that is going to take everything from fundamental material sciences, the devices themselves, the fabrication techniques, how you couple the qubits, the packaging technology, the control electronics, the cryogenics, the software stack. So there's a lot of emphasis. Sometimes we talk about like one piece of it, but we build quantum computers and a computer is a system. And you got to bring all of these pieces together. You got to enable that they work. Now we achieve over 99% uptime reliability on this system, which is kind of remarkable. I mean, people look in the field 10, 15 years ago, people struggle to maintain that level of control. You have to have two or three grad students attached to which experiment almost. And the fact that you can build machines with that level of sustainability right now is remarkable. So yeah, lots of challenges that we got to execute on the roadmap. But I look at the parallel just to close on semiconductors too. And in semiconductors, it was a very interesting sort of parallel in that we had lots and lots of challenges in imagining how we will go and keep scaling things down. But it is the collective mobilization of R&D and the collaboration and competition of a huge ecosystem that allocated you know, about $100 billion a year in R&D that overcomes those problems and challenges. And I think that that's what we need in quantum. We need enough scale that talented people are overcoming all of these challenges. Got it. Jeremy, I want to ask you as well what the biggest obstacle is to achieving progress in quantum computing before ending things off with a sort of rapid fire Q&A with the full panel before we close as we are nearing the end of our time here today. So Jeremy, tell us what is the biggest obstacle to progress. Great. Thank you. I think we need patience on this one. And Dario used the analogy of semiconductors. We can also look to AI where there was the AI winter that you know people speak of in the 1980s. And you know, the deep learning models that are you know, say we're so familiar with today that have scaled up. Those weren't necessarily popular at the time, right? So we don't necessarily know the model today. These things will take patience. You know, the journey is several years out. The other analogy I'd borrow from AI is that many of our deep learning models were built on what I would call fundamentally flawed data sets, right? The models inherited the biases of the data sets. And to that end, we need to make sure that when we do design these ecosystems, they're not solely technical, but they involve people from academia, they involve, you know, potential end users, and that we have an inclusive ecosystem here. And in that way, I think we can avoid, you know, only betting on singular paths, but also having the patience to see something through that serve the benefit to the wider society at large. Excellent. So before we close out, I want to make sure to just hear from each of our panelists one last time. And the question I'd like answered is, provide us a prediction, something quantifiable, something you can put a number to, whether it's a year or some other sort of measurement aspect about what is going to happen in the field of quantum computing in the years ahead. Some milestone reached perhaps, you know, a next great big challenge to overcome. Jeremy, let's start with you. Great. Well, I'm in the tough spot here along with some of these deep quantum computing experts, but, you know, maybe being the optimist and after preaching for patients earlier, I suggest that we'll see corporations running quantum applications in parallel to their existing computing systems by 2025 that they'll actively be using them to be able to understand what that growth potential is, what some of the trade-offs are. So actually, I see that happening by 2025, you know, leveraging the resources that are available by then. That's right around the corner. John, let's hear from you. What predictions can you share about where you think quantum computing is headed and what might happen in the years ahead? Well, from the perspective of quantum science, we're at kind of a pivotal stage because we now have these quantum platforms that allow us to study the behavior of many in a regime that has never been experimentally accessible before. And I think that opens new opportunities for scientific discovery. So it's my expectation that on a time scale of about five years, we will be using these quantum platforms to give us new insights into the quantum physics of many interacting particles by doing experiments that were never possible before. Now you were a particle physicist before you started to get more into quantum science and became one of the leading lights in that field. Is there a particular set of experiments or domain in which you think that quantum will realize some new fundamental understanding of the universe? Oh, well, I'm interpreting that as a question about the longer term. And I do have what you could call a dream, which is that with quantum technology, we will be able to do experiments which will give us insights into the quantum structure of space time itself. One of the areas which I've been interested in scientifically is quantum gravity where quantum physics and gravitational physics meet. This is a notoriously hard area to study in the laboratory. But I think with simulations run on quantum computers, we'll be able to deepen our understanding of that physics. And I think that's an exciting prospect. Excellent. I think we're all hoping for that outcome as well. Fraka, give us a prediction or milestone. I think in, or I hope, but I think I expect in 2025 we're past the time of believing and indicating when and time past. But then there is a fact there's an actual application of a quantum computer that is really faster in real life than any supercomputer and has commercial or practical value. So we're past the realm of guessing and believing. It's a fact. Excellent. Sounds like 2025 is a popular year. I'm going to mark my calendar. Dario, tell us as well, give us something measurable, some kind of quantifiable prediction or milestone you expect to be achieved in the coming years. Well, what we want to deliver is between this generation of the 100 plus 400 and 1000 qubit, which happens within the next three years. I'm going to go a little bit before 2025. That with those three generations of systems, we demonstrate a quantum advantage for some application that has a path to practical use. So that would be a good goal. And then I'll give a long-term goal. We just passed a milestone where a trillion circuits, quantum circuits have been running our machines since the last five years. By 2030, we want that number to be a trillion quantum circuits a day that gets run. And those are going to be the primitives that are going to be embedded behind everyday software for classes of problems that quantum are well suited. An exponential growth in terms of adoption over a decade from a trillion over five years to a trillion a day. Wow, a trillion a day. That is a mind-boggling number. Thank you all to my panelists. To hit on some of the themes we touched on today, it sounds like everybody is banking on a collaborative and open paradigm for quantum computing research in the years ahead. Everybody is hoping that it doesn't get too competitive and proprietary with people keeping their information siloed. And just an incredibly exciting array of potential applications that this new breakthrough technology could help with. Yeah, it is fascinating. It's super exciting to watch. And I cannot thank my panelists enough for sharing all their expertise and insights today. Thank you all for being here.