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

Before we get started, I'd just like to read the research disclosure. For important disclosures, please see the Morgan Stanley research disclosure website at www.morganstanley.com/researchdisclosures. Here today at the Morgan Stanley TMT Conference, we have IonQ. To start things off, can you give the audience an introduction to the company, maybe starting with its history and then the differentiated approach the company takes at quantum computing.

Absolutely. So first-off, my name is Thomas Kramer, I'm the CFO. And Quantum computing is a kind of computing that promises to solve problems that heretofore were either impossible to solve or not possible to solve in a timeframe that makes it viable. So examples are solar cells can capture roughly like 23% of the Sun's rays and convert them into energy. A simple plants can capture roughly 80% of that energy and do it much more efficiently. So we have this answer already existing in nature, but we don't know-how to harness it. The reason is because we can't actually model on this synthesis, because the problem-set is too complex for classical computers. So that's the advent of quantum computing. Today there are 2 basic technologies, architectures that do this, there is superconducting and there is ion trapping. Superconducting is taking a classical computer like all of our laptops here, it's silicon substrate, you run some lines and power and you compute in it and wouldn't it be great if you can take those 60 years of learning and just drop-in some qubits and thereby have a quantum computer. Turns out that, that is hard because qubits are notoriously fickle. A qubit is the basic building block of a quantum computer, just like the transistor is the basic building block of a classical computer. So it's a bit our qubit. The difference is that transistor can hold the value of 0, 1 and only the value of 0 or 1. A qubit can famously hold the value of 0 and 1 at the same time, in fact, in reality holds all the values between 0 and 1 and then it has a probability distribution among them. That means that you can perform much richer calculations. The way our company got initially started the [ span ] of our company is that Chris Monroe, who is working on the world's first atomic labs, realize that this can be useful in computing. And he used that same technology that today is how we all keep time because it's very, very stable and he performed the first quantum gates and that became the advent of what we are doing. Now, our 2 founders, Chris Monroe and Jungsang Kim together have 25 years of quantum technology experience.

Great. So now on the product side, can you give us an idea of where you guys have come from as far as product road-map and then which we should expect over the next couple of years.

Absolutely. So we used to like Moore's Law of ever more computing power cheaper and today there are hundreds of thousands of transistors and all our computers, actually in your iPhone. We don't need that many qubits for a computer to actually -- a quantum computer to be actually useful. Most people consider that if you have 70 functional logical qubit, that's the equivalent compute power of today's leading supercomputers. Currently, we have the industry-leading number of qubits, useful qubits, we call them algorithmic qubits, over-time this will be known as logical qubits, but we have 25 of them functioning in 1 computer. And that is many times more than what you will see in anybody else. Our goal is to go from 25 we delivered last year in fiscal 2022 to deliver 29 algorithmic qubits by the end of fiscal 2023.

So I want to stay with algorithmic qubits for a minute. I think something IonQ does extremely well is, helps the average person understand that not all qubits are created equal. We often say that once we get to a certain qubit count we can do things like beat the world's best high-performance computer, but some of your competitors have published research saying we're at a certain count that may be surpasses that but we haven't achieved the performance. So give us a better idea of how you guys approach error correction and how seriously you guys take this idea of algorithmic qubits or qubits that are actually computationally available.

Right. So you need to have a way to compare what a quantum computer does and you're exactly right. Algorithmic qubit is that basic measurement. If you look at different quantum computers and you run very common problem sets on them and then you just add 1 more qubit at the time. After a certain point when you add more qubits, you will not get better output. In fact, you will get output that is indistinguishable from noise. And so that number of qubits before start turning into noise, that's the number of algorithm qubits that you have. And the only way to improve the output of computer is to have more algorithmic qubits. And so that is what we are doing and today we are doing that by improving the fidelity, the quality of the qubits themselves. In the future, what you will do, is you will pool many physical qubits to construct 1 logical qubit that you can compute on and that's what's referred to as error correction. However, how much error correction overhead you need, how many physical qubits you need to create one logical one is directly linked to your fidelity rate or commercially the error rates in the qubits you do have. If you have high error rates, you're going to need many, many more qubits to create one good one. We published some research, which was actually we did error correction on live quantum computer and showed that we could do a 13 to 1 overhead on to create a logical qubit, that means that with 13 physical qubits, we use 9 for the error correction part and 4 for control functions. So with 13 physical qubits, we can create one logical one. The other player who has published anything on this is Google and in various parts of their research, they say they need a 1,000 to 10,000, sometimes 100,000 to 1. That is staggering because nobody has been able to put more than 127 chips of qubits on the chip, anyway, and if you're going to get to hundreds of thousands of qubits, that means that if you pursue this path, you are years and years away from delivering a function of quantum computer of any scale.

We often hear peers saying hey, for our company error side for the industry to hit this milestone, we need this many qubits. When we hear that, we told them, stop saying that. It is not productive. Every company has their own fidelity rates of their qubits and the quality of the qubits. And so every company really has a timeline in those milestones and varies. It might take Google x number of qubits [indiscernible]. So it's not apples-to-apples.

Yes, that makes sense. And then as we think about technological breakthroughs as well, I think there's some common misconception out there that I think some people have where they say, okay, by the time I read about it in the news, maybe that there is some breakthrough in physics that's when quantum computing can finally be achieved or be go mainstream. So the way I understand is there is some engineering around substrates, there is some engineering around lasers that are commercially available and even software that helped to achieve these milestones. Can you speak to that.

Yes, so there are. We don't consider that there any breakthroughs needed in terms of delivering quantum computing. There is incremental improvement needed. And for us, it is about delivering, just gradually improving the quality of the qubit so that you can eke-out a few more logical qubits before you start using error correction. And then the next big step is to implement error correction at-scale, which we have the first step for us was to show that we can actually make it work and use, show that in the functioning computer. So we've done that. We are not willing to implement it directly in our chips right now, because the cost of doing so will be more than we can deliver by just improving the fidelity of the qubits themselves. After that, the next logical steps will be what's often referred to as photonic interconnect or connecting several chips together, so that you can network them and create a larger computer by just having several course and new machine, after which the next logical step will be to network several computers together.

Got it. And then I was thinking more so on the revenue generation customer engagement side. You guys announced your bookings number yesterday for year-end 2022. Obviously, you guys are well-ahead of schedule where you plan to be even with quantum computing sort of in its infancy. Can you talk to some of the applications you guys have addressed over the past couple of years.

Absolutely. And this is a very promising field and is very fun to see all that you get to do is even more fun to think about all the things we will get to do, of course. But one of the most exciting projects that we announced last year was work that we do with Airbus, where they wanted to figure out a way to optimally stock the cargo. So every time paying comes in, it gets loaded with stuff, not just people and how many like how many boxes can you put in there, which one should you load first and which one should you put where, in terms of weight that should be over the wings, etcetera. This is an amazingly complex optimization problem, one that has not been able to solve via classical computer today, it's very similar to the traveling salesman problem of saying how many stops can salesman make when he is out on the road or easier for most of us think about is a UPS truck, it can make 120 subs per day. That's more or less always true, FedEx, UPS, Amazon, but you think that they get into work in the morning and they just get a list of go here and this is the faster way can do it. A computer can't actually compute that on-time before all the packages that have been delivered because this is what's known as a factorial when there are 120 possible stops, all the possible combinations are 120 minus 1, so 119, 118,117. The number of possible solution is 6.6 times 10 to the power 198 or something like that, it's a vastly large number, it is larger than the age of the earth in nanoseconds. Now the cargo hold can hold many more packages than 120 and you have not only the dimensions of what goes first, you got first 3 dimensions in terms of where you put it in the haul and then you have the weight. So you've got 4 attributes. And in order to do this, you need just vastly more compute power than we have today. And think about how many planes are being loaded every day, every hour. That's a very exciting project. Another one we did for Hyundai was for autonomous vehicles where for them, it's going to be all about, okay, can they really drive alone and how can you think about the edge cases. We know about the Tesla that ran rate into truck those parked across the highway that blended into the background, because of the setting sun and there was no modeling for that. And it turns out that quantum computers are very good at machine learning and figuring out from data, what can be done because they are inherently not digital. They're made out of real items and what we did for Hyundai was doing image detection. And so what is it that you're seeing and we're able to prove that by using a quantum computer and having lower resolution images and with classical, you can actually have a higher detection rate and correct image classification in faster speed.

And then if we think about some, going back to sort the earliest times we saw a quantum computers first in the market, these giant machines and dilution refrigerators to now sort of the focus of ion qubit shrinking down the quantum processor, combined with what we see now with AI and machine-learning and offload processors in the datacenter, how do you see the future of quantum computing developing sort of along that same path.

So computers need to be small. And that is true. If we all had any x, we wouldn't all have computers, because we couldn't actually fit them anywhere. Everybody here have laptops and like, your watch is now a computer. The goal for us is to make computer that eventually will be a smaller space computers. And we are well underway, which is why we also think it's important not to use dilution refrigerators because they are so big. And if you need a couple of missile silos to house a computer that just limits the amount of people who can have a computer and quantum computing will not kill classical computing, but it will be an important aspect of computing and it means that it needs to be available everywhere and like we are going for militarization and this is something that is, like it's a long-term game, but is also already happening today. We run our computers at room temperature and we require a space, there is not much larger than 2 like fridges that you have in your kitchen and they're getting smaller every day.

Yes. So I think to exactly the point you're getting to it really brings up this idea of like hybrid computer across different types of computer. What type of applications do you think that develops once you can get quantum processors in a datacenter in a server, allow workloads to sort of work across different types of computer?

We announced a partnership with Dell recently where Dell will take us to their customer base and their prospects. For people who want exactly that they want to have the ability to work with quantum, but they also have a very heavy investment in just regular enterprise computer today, and we need them to work together and what you will see is that today, your operating system very quickly and correctly offloads problems sets between the CPU, the FPU and the GPU and it knows like what you need to do where. We are not there with quantum computing, but we will be and the game will be to have available quantum compute power and know when to use it. And the first ones to do this will be large corporations who can afford to invest in both and then afford to integrate them.

Switching gears a bit. We've seen you guys have become very active in 2023 in opening a new development facility, manufacturing facility rather, doing the company's first M&A, can you just talk through the strategy behind those 2 deals.

Absolutely. The first is, we are opening an office in Seattle and right now we have our main office in College Park, Maryland, although thanks to COVID having an office somewhere means that you of course have lots of people all over the place, but we also knew we wanted to have another office and we want to have it in a place with talent, which means that you're looking at San Francisco, New York, Boston, Austin, etcetera, also at Seattle. It turns out that Seattle has a lot of space available after Boeing moved out. They also have a lot of skilled workers who are working in high technology in companies that haven't developed workers too many other technology companies like us. And so there is a labor pool that's available. There is also real estate that's available and we wanted to have access to this labor pool, and we also need to produce computers that aren't just put together by very careful scientists and are working, because of all the customization happen, we need to be able to stamp our product that are consistently the same and for that we want to have a new facility, where we could focus on that mission, having customer-ready hardware being produced and we're going to do that in Seattle. When it comes to our acquisition of Entangled Networks, Jordan is here who did the deal, I thought he can describe it.

Sure. So Entangled Networks is a small startup based in Toronto that we acquired and announced in January. The Company is focused on examining quantum algorithms and thinking about how to run them optimally on different quantum hardware. So their original premise was to say you have a problem, how could I divide that problem between maybe 2 or 3 different quantum computers of different types, maybe coming from different companies, but we saw the opportunity here to bring that [ skill ] set in-house and to help run algorithms on our IonQ computers and it cheaply helps us in 3 ways. The first is, we can take algorithms that we see today and chunk them up and look at different ways to run them on our current hardware and run them more efficiently. The second is that we can look at near-term architectures that we've announced and put it into future systems including multi-core, so having multiple chains via ions on a single trap and multi QPU, which is to say, you have multiple ion trap chips in a single system and we can use the expertise of Entangled Networks to take problems and decide, hey, you should really run this problem, this part of the problem on one chip and the other part on a different chip and combine them. So that is kind of a second way as it helps us in our near-term architectures. And then the third way that it helps us is it informs our long-term architectures as we're designing new systems. So when we're trying to figure out what to build next, how might I design a system that will run really well for certain types of algorithms or that will give me the best performance possible. And so this is a team that has all of that quantitative skill-set, is particularly talented on software and compiler technology and we're excited to be partnered with them to take our industry to the next level.

Thank you. So I think we'll pause here and see if there's any questions from the audience before continuing. Sure. Right on the back.

Just on that compiler and software point you brought up there. The other leading top time business from what I can understand is JV-ed CQC and clearly ticket is class-leading, is used widely maybe not class-leading. Do you see yourself moving ever more towards building that quantum stack for you'll have in-house software as well as the hardware as well.

It's a great question. So will we go full stack. The answer is really that we are a full stack company today. There's probably only one thing that we do that's not full stack, which is we do not develop our own SDK software developer kit, that's because there are plenty of them out there that have done a great job. We won't come one come all take whatever SDK that you like as developer and you can run all of the major ones on IonQ. But in terms of other parts of the stack, like the operating system or applications those we've been building for a long-time now. So we really do look more like a full stack company in every way except for the SDK.

Thank you. Quite new to the story. I wanted to ask maybe on what we're seeing with the hyperscalers investment in supercomputers and getting more predictive models with like how do you see that first of all, how are you seeing the hyperscalers besides Google that you mentioned, investing in quantum computing, first of all. And second of all, does it live side-by-side with supercomputing now as predictive models become more-and-more efficient.

So we see a lot of investment in quantum, which is good. The challenge with the superconductor investment is that there are that type of quantum compute has very high error rates, which means that currently the compute power delivers lower than from ion traps and that over-time, if you try to overcome the deficiency you have in the higher error rates by using error correction, the overhead cost is very, very high between 10,000, to 100,000 to 1, which means that the time to deliver the capacity of even having this error correction is longer, which is also why you see some of them saying that quantum computers x number is far away, it isn't that far away, but we welcome all the investment in this industry, because it will all be needed. To the second part of your question in terms of supercomputers, by the time you get to roughly 70 logical qubits, so these are fully error corrected fault tolerant qubits, most people reckon that, that is time where a quantum computer will have the same compute capacity as the leading supercomputers. That is not very long time from now, that's only a few years and you will still see that supercomputers will continue to exist, and you will just partition the workload or what is best delivered through a classical computer, be it supercomputer or other clusters versus a quantum computer.

Any other questions from the audience. There aren't. I'll just wrap-up with one more from me. As we think about your go-to-market strategy, 3 clear channels is quantum cloud, quantum as a service and then system sales. Can you talk about which you see is really the future growth driver of the business.

So I think this goes in waves. So far it's been access and quantum as a service. And what will happen as quantum computers actually become commercially available for purchase, that will become the largest channel mainly because these things are very expensive. We're talking about tens of millions of dollars per computer. So you don't need to sell that many of them before that overtakes as the main channel. Long-term, you will more than likely see more of what we have today in classical compute where a lot of it goes to AWS and lot of the value will be delivered over the software stack. Software will eat the world, so too in quantum. It's just further away for quantum.

And then one more real quick actually. On the M&A strategy [indiscernible] that you now do have that first deal under your belt, where would you like to add to the business if deals to come up opportunistically.

So we would like to add-in the near-term. We would like to add businesses that accelerate our roadmap, our technological roadmap 10 to 20 years from now. That's a much more complex answer.

And if there's no more questions from the audience, we'll wrap-up here. Thank you.

Thank you.