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[00:00:38] Speaker B: Hi, this is Nidhi Arora, vice president at Fernway Group. Welcome to another episode of Fernway Insights podcast. Our guest today is Professor Christopher Monroe, an international leader in quantum computing and co founder and chief scientist at IMQ. Professor Monroe founded IMQ in 2015 together with Jungsheng Kim, based on 25 years of pioneering research on trapped atomic ion based quantum computers. By going public in October 2021, IQ became the first pure play quantum computing company to do so. Mister Monroe is also a professor of ECE and physics at Duke University. He is an atomic physicist and quantum engineer specializing in the isolation of individual atoms for applications in quantum information science at National Institute of Standards and Technology. In the 1990s, Professor Monroe co led the team that demonstrated the first quantum logic gate. Professor Monroe is also a member of the National Academy of Sciences and is one of the key architects of the US National Quantum Initiative, passed by Congress in 2018.
Professor, welcome to our podcast. Very delighted to have you here today and talk about quantum computing. And I and Q thank you, Nidhi.
[00:02:00] Speaker A: It's a pleasure. Pleasure to be here. I look forward to this discussion.
[00:02:04] Speaker B: So let's jump straight into it and start with talking about the current state of play in the space of quantum computing. So, as the quantum experts and players in the industry like to say, we are in the NISQ erade, which is noisy, intermediate scale quantum error. For the benefit of our audience, tell us a bit about what is this NISQ era and what does it mean in terms of quantum capabilities?
[00:02:28] Speaker A: Yeah, that's a very good question. Quantum computing is a quite revolutionary way to compute at a very fundamental level. So there are some definitions, I guess we have to get through one of the main differences between quantum computing and conventional computing. What we're used to is that conventional computers are not noisy. They might be too slow for a certain problem, but the error rate is very low. And this is, of course, due to many decades of wonderful engineering, building much more reliable components and so forth. But that wasn't always the case, even for conventional computing. Early computers were noisy. The vacuum tubes would break in old technologies. The NISC era means that we have noisy, intermediate scale quantum computers. Quantum computers are much more noise prone than conventional computers, and this stems from the fundamental nature of quantum information. They can only work as long as they're not looked at, as long as they're that they need to be very well isolated. So any perturbation from the outside world can tend to make errors. That doesn't mean all is lost. It just means that the error rate has to be low enough so that you can execute your program before errors will start to take hold. You may have to run many times and take an average of your results. So what differentiates the NIST era from a future era is similar to what happened in conventional computers. The errors will get so low that we can do something called error correction. We can actually, using software, make sure that the errors are seen corrected, and we can go on about our computation. That requires a lot more resources. We're not there yet. It requires much lower errors to begin with. We're not there yet. We often consider these early stages to be something different than conventional computing when we're almost entirely limited by the noise in the device.
[00:04:23] Speaker B: So, in that case, Chris, how far do you think we are through commercialization of quantum computing? And what types of applications and use cases will we see in the commercial era of quantum computing?
[00:04:36] Speaker A: So how long is it before we have commercial use cases? To have value from quantum computing doesn't necessarily mean that we have to have fully fault tolerant computers beyond the NISC era. So even though we have noisy machines, they may be good enough to do tasks that we can't do otherwise. And a little bit of terminology here. We often measure the size of our quantum computer in terms of how many bits it can store. They're called quantum bits, or qubits, and they're very powerful because they can store multiple values at the same time. Of course, a single bit, if you can store both zero and one in some superposition, that that's pretty small. You can't do anything with just one bit. But if you have, if you put many quantum bits together, the number of possibilities scales exponentially with the number of bits. With ten quantum bits, you can store 1000 numbers at the same time. Okay, still, that's very small. With 20, it's a million. With 100 quantum bits, that's two to the power 100 values you can store at the same time. That's more than all of the hard drives, all of the memory in the world. With 300 qubits, that's more than the number, the possibilities, the configurations of 300 qubits is more than the number of atoms in the universe. So things blow up very fast due to this exponential scaling. So, fortunately, we think when we get to about 75 or 100 quantum bits that are good enough, that have low errors, we can do things that we could never do with classical computers. This is actually tantalizingly close to being realized.
Right now. We're at a level of 20 to 30 quantum bits, and we and others have many strategies to scale up to that number. We don't need a whole lot of qubits to make it interesting now. They have to be good qubits.
As you build your system bigger, you have to maintain a low error rate, as we talked about before. So that's going to happen fairly soon, I think. I mean, we're talking number of years, not ten plus.
[00:06:44] Speaker B: Interesting. And when that happens, Chris, which industries do you think will benefit the most across, like, different types of applications? Is there any sense on the magnitude of value unlock that we could do for these industries?
[00:07:00] Speaker A: Right. Yeah. You asked about applications. Where will it hit? Good. So this is what makes some people uneasy in this field, because we. I have to say, we don't know exactly what the first killer application will be in quantum computing. We have very strong hints. So where we look for are problems that conventional computers are not very good at, problems that require lots of configuration. An example I like to use is a famous math problem called the traveling salesman problem. This is a logistic problem, and it goes like this. If you imagine having to visit all of the state capitals in the United States, say just in the lower 48 states, all of 48 state capitals, exactly once, and you want to minimize the total path traveled, there are actually a lot of possibilities, a lot of combinations. 48 is still on the borderline of being interesting. But when you get to 100, 100 cities, the number of configurations, you can't store all those. There's not enough memory, really, in all of the computers in the world to store that. So problems like that, where you have lots of configurations, but there's one answer, or there's, you know, you're desiring one outcome. Types of problems like that are very likely amenable to quantum computing. The one word I would use to describe these problems is optimizers, optimization problems, all optimization problems, they have complex inputs, many configurations, but one output. One example is modeling the weather. Again, I don't know specifically how quantum computer can do this, but we have models of climate that are very complicated. We can't solve them. Quantum computers might give us the ability to take better guesses at the models that are behind something like the upper atmospheric chemistry. Financial models is another one, actually. At IonQ, this is surprisingly taking a life of its own. Financial models are very complicated because they have many variables. There's thousands of financial indicators throughout the world that can drive the price of the Dow Jones index, for instance. And if we had a model that could at least more accurately predict it, it would be very useful, and quantum computers might be able to do that. Nobody thinks a quantum computer can find the optimal, the best solution, but it still might be better than what we could do using conventional computers. So that would be very interesting. One final example. It doesn't sound like an optimization, but this comes from physics and chemistry itself. When you take a complex molecule, there are many electrons on that molecule. Those electrons actually do their own optimization automatically. The electrons find out where they need to be. They minimize their energy, is what they're doing, and we sort of understand the models of how that works, but we can't solve those models once again, because there's too many configurations. Quantum computers have been already put to the task of simulating very small molecules. And unfortunately, these molecules are so small that we already know the answer. So it's not adding value yet, but solving a problem in a different way like this will allow us, when we scale our systems, to be bigger computers, we'll be able to solve bigger molecules. That's very promising. And, of course, it hits the oil and gas sector, the energy sector, the big pharmaceuticals, from drug delivery to drug design. Very interested in molecular simulation. There's a lot of computing power right now that goes into molecular simulation. If we can displace that or improve it using quantum computers, that will be a big deal.
[00:10:31] Speaker B: The term quantum advantage is also used a lot, right, in quantum research and literature these days, and we haven't touched upon that yet. What does that mean? And also, how do we think about it in context of commercialization or, like, the different stages of quantum development?
[00:10:50] Speaker A: So, quantum advantage, sometimes it's called quantum supremacy is a little bit of an academic term. It means that you have, if your quantum computer has shown a provable quantum advantage, it means that it speaks to the complexity of the problem you're solving. It could take a problem that's normally exponentially scaling with size and make it polynomially scaling. What that means, that's a lot of words. That just means that it makes a problem, an entire class of problems, easier to solve, like the traveling salesman problem I mentioned. This is a very hard problem. It's an exponential problem. It gets roughly twice as hard every time you add a city. If you can make it, if you can change the complexity of that problem to make it easy. Then you can prove you have quantum advantage. The thing is, I tend not to get too caught up in the academic definitions of these things, because the real use cases of a quantum computer will probably not have proofs.
It will not come with a sheet of paper saying, we prove this does better. You don't need proofs if you're doing an optimization problem.
If you are optimizing a function and you get a better answer, you can check, you can simply look at the big computer you used to use and show, did I get a lower energy of this molecule than I had there? If I did, then it worked. I don't need any proofs. So the good news is, for commercial use, we don't care about proofs. And this confounds academics. And quantum computing, for better or for worse, has big academic sort of community around it, and they like proofs. But when you use computers, you don't need proofs. You just need to show by testing directly that it works better. It doesn't have to be the best solution. It just has to be better than what's out there.
[00:12:33] Speaker B: Interesting. And so, professor, as we understand, like, like you're also saying, right, like, right now, quantum computing is a nascent technology, and still, if not like decades, but a few years out from outperforming supercomputers, for the lack of a better word. What do you think is the bottleneck here, if there is any, right? Like, is it the hardware or the software and algorithms?
[00:12:59] Speaker A: Yeah, that's an interesting question. If you build a computer out of hardware as a device, it has software. We have people working on that. I wouldn't call that the bottleneck. The bottleneck is a little hard to pin down, because quantum, and I would say it this way, quantum computing is so different than classical computing. It requires a different thought process on what a computation is. And frankly, we don't have all that many people thinking really hard about how to write software for real machines. And then there are those that have hard problems that might not know or care about quantum physics at all, which is fine, but we need to sort of triangulate between all three of these groups. The quantum software engineers, the hardware engineers. That's sort of where I am, and then the application specialists that know, that know the structure of the problem that is interesting for them to solve that they can't do on regular computers. So getting these three communities together is really hard because they come from totally different worlds. And so we're learning. I mean, it's been quantum computing really has only been out there for about 25 years, and the communities are starting to. Starting to interact. What's notable is the last five or ten years, industry has played a major role. And this is a great thing, not only because we can really have engineering, but industry is what connects with commercial problem. Academics alone very useful for doing foundational research, research into components. But it's industry that's really going to show the way, I think, in the future. And so I think the challenge, it's not really a scientific challenge. It's more of a social challenge to get people to think about the quantum way of computing. If they have a hard problem, they should think in the back of their minds, well, what's the quantum way? And let's kind of think about this. So it's very hard to predict when, you know, when we're going to strike gold in this field. But once it happens to, I think, then that's going to lower the bar for everybody to jump in, and that's going to be very interesting. So I would say the bottleneck right now is just more social. It's just getting these ideas out there.
[00:15:08] Speaker B: And speaking of the quantum communities, Professor Monroe, now that you brought it up, what we are also seeing in the quantum space is an emergence of multiple full stack players.
They have either done it organically, so, for example, the likes of IAM Cubes and Righetti, or there are some who are also doing it inorganically. Last few weeks, we. A few weeks ago, we heard about the merger of Pascal and QNCo, and one of the motivations there was to become an end to end or a full stack company in quantum computing space. What do you think is driving that? Why are players increasingly looking to become full stack players?
[00:15:47] Speaker A: And the last question I should have said, I don't want to minimize the difficulty of building quantum computing hardware. I hinted earlier that qubits are shy. They're very fragile, and engineering a big system is very hard. There's a lot of engineering that has to happen, but this is really starting to happen already. But it leads into your current question, which is building the hardware for quantum computing is very expensive, very difficult. There are many different technologies right now. It's a friendly competition. I mean, we're all, I think everybody in the field, you know, wants the field to grow at the same time. We compete. We're using different hardwares. But most of the companies in quantum computing have been not hardware, but more on the algorithms, applications and software side, which is, it's good to have we need that community as well, as I mentioned. But I think in these early stages of quantum computing, a software company really needs, really needs to co design the hardware based on their software. What I mean is that the software and hardware can't be separate. Now, we're not used to that in conventional computing. We have totally different hardware, like Motorola chips or intel chips, Apple and the PC, but they can run the same software. So the software, in a sense, the hardware is so good, it's a commodity that we can have entire software companies that don't really care about the hardware. We're not there in quantum computing. It's going to take a long time to get there. So right now, I think the more adept software companies are starting to latch onto hardware. I think Pascal and Q and Co. Is the perfect example of that, as you called it, full stack. That means the application layer, the software layer, then the controls layer, and then the quantum hardware layer. And a successful full stack company will allow the users at the very top of the stack to not care what's at the bottom. That will be success. Sort of like how we use cell phones today. Nobody knows. I guess you could know what's inside, but nobody cares. You don't have to. That's a good thing. And so having a full stack approach is really, as you say, that's really what's happening, and it's a good thing. I think we need to. In these early stages, it's very important to be able to co design the hardware to the software and the other way around, take this. To write better software, you need to look on the native expression that the hardware can do. There are quantum gate sets that certain hardware has other hardwares don't. And you should write your software around that to compress it, make it run better, run larger problems that will allow us to get to quantum advantage sooner.
[00:18:26] Speaker B: Professor Moron. Now let's shift gears a little bit and talk about your journey. You started your career as an atomic physicist and then spent years harnessing the power of quantum physics. How and when exactly did you get the motivation for IMQ?
[00:18:42] Speaker A: So indeed, I started about 25, 30 years ago in the field of atomic physics. I was actually working for the us government at NIST in Boulder, Colorado. I was working on atomic clocks. So we were the research arm of the atomic clock division of the United States. Well, we were playing. We were doing fundamental physics with individual atoms, and we were connecting them together in a very funny way that makes the clocks run faster with less noise. It was a very academic thing, but this was sort of an academic minded group, and that was with David Weinland, who later got the Nobel Prize for partly based on this work. Well, what we were doing, we were entangling these atoms to make the clocks, and it turns out what we were actually doing, we now call. We were making a quantum logic gate in a tiny little quantum computer made of atoms before we even knew what a quantum computer was. So in the mid nineties, this field fell in our laps when it became clear that quantum computing can do things, certain tasks that classical computers wouldn't be able to do. So I have to say, at the time, being very research minded, it was an amazing times. We were so far ahead of everybody in the world, five or ten years ahead of everybody. So we were publishing like mad, as an academic would do, but in the back of our minds, and I think I could speak for Dave Weiland as well, we were thinking about, well, how do you make it really big? I mean, a few qubits is cute, but how do you make a real computer? After all, our processors have billions of transistors on them. How can we think about scaling to something useful? And then we started thinking about, this is not really physics anymore. It's more about, how do you build a big system and maintain its coherence? And I will say, the one thing that's interesting is, back then, we certainly thought that, well, these are great demonstrations, but the real thing will come from solid state. There will be some solid state platform, just like, I mean, we've been born and raised on solid state transistors. A very large scale integration. VlSI has been amazing, allowing Moore's law to happen and giving us all the wonderful information processes we have. So it made sense that, well, if it's a computer, it's going to be solid state. That's how I thought 30 years ago. I don't think that way now. And it's a little bit subtle. I think there are a lot of solid state computer players out there. It turns out ensuring that a solid state system behaves quantum and is isolated turns out to be really hard. And I think there's a lot of research that has to happen before that's even possible. We work with individual atoms, which might sound researchy and exotic, but the atoms are pure. They're atomic clocks. They're absolutely identical. They're in a vacuum chamber. They're not part of a solid. They're suspended. And we poke at them with laser beams. Again, that sounds researchy, but the performance speaks for itself. These things perform and they can scale because they're perfectly replicable. So over the last ten or 15 years, it became clear that we probably can scale, and I don't know of any other technology that can scale. And so it was natural to think about an industrial approach to doing this scaling, which is based more on engineering than science. The science of the atom is done. We're not going to manufacture a better atom. There's no manufacturing or yield issue. The yield is perfect. These atoms are atomically perfect. It's a question of controlling them, levitating them, quietly being able to poke at them with laser beams in a very precise way. There's a lot of optical engineering, but this is all engineering, and this is something that I think in industry is much better set up to tackle. And so, yeah, five years ago, with my colleague Jung Sang Kim, who's more of an engineer than I am, but he's interested in these atomic systems, we pulled the trigger, and due to a champion in the venture capital world who approached us and convinced was just bugging us, you have to make a company. It's time to do it. With him on board, it made it very easy to start to think about that. And that was six years ago.
[00:22:37] Speaker B: And when you got there six years ago with Chung Shaq Kim, there were also other industry giants, including IBM and Google, that had also started pursuing quantum computing technologies. But theirs was based on a different technology, superconducting qubits. Now, IMQ is the first one pursuing the trapped ion system. Tell us what your thought process was at that time when you went down this path of trapped ions.
[00:23:04] Speaker A: Yeah, I would say with these very big companies putting big investments in a different technology at the same time, it was a little bit scary. But also, it emboldened us because the technology that these big companies were developing, these super reducting qubits, they couldn't touch the performance of our atoms. I mean, it was just obvious. It was very clear. I know they have infinite money, and that's the scary part, but they, and I understand the motivation. These are players in the conventional computer game, where we use solid state components. And so it makes sense to think about building a quantum computer based on those same components. But I think we're very thankful that these big companies made the investment, because this made it much more interesting for us because we have the performance. And on the other hand, we have a higher bar because there's no history of using individual atoms in a vacuum chamber poked with laser beams. But I would say to that, that quantum computing is so radically different. Than classical computing. Why would we ever think that a quantum computer will look anything like a classical computer? So I'm actually nothing. Not too concerned about that. So I'm thankful that there's a big community of technologies out there, because our system does outperform everything else out there. So, in a sense, it really emboldened us.
[00:24:26] Speaker B: And professor now, with IaMQ's IPO last year, it's one of the major news in the quantum computing circle. Right. When you started the company six years ago, did you think that you would go public in six years?
[00:24:41] Speaker A: When you say we started the company six years ago, I think eight years ago, we were thinking the company might start in 2020, something like that. So everything was accelerated, everything was faster. Going public was, again, our CEO, Peter Chapman, and he met through our investors, Niccolo Damasi and Harry Yu, who had this spac going on.
It took about a year to get it going, but, yes, it happened much faster than we would have imagined.
[00:25:13] Speaker B: Now, that's obviously the biggest fruit of many years of your pioneering research in quantum physics. But tell us also a bit more about some of your other achievements. Professor.
[00:25:25] Speaker A: As I mentioned earlier, I started as more of an academic. I still am an academic at Duke University, but I'm an atomic physicist, and I work with optics and lasers. In fact, I used to spend most of my time in the lab, just lining up a laser, tuning it up. And I'm not saying it was fun, but we had to do this because we needed a source of light that was reliable, that we could use for our experiments. And the one thing I've learned at INQ and building our systems, even at the university, is that you don't have to spend all your time aligning a laser if it's well engineered. If it's well engineered, you can just turn it on and it works all day. We're sort of moving. What's interesting is we're moving beyond physics now. We're using these lasers to do physics. And the one thing that really excites me on the scientific side is that we're building computers based on lasers and optics and atomic physics, and we're applying these computers to do physics problems of a different sort. So we're. You know, I don't want to say I'm leaving physics, but we're doing physics at a much higher level. So I'm learning a lot about things from metagenomics, how you map the DNA sequencing problem to a graph problem that maybe you can solve on a quantum computer. I'm learning about solid state phase transitions that you can simulate on a bunch of qubits, learning about other optimization problems. It's like I'm a student again, and that makes it very fun. So if you say my other interests as a scientist, it's just a very broad range of activities that we can do in this field. And again, that makes it fun. It's not just physics. It's not just chemistry or engineering or computer science. It's all of those things. And I think one thing we're learning here at Duke University is that we're sort of opening the door for every walk of scientific life to get involved, not even just science, but sociology networks, how interactions happen in social and otherwise. I mean, quantum computing is a new way to think about maybe solving problems that will hit all these areas.
[00:27:25] Speaker B: That's very inspiring, professor, and especially for many budding scientists, I'm sure. So let's switch topics again here, professor. So at funway, we continue to see a big divide between technology providers and then the end customers. For example, like in this case, there are a lot of enterprises who may have the budget to invest in quantum technologies, but they're still taking a wait and watch approach. Right? In your view, how should these enterprises or end customers be thinking about adoption, which could also help the commercial efforts in the quantum computing space?
[00:28:05] Speaker A: We need these people. If they have problems that are vexing, that are very hard, we need them to somehow reach out. Now, maybe they don't have to make a large in house investment. They certainly don't have to build a machine they can consult with, maybe a quantum software concern, or they can use available small quantum computers that are on the cloud, available through AWS or the usual cloud, GCP or Microsoft Azure. You can actually get access to IQ computers and other computers. Quantum computers, they're small, but you can maybe start to think about running very small versions of these programs. But even that's a big investment.
It really takes a full timer to start thinking about applying these problems to your, to apply quantum computing to your problem. So I think the first step would be for these companies to sort of reach out to other companies and just say, you know, what's going on out there, do some exploration.
Nothing is going to be free. There will be some amount of investment. But I don't think you have to make a big jump and say, okay, we're going to throw 10 million per year into this. I don't think it has to be that right now. Waiting and seeing is a little bit passive. I think these companies should be a little more defensive and maybe start think about working with some of these companies, maybe the software companies, but as YZ pointed out, they're now becoming more full stack. But even our company, we have a big software team. We have an applications team. We can't afford to run 100 applications to do R and D on 100 applications, but we can do a few. And if this one, if there's a sector of the economy where they have a very interesting problem that maybe quantum computing shows signs of solving, they should reach out to us or other full stack companies.
[00:29:49] Speaker B: And going back to the quantum computing landscape, we are seeing some m and a activity out there. Right. We had Honeywell Quantum Solutions and Cambridge Quantum computing merging last year, in November 2021. And we also spoke about the Pascal and Q and core merger already, which just got announced a few weeks ago. What is your expectation in terms of consolidation in this space going forward?
[00:30:17] Speaker A: Well, I think the obvious one is already happening. It's the software hardware consolidation. That's a natural one. I think it befits both communities to do that. I mean, at INQ, we have our software team, but we're very, you know, we're very open to that as well. I think there probably will be further consolidation even on the hardware side. As time goes on, there will be certain hardware modalities that need to be, there needs to be more research on them so that maybe they go more to the universities or department of Energy labs, you know, national laboratories, government funding. And so I think some of these hardware plays will either just stop or go to more researchy sides of things. And there probably will be some consolidation on the hardware side, but that will be less prevalent than the hardware, software consolidation. There are a lot of companies out there, so I think there's going to be a lot in the next five years, there's going to be a lot of consolidation. It seems a little crowded out there right now.
[00:31:11] Speaker B: While we expect consolidation to continue, there's also, like almost kind of a space race out there, right, to build a scalable quantum computer. Now, like you said, that we have multiple hardware technologies out there. We have super conducting qubits, we have drab dimes, neutral atoms, silicon photonics and so on and so forth. Right? Any thoughts on who would emerge as the winning technology or winning company, so to say, and why?
[00:31:39] Speaker A: Well, you know how I'm going to answer that. I wouldn't be in the field of these trapped ion quantum computers if I didn't believe it was going, it was going to win. And for the reasons I mentioned earlier, I think the solid state. Sometimes we call them synthetic qubits platforms, and so these are man made qubits, and each one is a little different, and there's a yield issue, and there needs to be a lot more research in these systems. There's some very promising ideas on how these systems can be so well engineered that the errors are not only low, but they can do error correction sort of implicitly. That's really interesting research right now. Nobody has shown that kind of behavior in a real system yet, but the component research should keep going for sure. But I think in terms of building systems, the natural qubits, like atoms or photons or electrons, neutral atoms, trapped ions, these are particles that are given to us by nature. They're already quantum comes down to a question of control, and that's an engineering challenge. And for that reason, I think those technologies are going to be the winners, at least in the short run. I think you never know. There could be a breakthrough in some material that just magically has kind of the properties we need. But until that breakthrough happens, I think the natural qubit systems are going to be the winners. Trapped ions, charged atoms versus neutral atoms, they are very similar at a high level. The neutral atom systems, they're very hard to control. I mean, they don't have a charge, so it's hard to get them where you need, whereas the charge systems, we can use electric fields to push them exactly where we need. They stay there all day and so forth, so they're much more controllable, and so they're more mature. Right now, individual photons is an interesting platform that will be used, even with atoms, in order to be a conduit to transfer information off chip, like a data center. We'll use single photons, but there are platforms that use just photons and no atoms. It's interesting. It's very difficult because there's no memory, and we have a good history of trying to build computers without memory failing. So not having a memory means it's very difficult. And photons travel so fast that it's hard to keep them around. So it seems like you do need photons coupled with some type of memory, which will probably be atom. So I think we're in a very good situation in atomic physics and atomic systems as being the short term winner in quantum computing. Now, 30 years from now, maybe there's going to be some exotic material that, you know, where. Where we. Where we see some interesting behavior. One other aspect of these synthetic solid state qubits is that what's exotic about them is they have to be kept cold, I mean really cold, nearly zero degrees, near absolute zero. Whereas the atomic systems, because they're in a vacuum, the system could be at room temperature. So when you scale up to do research, that's fine. We have no problem cooling a little sample down to nearly zero. But if you want to build a big system, keeping it cold, that's a serious for scaling. And that's something that I think these companies will be confronting in the next decade.
[00:34:45] Speaker B: Interesting. So, professor, we are almost towards the end of our conversation, so in closing, what I'd like to ask you is that all stakeholders will have a meaningful role to play in shaping the future of quantum computing. Weve spoken a bit about the end customers already. What are your views on the role of governments, investors and even the quantum computing companies themselves?
[00:35:09] Speaker A: Oh, yes. As I mentioned, one of the bottlenecks in quantum computing is sort of this interaction between all these different communities of researchers, scientists, engineers, applications people and others that just have, they want to solve a problem. Theyre not scientists or engineers. The us government and many other governments in the world have taken a very active role in quantum computing because of that, because it's such a sea change of the way we compute. And historically, even in the US, governments can be a good catalyst to take research out of the laboratory and get it into the commercial world through the National Science foundation, through our Department of Energy and Department of Defense laboratories, for instance, NIST I mentioned, I used to work there. They're a small agency, but they, they actually did most of the groundbreaking initial research in quantum, and they have some wonderful labs themselves. So the us government has played a very active role in spawning research programs and getting the research out of the lab and into the hands of commercial players and companies. So for their part, investors are doing well. Look, theres a purity to an investor whos just interested in their investment. If it makes sense, theyll be there. So that they obviously represent a source of initiative and even some of it is research. And so this is very important. And I think it's much more nimble than government spending in pure research in that they will go to the, they will go to the companies and performers who look most promising, and they have to make their case. Things are very fast moving there. The quantum computing companies themselves, well, I think they're, well, there's a lot of hype in the field. We don't know what type of a panacea quantum computing will be, but I do believe when it hits an application area, many other sectors of the economy will follow. Suit. But these companies can. They have to maybe put their heads up and start to do their own part in connecting to those being active and finding those that have applications and those that have hard problems. And as an academic, it can be awkward sometimes. I don't want to say to overhype things, but just to promote the idea of quantum computing for some problem that all the academics are saying, oh, it'll never work. That'll never work. That's what you're used to in academics. So the company should just not listen to that and build their stuff, build the best stuff they can and connect to customers. It's a very strange market in quantum computing. Now, we're not selling bicycles, and we can count revenue based on how many bicycles we sell. And we know how people are using those bicycles. We're selling the future. And as long as people have problems that are hard and they can map them to quantum, I think we'll be in good shape. But we have to find them. The companies themselves really have to take the initiative.
[00:38:00] Speaker B: Professor, it was a really insightful discussion. Thank you very much for being here with us today. Thank you.
[00:38:06] Speaker A: Okay, Nidi, my pleasure. Thanks again.
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