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

Okay. Great. Thanks everybody. In case you haven't seen me in the seven sessions already done today. I'm Joe Moore from Semiconductor Research at Morgan Stanley. Very happy to have the management of IonQ here, CEO, Peter Chapman; and CFO, Thomas Kramer. So guys, maybe if you could -- we could do this as a little bit of an introduction to Quantum in a couple of parts. If you could sort of start out by just talking about the promise of Quantum technology, and then I'll talk a little bit about IonQ's approach to that market, which is quite a bit different than others.

Okay. So it has been said that person who can explain Quantum Computing is worthy of a Nobel prize unto himself. You don't have to go out back and do physics and all the rest of that stuff. You just have to explain what it is. So we're probably not going to win a Nobel prize in this next 30 minutes to explain Quantum. So Quantum Computing. Originally, it started with this idea that for the natural world is -- it really didn't matter about Moore's Law which has allowed it to go for another million years that you still wouldn't be able to do computational things that were required in particular for chemistry in the natural world. And so -- and it's interesting today as a kind of deep point right now in time is we're just getting to a point where IonQ quantum computer is equaled in computational power to a single DGX 100. But the interesting thing is every time you add a qubit, it doubles the computational power required. So you add one qubit, now you need two DGXs. The problem is, is that every time you add it, it doubles the need, you add two qubits, now you need four. So right now, we're at 32 algorithmic qubits. Qubits is equal to one DGX 100 with 80 gigabytes of memory. To get to our next system, which is 64 algorithmic qubits, you need 3.5 billion DGXs to equal the computational power. You would need 3.5 billion times 80 gigabytes of memory to be able to do the matrix math. So of course -- and this was the original insight that into Quantum is there's a set of things that just don't scale well in a classical computer. We happen to be that literally today with our recent announcement of #AQ 35 is that we're finally to the point where classical computing, we're starting to leave it behind. And this was -- Fineman actually saw this back in 1981, as he said, to be able to model chemistry, even if we allowed today's classical computers and Moore's Law to go for a million years, it really wouldn't matter. You still wouldn't be able to do the kinds of things we want to be able to do in compute. So we needed a different way to build a computer. And that realization was the quantum computer. So these quantum devices, and they're not good for everything. It's a bizarre little device, it's not good at adding one plus one, but it can solve differential equations. It was like, how can that possibly be, right? So there happens to be a set of problems that quantum looks like it's going to be very good at. The chemistry is one, which is the original insight. The second one was machine learning. Everything that we've done so far in machine learning seems particularly actable. Optimization problems and maybe strong AI. So as you asked the question, what is it? It's a system which is not based on binary systems, it's based on a quantum system. It's not digital, it's not analog, it's quantum. And it just turns out the real world is that everything is based on is actually quantum, it's not digital, it's not analog. And it turns out a digital system has a hell of a time trying to simulate what a quantum system does. And interesting, we happen to be in an interesting place in terms of -- we're already at this interesting place right now for quantum simulation. But we're also in a very similar place actually for NLP, is today for -- to train a large language model is that you need -- we were told by one of our partners is you need 30,000 servers. Each one of those servers has 8 GPUs in it, so a total of 240,000 machines, and it runs for about three months for a total cost of about $1 billion in run time. So we're just getting to a point where our computational needs are really exceeding what it is that we can kind of classically build. Next year, when the next version of ChatGPT needs 10x the data, will it be $10 billion to train it? So our classical systems are not scaling well. And you even see people like Sam Altman come out and say -- and Elon who've come out and said, look, we need to have higher energy output for the planet, so we can power more data centers for classical hardware. We don't think that, that's a solution. We think quantum is actually a solution. It's not really reasonable for the classical hardware side to build 3.5 billion processors to see really one of our Quantum devices. So it's this kind of idea that maybe there's a new way to compute things that's much more efficient. It turns out that one of the -- we're talking about a single processor here versus 3.5 billion GPUs. That machine plugs into two standard wall outlets. So you can imagine what it takes to power 240,000 GPUs for three months. So this has a huge savings both in terms of the cost of the machines. I think we calculated out, what, 240,000 GPUs costs versus one of our systems. This is a hell of a lot cheaper. And certainly a hell of a lot cheaper to run. At #AQ 64, which is the system we're building right now, it's a very long answer sorry, Joe.

It's okay. Please go ahead. Makes my job easier.

At #AQ 64, it can consider in a single instruction of roughly the speed that you have of your laptop, it can consider 2 to the 64 different possibilities in parallel. So look, you're on the Internet, do a search for 2 to the 64, the answer is roughly 18 quintillion different possibilities in the single instruction. So we can explore a computational space of that size. So now if we're using numbers, you probably haven't used ever. And so let's compare that with today. Frontier, the world's largest supercomputer, which is over at Oak Ridge National Labs made up of lots of little blade servers and GPUs and all the rest. It can do 1.2 quintillion floating point operations per second. So this next chip that we're building can do 18 quintillion in a fraction of a second. So the single chip is actually more powerful than all the supercomputing devices that mankind has built so far. Another way to think of it is that if we took everyone's cell phone on the planet and we built a supercomputer that a single chip would equal all the processing power of all those cell phones. After that, we're going to go build the next chip, which is 256. So now we're getting into two to the 256. Now this is a really large number. And now we need to find kind of things, how can I even explain to you what its computational power is? At 120 algorithmic qubits, is it can consider in parallel equal to the same number as there is Atoms in the known universe, all 13.8 billion light years across. So if you were to convert all the matter in the universe, in the transistors, it still wouldn't be able to compete just for this one chip. But that's only 120. I get to be the wrong salesman. But wait, there's more. So we're talking about 256. So it's, what, 2 to the 140 more than the number of atoms in the known universe. So these devices are now going to be more powerful than all the computing devices mankind has produced and classically will produce for the next million years. And by the end of the decade, we'll have 1,000 algorithmic qubits, and suddenly, you will have 2 to the 1,000. And so you're looking at numbers now that can no longer be represented just the number itself cannot be represented in 80 gigabytes of memory as to how much more that these machines are more powerful than today's largest super computers. The multiplier by itself, just the one number can't be represented. We're quickly getting into the place, in fact, actually the largest number that you can represent on a computer has 308 digits today. And so we'll quickly be on that just in terms of be able to explain how much more powerful these machines are. Your laptop will no longer be able to calculate how much more powerful just as a single number. And so that's what it is. That's what we're doing, and that's what we're building. And you might ask yourself, what is it that you would do with these things? What's the plan? Well, it turns out that there's problems all over the place that need this kind of computational power. You actually experienced it already this morning. More than likely, you had a delivery that was made to you. And so the question is for the delivery for the logistics company, what is the optimal route to deliver? The average delivery company, the delivery driver delivers to 120 addresses. And so the question is for the 120 addresses in one day, what is the optimal delivery route? Because that's got to be an easy problem to solve because clearly, we do it every day, so it must be optimal. But it actually -- it turns out, if you remember a little bit of high school math is that, that's a factorial problem. So you take 120 minus 1 factorial, and that's a number of different ways to deliver that package. Should I go to this address first or that address first, and then where would I go after that? Well, 119 factorials is a massive number. And if you were to look at that classically and do it in parallel is that it would take a lifetime just to calculate for one delivery driver. But with Quantum, we have a quantum system big enough, you could go through and calculate that in a single instruction. So -- and it's a little bit strange because quantum doesn't give you a discrete answer, it gives you a high -- a probability. So you run it a bunch of times to be able to figure out -- you get a bell curve and you figure out basically after running it 100 times, where it appears the most at the top and that turns out to be the answer. Now I'll just point out that if you wanted to optimize for San Francisco, the delivery route for all of San Francisco, how many drivers do you think are in San Francisco, make a guess? I'll make it up. Let's assume there's 1,000 delivery people in San Francisco. So if I wanted to optimize all of San Francisco to make sure that they get the optimal routes for everyone in San Francisco, 120 addresses, 1,000 people, so that would be 120 times 1000 minus 1 factorial. What do you know that's a number which is more than the number of atoms in the known universe. So these are the kinds of problems that these things that you just think to yourself on this must have been solved. We must have figured out a way to get this to work. But it turns out it's not actually true. And these are the kinds of problems that quantum computers could be used for.

Okay. So I feel like the capability you're talking about, there's a general consensus that we'll get there that Quantum can address a lot of these things. I think what's different about you guys is you think you're going to get there a lot quicker than the scientific consensus. So can you talk about that? And you think about Quantum as like low temperature, superconducting advanced physics kind of stuff, you guys have a very different approach. So can you talk about your approach to it?

Yes. So there's many ways to build a qubit. I think there's a dozen or so right now. They generally fall into two categories. One is man-made qubits, that would be the superconducting. And the other one, which is all natural qubits. That's the eco-friendly qubits. So these are made out of things. We use individual atoms, photons kind of those kinds of things. People who are using a natural qubit have the advantage that their yields for their systems since they don't manufacture the qubit is 100%. The people who are making man-made qubits, they have a problem, which is that their qubits are not just one photon or one atom but tend to be made on a chip. And so you need those things, the yield to be really good. So there's kind of -- there's two problems. One is, how do you manufacture a qubit. People who are doing natural qubits have an advantage over the people who don't. Both systems have a problem in that qubits by their very nature, want to be isolated from the environment. So the amount of isolation you can give them controls the what we call noise in this business. And the noise determines how large a quantum circuit you can run. So I'll give an analogy to this, which is imagine if you're doing Excel, and you were going to do 2 x 2 x 2 x 2 and you're going to do that 8x. And at the end of it, you were expecting to get 256. That's what we expect on a digital computer. But in quantum, there's an error and that error compounds across the calculation. So when we do 2 x 2, we get 4, but we also get 4 plus or minus the error. So if the error is large, it compounds every time I do 2 x 2 x 2, instead of getting 256, what I get is 256 plus and minus maybe 500 or 1,000 or 10,000. And so the number that -- the answer you're getting is pure noise. How many times you can multiply that 2 x 2 x 2 is really dependent on the noise. And also the application, like, for instance, maybe if you were -- and this is not really a quantum thing, but just so you can understand it. Maybe if I was calculating sales tax, I don't really care about a little rounding error on the noise. So that would be totally okay. But if I had an application that I wanted to do 500 of those 2 x 2 x 2, even a small amount of noise because it compounds would limit the usefulness of the quantum computer. So Ion traps the technology that we have today has the best noise or as little as possible in those systems that allow us to run the largest application. And so this noise controls kind of how large quantum circuit or program that you can run. And that gives us this huge advantage. Quantum has people want to compare the kind of -- it's very common everyone wants to compare different qubit modalities and all the rest. I don't think of it as kind of comparing ion traps versus superconducting. My guess is that these things all have some useful time in the market, but it's going to be at different points. And Ion traps happen to be today and probably will dominate the market for the next 5 years. If you ask me 10 or 15 years or 20 years, I would actually choose a different modality. Like Microsoft happens to be working on topological qubits, which seems really elegant. The only problem is we haven't seen one yet. Mankind hasn't found one. It's like the old Higgs boson particle before they found that. It's very elegant but it's not here yet today. Maybe 20 years from now, that will be a great thing. And where IonQ has the advantage is that we will have the market basically to ourselves for the next several years and generating revenue and our existing cash position gives us -- so maybe 10 years from now, who knows? You might see that IonQ buys a different kind of qubit company because we think its time frame in the market is coming into me. I don't see these as competition. I just think that they all have different times where they'll mature. To take any qubit from kind of starting point to a product requires about $1 billion. So often, you see people who say, "Oh, there's been a breakthrough in the laboratory," that's on a daily basis. I see things that say, a breakthrough. But you need now $1 billion to go from that lab experiment to a product. And that's easily 5, 10 years. You're going to have to run through, what, 3, 4, 5 rounds of different series to be able to get that kind of money. And so these things just take time. So IonQ happens to be leading today.

Great. Maybe you can address a couple of recent things that people have asked about. One, in terms of the two co-founders, I mean, you've seen this company grow a lot from 60 people three years ago to over 300 now. But your two co-founders have both returned to academia still involved in the company to some degree, and I know Chris Monroe was quite prominent at the Analyst Day a couple of months ago. But can you talk about that dynamic? And why would they make that transition?

This is a juicy stuff. Taylor Swift, whether or not she's going to endorse things to be on par with our two co-founders. So the two co-founders is -- they are and originally were college professors, and one was at University of Maryland and one was at Duke University. And it's an unusual story. Actually, NEA approached the two of them to start this company. There's actually nothing about this company, which is normal. So I mean most companies start by a bunch of people going and chasing VC. We had VCs chasing to the two co-founders. And that's how the company got started. We put together a deal with the University of Maryland and with Duke where we gave them roughly about three quarters of a percent of the company in exchange for the exclusive royalty-free license to the IP that the two college professors had done in the last 15 years, and going forward, until 2025 now. And so the work that goes on over at the universities is actually significant because they get a lot of government dollars to invest in Quantum. And that IP comes to IonQ. So what happened is when we started with the IPO, it was basically just the three of us. And so we said, look, we don't want the two co-founders to come to the company because then the IP arrangement will go away with the universities. And so it's economic value to the company because right now, there's about -- I think the number is about $100 million a year, which is going into those colleges for Ion Trap technology, which we get to monetize. So I don't want that to go away. In fact, actually, they're working on things which are probably 8, 10 years away, which I'm not having to invest in because the government is kind of paying for it. So we said, Chris, you go -- unfortunately, we had University of Maryland and Duke, and we said Jungsang will come to the company, and one will have to stay and we decided he will go to Duke, and we kind of lost out on the University of Maryland thing. So he went to stay at Duke and spend his time there. And then Jungsang took a sabbatical to come here and to work with us. And then Jungsang in the very beginning was VP of Engineering because we didn't have anyone and then we hired a VP of Engineering, it was great. We got that covered, and Jungsang then went and took a VP of R&D. And then we hired a VP of R&D, and he left that position. He went to applications, and we hired somebody for that. And so he's just been kind of [indiscernible] at this point, he's been doing that for about two years now. And so now he's finally getting to a point where we've got a complete management team of kind of experienced people and is going back to being a college professor at Duke. And so there's really not much more to that story than just that. It's -- and the company still benefits from their -- the work they do over at Duke because we still have the arrangement where the IP still all comes to IonQ.

Great. That's helpful. And then I want to ask about some of your targets, and you guys have been very successful hitting the milestones in terms of the #AQ targets. But maybe just first, definitionally, can you define algorithmic qubits and how some of your competitors talk in terms of qubits and how does that compare?

Yes. Not all qubits are the same. So in a layman's term, algorithmic qubits just mean useful qubits. I can kind of explain it in a little bit more detail, is it turns out that for most algorithms that you're going to write in quantum you need in gates roughly the square of the number of qubits to be able to run an algorithm. So if you have 10 qubits, in your algorithm. If you're going to use all 10 qubits, your algorithm is roughly about 100 gates to be able to use those 10 qubits. Now that is not true for everything. It turns out there are some applications that consume gates much faster and there are some applications that don't. So this is an average. It's sitting in between. What we do is there's a benchmark put together by the QED-C, which is a consortium of quantum companies and a set of algorithms things like Monte Carlo simulation, where we run those things, and these things consume them roughly at the square of the number of qubits. So now you look at some of the competition, they might say that they have 1,000 qubits. But it turns out the error rates are so bad that only, let's say, five of those qubits would count for its algorithmic qubits even though they have 1,000 because the error rate controls how long a program you can run. So what matters is not how many qubits you have, but how many you have plus the error rate and then the best thing would be to run this benchmark that sits down and says, okay, can you run this size Monte Carlo simulation to determine? And that's what algorithmic qubits are, it's just simply a way to choose a benchmark that hopefully aligns with what customers want, which is to be able to run larger and larger quantum circuit.

Great. So #AQ 64, I mean, as you said, you hit the milestones to date, figured you're up to #AQ 36 as of the most recent quarter. You've talked about #AQ 64 at the end of 2025 which -- and that's where you sort of say, we're going to surpass the ability of classical simulation classical computing ability to solve these problems. What are the things that you still have to do to get to that milestone?

Well, so a bunch of good news. First, as it turns out that we don't think you need error correction to do it. So in these noisy qubits, one ways to get rid of the noise is to do error correction. The same error correction that you see in classical hardware. When you send a message over the Internet, we actually send more data than the data that we want to send because we recognize over the Internet that, that packet will get noise in it. And so we need to be able to correct for that noise. So it turns out memory has this, everything in classical electronics has error correction built into it. So Quantum can use error correction as well. So originally, we thought to hit #AQ 64, we were going to need roughly 16:1 error correction, meaning you're going to use 16 good qubits to be able to get one really good one. And the good news is now we think we don't need to do that. We found something in the last couple of years called error mitigation, which is in software, we can mitigate the errors. And so -- and that's good, too, because it means we can fit the #AQ 64 all on a single chip. So we don't need to span across multiple systems to be able to do it. However, to go beyond #64 to get to 256. Then at that particular point, then you're going to now need to have enough qubits to bring in error correction. And now you need to have not just one quantum computer, but multiple of them. And so one of the really cool things about Quantum is that if all of us had quantum computers, let's say we all had 64 #AQ quantum computers, we can all come together as a group and put together fiber optics between our computers, and we can make one really big quantum computer. If there was 30 people in this room, we can have 30 times 64 qubits. And the cool thing about these qubits is they do not care where the other qubit is to do computation. The qubits don't know if they're right next to each other or if they happen to be on the other side of the planet. And the other cool thing is in classical computing is when I build a supercomputer and I take two blade servers, I don't get 2x the power because the network and the overhead in the software reduces that. Maybe I get like 1.3x, 1.4x in exchange for my two. So there's this tax to be paid classically for networking. But for Quantum, there is no tax. The fact is that the communications happens through a method that we don't really understand, which is the strange entanglement and the qubits literally don't care. So what's interesting is we just announced the first networking part of the quantum to be able to network these chips together. There's kind of three steps to be able to make this happen. We just showed the first one. We just announced that. And by the end of this year, we'll have completed all three steps. So now you will have a networked set of quantum computers that will allow you to get to much larger qubits counts to be able to hit #AQ 256. The other thing, and this is true for -- these things are true for all quantum, not just IonQ is to get to much larger quantum computers that need to be built out of a lot of cheaper, smaller quantum computers. So the one place where Moore's Law does apply is actually in the cost. And so in every generation, the systems need to get smaller and cheaper because future generations of quantum computers will basically be like blade servers and you'll need to put 16 hundreds, thousands, maybe at some point, millions. And so you need the cost to be able to scale as you ramp up the number of quantum computers required and about half of IonQ is working on building more powerful quantum computers and the other half of the IonQ is working on smaller, cheaper and more manufacturable quantum computers. And IonQ is unique in that space. I think we're the only ones in the industry today who is thinking about how do you build kind of a productized quantum computer that you can easily ship and you can see this in the generations in the Forte system, which is the 35 algorithmic Qubits, it fits in 8 racks. And in the 64, the goal is to fit it into three racks and the 256, the goal is to fit it into one rack. So even though we're getting bigger and bigger, every generation gets smaller and cheaper. In fact, the 256 that fits in one rack actually has more than likely 8 Quantum computers in that one rack. So you're starting to shrink down the size. And with that, the cost of each one of the systems.

Great. Maybe you guys could talk a little bit about the financial model and to the extent maybe the different sources of revenue, talk through some of that? I know you start to move to more systems revenue at some point. Can you give us an overview there?

Yes. So if you look at kind of the breakdown. And for us, about half the revenue is government. Internationally, it seems stronger than domestically. Maybe that's the state of the U.S. politics. I'm not quite sure. And then the other half would be industry. So both seem to be quite strong compared to this time last year, our top of funnel has grown significantly. These systems are expensive which means typically that they're difficult to predict in one quarter because -- and they are often tied to other things like government cycles. So like inside the United States, most government sales really show up in Q3. And so you can kind of predict some of these things as to when they happen. Industry, typically, it's in Q4 is a pretty busy time. So different time periods of the year will be better than others. We are quickly getting to the point now, starting literally kind of this last week where we're starting to take workloads from NVIDIA. Up to this point, if you were doing simulation on our 11 cubits system, people would sit down and say to me, why do I want to use your computer because I can simulate it on my laptop, and they were right. And then when people sat down and said, #AQ 29, they said, well, I can simulate that on a single DGX GPU. Do I need to use your system? But now we're finally getting to a point where it can no longer be simulated. So that part of the market IonQ. And actually, most people don't know, but some of the larger clusters of NVIDIA's hardware happened to be sitting in data centers doing nothing but quantum simulation. And so now those people are going to be coming to IonQ to do their work, just simply because even if you have thousands of GPUs, you just can't simulate what it is that we do with one of our systems. So we're just starting to take those workloads from NVIDIA and bringing them to IonQ. The other thing is, once you get to #AQ 64, is the economics change because what it is you can do with it suddenly is interesting. In these early days, if you can simulate it on your laptop, that probably means also that there was a classical thing that you could do that was probably better than what you could do on a quantum computer. But we're getting into this really interesting area now at around #AQ 64, definitely by 256 where classical is just left behind. And so now there's -- we just simulated with 35 benzene for the first time. So we're starting to get into interesting molecules from a commercial point of view. But once you get into #AQ 64 and 256, then you get into small molecules for drug discovery. And so it's starting to be a really interesting time. And that hopefully will drive demand for systems to sit down and say, well, if you're in pharma, and you're in small molecule drugs, you need to have one of these systems to be able to simulate those molecules.

Great. So maybe we can pause there and see if there's questions from the audience?

You mentioned a few times how like IonQ is the leading player in the industry. And there's definitely a couple of different companies that all say that about themselves. Probably most notably of late Quantinuum, the spin off of Honeywell. It's taken a bunch of money from JPMorgan and others. If you wouldn't mind just comparing, contrasting your approach versus theirs. And I think they had some big announcement today about solving the wiring problem for scaling and how you think about that, both from an industry breakthrough and commercialization standpoint as well as from a competitive standpoint?

Well, first, I think it's the -- whether it's quantum or not, it's the job of every CEO to say that their company is the best. So that's probably just take that with a grain of salt. So each one of them is a little different, Quantinuum is what they do is they -- and they do this because they can't do what we do because of patents. What they do is they do quantum computing, and they do shuttling, they take ions just like us, and they bring them into a compute zone and entangle them and then shovel them away and bring in other ions. So there's a lot of shuttling going on. To actually look at what they're doing in terms of the timing and what they're spending their time. About 99% of the wall clock time is being used to shuttle back and forth. So I'm going to have to draw into an analogy, which one, hopefully, you know. If you remember back in the day when we used to have hard disks with physical platters, we would optimize the hard disks so that the head of the hard disk would not move because the physical movement of the hard disk, the -- actually it was all where all the time was. And so we used to defrag hard drives for databases, so you put everything close together so you can minimize that movement. That's kind of the difference you can kind of think of as between us and Quantinuum. They're having to spend all their time shuttling back and forth where in our case, we've got [32, now 36 qubits where no shuttling has to happen. So in terms of the amount of compute time that you get to use and ours spends most of the time, computing instead of it actually shuttling. So that's kind of the largest difference between the two. The two things are very similar in the sense that they're both ion trapped quantum computers. So maybe another way to think about it is -- and these are poor analogies, but they have a 2-bit bus, and we have a 36-bit bus. And so it's a kind of different system and has different performance between them. I don't know if that answers your question.

I apologize. I know nothing about quantum. I tried to watch that TED Talk, and I still have an idea how it works, how that computer beat the humans every time. But really simplistically, so Google TPU is the most efficient matrix multiplier semiconductor unit, but NVIDIA has the vast majority of the market because of CUDA and usability. What are the [indiscernible] with Quantum when it comes to software and usability?

It's -- software is very different. So like a GPU, if you're going from a classical system to a GPU, you have to basically rewrite your software. Quantum is no different. Where the difference is, is that you're just quickly getting to a place where you just [indiscernible] another solution. Like if you wanted to go do a large molecule, like we're right now looking at a molecule which is very commercially significant. It can't be calculated classically. The only way to do it is to use this quantum computer. So it just turns out that this molecule happens to be the basis for the drug industry. And so it's really an important molecule. We just haven't been able to do it. So there will be some amount of pain into going and writing an application in a quantum way to be able to make that to work, but there is no other solution. It's just not possible to be able to do it. It's like trying to do the simulation for #AQ 64 using a GPU. Who's going to go out and buy 3.5 billion GPUs. It just can't happen. So there's kind of -- and these's set of things, they're often in a place where there's just no way to do it. There is no way, there is no alternative.

And so just a follow-up. I mean there aren't that many people that were CUDA, but there are a lot of people that could use PyTorch. What's the equivalent analogy here?

Actually, NVIDIA is working on their version of a quantum CUDA to be able to bring it up. They were very successful with that, obviously, with GPUs. They're now doing that. We don't have a horse in that game. We start kind of from the hardware, we go up in terms of operating system into a compiler, but we don't make software tools. So that is a number of other players. NVIDIA is one, Microsoft, Google, IBM, Amazon all make tools and are all competing at that layer to control the developer kind of -- in the future, what I think is that people will get to these systems by using libraries where the abstraction level is way higher. So if you're a chemistry person, you won't need to know anything about how to program a quantum computer. You'll just have a library that you get to talk to it in chemistry terms and it figures out now how to run the thing on the quantum computer.

And how extensive do you think the compiler work will be? Will you have to redo it every time you upgrade the hardware? Or is it going to be...?

No. The -- it's interesting. The compiler as we learn more about the systems, we learn new ways to optimize the circuit. And so that happens to be a very fruitful. One of the things which is really fascinating right now is that the software side, which includes the compiler, is actually going much faster than the hardware. So these two things are going to come together. Just in the last week or two, there was an announcement by a little quantum company. They were looking at a molecule and they calculated to be able to get to the ground state energy would need 1.5 trillion gates to be able to do that. And that would require a quantum computer that's years away, but they found a way to do it with 410,000 gates. So they had a 4 million x improvement in the algorithm, which suddenly took that molecule and meet it near term. So what most people are not watching is they're not watching the improvements in terms of the software side because there's huge improvements on the algorithmic side, which is making these things come in much sooner than most people expect.

Great. Well, we're going to have to wrap it up there. Peter, Thomas, thank you so much for your time.

Thanks, Joe.

Thank you. Thanks, everyone.