Lance Glinn:

Welcome into another episode of the Inside the Ice House Podcast. Today's guest is Matthew Kinsella. He is the CEO of Infleqtion. Matt, thanks so much for joining us Inside The Ice House, happy to have you here.

Matt Kinsella:

Great to be here, Lance. Thanks for having me.

Lance Glinn:

So, I want to start our conversation with Infleqtion's most recent first quarter earnings. I know this happened just a couple days ago. When you look at the performance that Infleqtion has delivered, just what stands out to you through quarter one as the core drivers of that success and really how the business has evolved?

Matt Kinsella:

So, core to our business is our focus on both quantum sensing as well as quantum computing. And that's made possible by the flexibility of the underlying technology that we utilize and we can get more into that later in our discussion. And because we make both quantum sensing devices and quantum computing devices, we have a more diversified revenue stream than many other quantum, all other quantum computing only companies. And so, the drivers of our $9.5 million dollars in revenue this quarter and the drivers of our at least $40 million in revenue this year will be both quantum sensors and quantum computers. And so, we have a reasonable mix of each inside those numbers.

Lance Glinn:

And how does that success that you've already seen in quarter one, because you mentioned a little bit about the rest of the year too, how does that shape your confidence in the broader 2026 outlook and what you see for quarters moving forward and everything beyond?

Matt Kinsella:

So, we announced, it wasn't an earnings call, but we did a business update back in March and in late March. And at that point in time, we had guided to approximately $40 million in revenue. And now that we have wrapped up the quarter and I'm seeing things that are making me optimistic, I've tweaked that guidance to at least $40 million. And so, it's a subtle but important tweak and hopefully that showcases that we have good confidence in our ability to grow revenue.

Lance Glinn:

And I think one of the constant tensions for companies in emerging tech is balancing the needs to deliver obviously quarterly results while still investing in long-term breakthrough innovation. It's the focusing on the here, but also focusing on what might be here years from now too, at the same time. How do you approach that balance in a space like quantum where timelines can obviously stretch, but expectations obviously still and always will remain high?

Matt Kinsella:

So, I think about this a lot and I say it, the way I say it internally is we have to sell what we have on the truck and drive that truck to the future. And so we are, again, unique because we have useful products today. And so, we are held accountable for two metrics or I hold ourselves accountable for two metrics. One is near term points on the board by selling the things that we already have to sell. And the second is investing in the R&D to bring forth the progress we need technologically to get to the next phase of our product development and largely that is to make quantum computers useful.

Those are not yet commercially useful unlike quantum sensors, which are. And so, a lot of our R&D is to make these quantum computers get useful faster because it's going to be a massive market opportunity when we do that.

Lance Glinn:

Absolutely. And so, you talk about products, you talk about R&D, you actually brought a prop with you today, which a lot of people don't bring props with them, so I appreciate that. It was unexpected to me, so I don't have any questions about the prop, excuse me, on my script. So, I'm just going to free flow it here. It's on the table, no, it's not the mug, nor is it the cup of water. But show us what that is real quick.

Matt Kinsella:

Absolutely.

Lance Glinn:

Tell me a little bit about it. And we talked a little bit about before we recording about holding something tangible in your hands to explain. Because with something like quantum, a lot of people may never hold it tangibly in their hands. You think of it as like an intangible that just happens and just works, but yet you're holding something I got to hold it before. So, explain what that is and sort of just the function that it has.

Matt Kinsella:

Right. Okay. So, let me start at the very basic level, like what is quantum? When we're talking about quantum, we're talking about the world of the very small. So, we're talking about the atomic and the subatomic levels where there's a whole different set of rules that govern down there and those are called quantum mechanics. They were discovered in the late 1800s, early 1900s when scientist like Einstein realized that at the world of the very small, things work very differently than they do at the world of-

Lance Glinn:

Which we live in.

Matt Kinsella:

... that we live in. And these quantum mechanical principles, they're not intuitive, they don't make a lot of sense. But if you can harness them, you can build products that can do things that classical technologies cannot do. So, what is this? This is our quantum core and inside this core live millions of atoms and those atoms are the basis of all of our products because what are we doing? We're using lasers to interact with the atoms inside this quantum core, take advantage of the quantum mechanical properties of those atoms and then turn those atoms into useful products.

And those products can range from clocks to quantum spectrum, which is what we call our quantum antennas to quantum sensors to quantum computers. And at the end of the day, we are using lasers to take advantage of those quantum mechanical properties and then turning them into that whole spectrum of products. But the underlying core technology is the same, it's something just like this. And why does all this matter? It's because if you can harness those quantum mechanical properties, these things can perform at orders of magnitude. We're talking like 10, 100,000, 10,000 X performance levels to classical technologies.

Lance Glinn:

So, is something like that, that Quantum Core that's in, is that in all the different products that you have? So, something like, obviously not that specific one, but something like that is in all the specific products. Are they all shaped to that specificity? Are they shaped differently?

Matt Kinsella:

They can be shaped differently depending on the use case. So, for our clocks or our antennas, we can actually use a much smaller version of this. For our computers, it is something that looks like this. And then for our sensors, our inertial sensors, we actually just sent one up into space three weeks ago, it looked quite a bit like this actually. And so, depending on the end use case, they'll be shaped differently. The atoms have to move through different vacuum chambers and then your pressure has to come in or out and ultimately this is a vacuum cell.

Some of our products don't require as, what's the right term?

Lance Glinn:

Hefty.

Matt Kinsella:

Vacuumy of a system, they can be less vacuuming. And so, they will look different and can be smaller or in some cases bigger, but they're functionally the same thing more or less.

Lance Glinn:

And we were talking before the podcast and I had mentioned to you that it's conversations like these that I really learn the most in because we've all heard of quantum or we've all heard of the word. Just because you've heard the word doesn't necessarily know you know what the word exactly means.

Matt Kinsella:

Right, totally.

Lance Glinn:

And so, I have, I guess a basic knowledge, right? I've done my research for this conversation, I have an idea of what it is. But again, to see something tangible that you can hold in your hand to help explain exactly what it is, how important to you is that education to helping people like myself who aren't in the quantum space understand exactly what it is and what it's going to help the future look like.

Matt Kinsella:

It is critical and it's so critical that it's one of the bases of our entire company's strategy. And what I mean by that is, so before I became CEO of Infleqtion two years ago, I was the first investor in Infleqtion actually. So, I worked on Wall Street and as a venture capitalist for about 18 years and I got really curious about quantum in 2017. I started going down the quantum rabbit hole, met a bunch of quantum companies and most of those companies were focused only on building quantum computers. And that to me was super interesting because quantum computing is going to be very important to humanity, but it's always off in the distance.

And what I found so intriguing about the neutral atom technology, and that's what our company is based on and it actually comes out of the University of Colorado Boulder and that's where our founder Dana has spent 40 years on Nobel Prize winning teams developing this technology. What I found so compelling about neutral atoms was that flexibility that we talked about at the beginning to actually build and put quantum products out in the world that have real quantum advantage today because that makes it real.

Until quantum computers get commercially useful, it's exciting for us to get ready for them, but they just don't do anything useful for us yet. Our quantum sensors can do things that are very, very useful to humanity. So, part of our goal, going back to the sell what we have on the truck today, it's to generate revenue, but it's also to show the world that quantum does bring value into the world today. And I think that will just actually help accelerate us getting to quantum computing because as we build and ship these quantum sensors, it makes us better at building our quantum computers.

Lance Glinn:

Absolutely. No, I think that's a great point. You bring up, you were in venture capital for so long, you've been CEO of Infleqtion for, I think you said about two years. What made you want to switch and focus from venture capital to now solely on one company?

Matt Kinsella:

Yeah. So, I started my career here in New York at a hedge fund called Maverick Capital and I focused on fundamental equity analysis. And so, really going deep on understanding companies and then investing in the stock market and I loved that. Then I helped start our venture capital effort at Maverick and that's why I moved out to Silicon Valley. And when you go from public equity investing to venture capital, you get a little closer to the businesses, if that makes sense. You are working with the management teams more closely. You almost feel like you're part of the team, even though you're still one step removed.

But I noticed that I really loved that incremental step towards the operations of the company. And so, I didn't actually have any plans to leave. You talk to people in finance a lot, you probably know that 18 years at the same fund is like, I don't know, there's like a dog year multiplier. It's like 100 years in finance world, right? So, I was planning to be there for the long term-

Lance Glinn:

And I feel like that's a rarity now.

Matt Kinsella:

It is. It is. Yeah, it's very unusual. So, I had no plans to leave, but a couple things broke the right way and this opportunity became available. And I think most VCs have one company in their portfolio that they spend outsize amounts of time on and they absolutely love. And Infleqtion was always that to me. I'd been on the board since 2018 and so it probably should have been a hard decision to leave a really good career, but when the opportunity became available, it was a pretty darn easy decision. So, why was it easy? Really because I believe that this is so, so, so important to humanity to get out into the world, so important to the United States of America to win the quantum race.

And it's the opportunity to build a generational company and be part of something truly special. And so, my wife is a native San Franciscan. We had a three-year-old and a one-month-old at the time and we had a very defined path. We were going to move to Marin County across the Golden Gate Bridge and so this involved a move to Boulder. So, there were a lot of things that made this the non-obvious choice, but it, again, was a very easy decision.

Lance Glinn:

Absolutely. And you mentioned a couple things. One, the quantum race, we're actually going to talk a little bit about that quantum race later in the conversation. And two, sometimes you just have a passion that you don't know is inside.

Matt Kinsella:

Totally.

Lance Glinn:

You have a passion. You may have not known that it was for quantum and yet you find Infleqtion during your VC career and you just realize that, hey, maybe that's your calling. And while yes, there are those non-business variables that come into play obviously too that you have to make or that's used to make your decision, but sometimes you just have your passion. And I can clearly see that quantum and Infleqtion frankly was yours.

Matt Kinsella:

They are. Yes, you're absolutely right.

Lance Glinn:

So, over the course of our conversation so far, you've mentioned this quantum spectrum, this quantum sensing and this is starting a new category within the industry already. What does it mean to define a category in a space that's highly technical and why was now the right time to create something distinct?

Matt Kinsella:

So, one of the things we can turn our atoms into is almost magical antennas and we call those magical antennas quantum spectrum. And what can you do with a magical antenna? And any of my colleagues who are quantum physicists will definitely take issue with me using the word magic because they don't like when I call it [inaudible 00:12:29] magic.

Lance Glinn:

Nothing's magic.

Matt Kinsella:

Yeah, there's no magic. Yeah. So, normally you need an antenna and antennas have been around for 120 years, RF technology is very robust. The way our traditional RF technology has worked going back to the late 1800s or early 1900s has been you have a metallic antenna that's approximately the size of the wavelength you're receiving and then that antenna vibrates based upon the wavelength that's receiving. And then you extract the electric signal that's embedded in that vibration. Now, what is the obvious thing for that is that some antennas need to be huge because you're receiving very low frequency, very long wavelength.

At the extreme, they can be a kilometer long or you've seen massive antennas out in fields around the top of every building here in New York. We can collapse those massive antennas down to something much smaller than this. And the reason-

Lance Glinn:

Smaller than that.

Matt Kinsella:

Oh yeah. It can be the size of a sugar cube.

Lance Glinn:

Wow.

Matt Kinsella:

It's still hooked up to maybe a mini fridge sized electronic and photonic system, but ultimately that will become chip scale.

Lance Glinn:

Wow.

Matt Kinsella:

And so, how do we do that? And this is one of these orders of magnitude changes that quantum can bring about. It fundamentally rethinks the way you receive an RF signal, and so instead of a metallic device vibrating based upon the frequency of the signal, the atoms themselves become mini antennas that can receive the entire electromagnetic spectrum. So, instead of needing a very dedicated metallic antenna for a specific band of the spectrum, this can be tuned to receive the entire thing and it can make these massive antennas very small or can also do the job with the very small antennas to begin with like we have on our iPhones.

And so, this is a huge potential category here and why did we define it and how did we define it? It's because we're just seeing huge demand pulls for it. Based on what I just explained to you, you probably can grok why people might want this. We can get into some of the specific use cases, but it's truly like a step function change in tech.

Lance Glinn:

Yeah. To take something that is, like you said, these antennas that are on the top of almost every skyscraper here in New York City and I think you said a sugar cube, potentially bring them down to the size of a sugar cube. You can just imagine in your head what that could mean for the world, what can mean for just everyday people and all of the different use cases that may not exist now, but could exist from that in the years to come.

Matt Kinsella:

Exactly. The obvious low hanging fruit is take big things and make them small, but the potential of other types of applications are vast.

Lance Glinn:

And why do you think ... So, if you're trying to, or if you think you can take something that's so large and bring it down to a obviously sugar cube level size, why do you think existing symptoms right now are starting to run into problems, are starting to essentially fall short in today's environment?

Matt Kinsella:

Well, if we look at the RF use case in particular, let me give you a couple of examples as to why you might want to go from using something big to something small. And then I can actually extrapolate this answer out to a much broader subset of what technologies are starting to be less robust than they once were and why quantum's going to have to step in. But let me address the more narrow question first. Imagine you're in the army and you're out in the field and you are trying to use radar systems to detect things that are coming at you, drone swarms.

Often those signals that are being emitted by the things you're trying to detect are in those frequency bands that I described that are very long, so very long wavelength. So, you have a semi-truck sized array of antennas next to you to help you pick these things up. Now, what does a semi-truck-sized array of antennas do? It opens you up to be a target. I think the life of a radar system in Ukraine right now is like two minutes. They turn it on, it's identified, it's destroyed. And so, there are two things that our technology can do that would help against that situation of being destroyed instantly.

Number one, it's much smaller. And number two, and I didn't mention this, this is a part of the other magical property, it doesn't emit anything. So usually, any kind of RF receiver or any kind of RF transmitter obviously is constantly, if they're receiving, it's emitting, this does not emit and so therefore it cannot be detected. And so, if you are out in the field and you want to not be detected, this is a great way to do that. So, there's one example and let me give you a more commercial example actually. So, high frequency trading, very relevant to the building we're sitting in right now.

There are two ways to send long distance high frequency trades. One is across the fiber optic network and that will get you from point A to point B at like 0.6 the speed of light because there's a lot of hop skips and jumps it takes. What high frequency traders have been doing for a long time is sending signals at those low frequency long wavelengths directly up into space, bouncing them off the ionosphere and then bringing them back down and receiving them on a massive antenna that's maybe 50 miles away from an exchange and then wiring fiber directly to the exchange.

Now, a massive antenna is hard to build right here on Wall Street, but you could build a very small antenna. And so, you could imagine a world in which they're replacing those huge antennas that are often a field in New Jersey and bringing them much closer, maybe even co-located here on the exchange to allow them to make those 1X the speed of light trades as opposed to the 0.6X speed of light. So, it's almost like a zoning permitting issue because you can't build these huge antennas that is going to allow quantum to proliferate.

Lance Glinn:

That's fascinating. I really think that's fascinating. And I appreciate you showing the two examples because not everyone is in the field like you were talking about having swarms of drones and using radar technology. Not everyone's doing that. So, it's hard for everyone to relate to that. But when you bring that second example, I think it just opens up the real world more common person use cases that I think a lot of people or a lot more people say can relate to. So, I really think that's fascinating. Now look, Infleqtion is building prototypes, is running these field tests, hardening these symptoms or these systems, excuse me, for real world deployment.

But through everything that you're talking about, just what is the process? What is the transition in taking something like this and then bringing it to something that's truly operational at scale?

Matt Kinsella:

Yeah.

Lance Glinn:

Because one thing to obviously talk about it and to build it, but then to scale it and then commercialize it, what's that process like?

Matt Kinsella:

It is in many ways it's in many ways as challenging, but a very different challenge than the scientific breakthroughs you need to make. So, I like to think of all of our products as this conveyor belt and on the left-hand side or the beginning of the conveyor belt is a DARPA program or a NASA program or one of the national labs funds us to build something that didn't seem possible in the past. So, let me actually use, let's switch to our clock product and we'll use it as an example of how we bring things out into the world.

So, four years ago or so, DARPA said, "Hey, can someone build what's called a rack mounted optical clock?" Historically, optical quantum clocks have been bigger than this table. And we said, "Yeah, we could probably do that." And so, as part of that program, we took something that was the size of this table and we shrunk it down to something the size of an Xbox call it. And now we've gone from research to prototype. And the nice thing about this is we know there's somebody out there somewhere probably in the defense department who wanted this thing. And so, now we have a prototype for this thing and then you can move it into a procurement process.

And so, we got put into something called the accelerated procurement for the fielding of innovative technologies program where we now were set to build 12 of these clocks and then sell them to the Army. And through that process, we learned how to systems engineer these things, how to harden them, how to turn them from a prototype into a product. And now, we've got a product that is hardened, that works and can be sold into the commercial markets or the military markets. And so, that's been the process we've followed and there are different challenges along the way.

And then because of our particular technology, because everything is based on one of these quantum cores and then lasers, you can do huge amounts of integration to take it down from an Xbox size ultimately to chip scale. And so, you can think of all of our products moving down that conveyor belt.

Lance Glinn:

Where do you think we are in the lifecycle of quantum? Are we at the point where it's hype still? Are we at an Infleqtion point? Are we at no pun intended and Infleqtion point? Are we at a point where we're ready to start commercialization? Where do we sit within this lifecycle on this ecosystem?

Matt Kinsella:

Different answers for the sensing and the computing side of the business, sensing ready to ship, like shipping, commercialized basically and various stages based upon the type of sensor. Compute, the analogy I like to use is we are probably call it where GPUs and AI were in 2016, 2017. And what I mean by that is at that point in time, you started to see large deployments of GPUs at the national labs that were focused on physics type problems. But the folks of the DOE and the folks at NVIDIA had a hunch that there would be more and the attention is all you need, paper had already been written at that point.

And it just took the time to match the large language model architecture to GPUs and then the rest is history. And then we all experienced, what was it, 2022 or whatever?

Lance Glinn:

Yeah, late 2022-ish, 2023, maybe early.

Matt Kinsella:

November of 2022 of GPT-3, first ChatGPT. But it had been bubbling under the surface for many years before that. I think we're around that time, you're starting to see the Department of Energy talk about deploying quantum computing systems in the national labs and I think it's going to follow a very similar trajectory. And I think that the Department of Energy feels very similar to that, that they are laying the groundwork to take quantum and it's going to become the commercial success that GPUs and AI did.

Lance Glinn:

Is there an intersection between quantum and AI? And if so, what do you see as the most important or most significant applications emerging from bringing these two technologies together?

Matt Kinsella:

There is a very significant overlap between the two and I think they're going to be self-reinforcing and a virtuous circle and it works both ways. So right now, we use AI to accelerate the development of our quantum computers and I'll give you one specific example. We have enough physical qubits to make these computers useful. The qubits are getting to the quality where they are going to be useful, but they're still prone to errors. And so, one of the things we need to spend a lot of time on is error correction.

And this is very normal in classical technologies too. Your computer, a bunch of your bits will always fail, but there's error correction built into it. Or if you're receiving a signal over Wi-Fi, there's redundant signal being sent because you know you're going to lose some. So, error correction is one of the last things that need to be accomplished in quantum and AI can help you do that because you can detect the error, but tracing the error back to its root cause is inherently an inference problem. And so, large language models are very good at inference. And so, we've used inference to help us decrease our errors.

So, that's one way AI is helping us get to useful quantum computing. Now, how does quantum help AI? There are a subset and when I say subset, I mean a huge, huge, huge market opportunity of problems that classical computers, even the most powerful GPU clusters will never be able to accomplish because they're quantum mechanical in nature. And so, even the most powerful GPU cluster at the end of the day is boiling everything down to zeros and ones, but that is not how nature works, nature is not binary, nature is quantum.

And those are the types of problems that if you tried to point a classical computer at, it would take it like a trillion years because it's just going to try every single possible combination of zeros and ones and serially run through all those and it just would not be able to accomplish. And that's actually what modern day encryption is based on, the fundamental and inherent inability of computers to do certain types of calculations. Those are the types of calculations that quantum computers can do very easily and that could be modeling the interactions of two atoms as they come together and the electron interactions as those atoms come together as molecules is quantum mechanical in nature.

And so, if you're trying to figure out how to build new materials by smooshing new atoms together or new drugs by smooshing new molecules together, there's a huge part of that equation that is quantum mechanical in nature and classical GPUs can't do that. So, you will have the GPUs and AI clusters doing a large portion of the work, but increasingly those really hard quantum mechanical property parts are going to be hived off to the quantum computers. And so, I believe the way the world's going to look in the future is you'll have a data center stack where you have CPUs and then GPUs layered on top of CPUs to enable large language models.

And then what we call QPUs, quantum processors will layer in there too, you'll send problems into the data center, you won't care which part of the stack solves it. It's just now you can solve problems you couldn't solve in the past. So, I think they're very, very, very self-reinforcing.

Lance Glinn:

And so, we have AI, we have quantum and I think there's a clear AI race going on. And you mentioned earlier in the conversation this quantum race too. What does it mean in your mind to win in quantum or to win the quantum race?

Matt Kinsella:

I think humanity wins when we get more quantum technologies out there because of all the new types of applications that it will enable and the new breakthroughs in compute we will all experience. That said, I do believe it's important that the US and our allies are the ones who win this quantum race because we know that some other countries, China, Russia are investing heavily in this as well. And it's not too dissimilar from the space race or even maybe the nuclear race, the side that achieves this first will have a large advantage ranging from the ability to crack encryption and spy on everything that we're doing.

Or to steal all the Bitcoin, but also maybe even more tangibly, one of the things that quantum can do is recreate the navigation and synchronization that we rely on GPS for today. And if there was, god forbid, a hot war to breakout over the strait of Taiwan, the first thing to go down would be GPS and the side that could navigate or synchronize or communicate in the lack of access to GPS would have a massive advantage.

Lance Glinn:

And this has to be, and correct me if I'm wrong, this can't be an Infleqtion versus the rest of the world fight. This has to be a industry-wide battle, so to speak. You have to work with others in the industry to come together to win this race, correct? Infleqtion can't win this race on its own, or maybe it can.

Matt Kinsella:

There's some coopetition happening for sure. And we are at the phase in the quantum computing world where I think everybody is the rising tide is lifting all boats, we're all trying to get to useful quantum computing. That said, I do think actually probably the most important function we serve for each other is just that competition, which has driven the US economy for hundreds of years. And I think you wouldn't be surprised that China's taking a very top-down directed investment approach like they're doing for AI.

The US is taking advantage of our robust capital markets that come from this building, the New York Stock Exchange. And just like you see competition between all the large language model providers, they are competing like heck with each other, Anthropic, OpenAI, Gemini, but they're pushing each other faster and that's why we're in the lead there. I do think you're going to see a similar thing play out with quantum where we are going to compete with each other, but it's going to push the US to win faster.

Lance Glinn:

And speaking of that competition, looking ahead now as we get towards the end of our conversation, what do you think will separate the companies that truly lead in quantum versus those that end up being stagnant or maybe even end up falling off?

Matt Kinsella:

I deeply believe that the commercial mindset and the ability to ship product is going to be the thing that makes the difference. And so, everybody's got research efforts. We're all racing to get to useful quantum computing as fast as we can, but the transition from a research mindset and a research focus to a focus of we need to ship, we need to build, these things need to work, they need to work like they're supposed to work, that's a really hard transition to make. And we have intentionally embedded that in the DNA of Infleqtion from day one because we sell all these quantum sensing products.

And so, it's non-intuitive or was non-intuitive five, six years ago that that would be the answer, but I think that will actually ultimately be the answer.

Lance Glinn:

And what does the next chapter for Infleqtion look like?

Matt Kinsella:

So, we need to sell more sensors and we need to get to useful quantum computing and that's what we're focused on every day and we need to make our sensors smaller, cheaper and better. So, they go from Xbox size to chip size, they get cheaper so it's a no-brainer to replace classical technologies and then we got to take everything we learn from what we're doing in sensing and apply that to computing to get to useful quantum computing faster.

Lance Glinn:

Well, Matt, I learned a lot during this conversation, this was a fascinating one. Thank you so much for bringing your prop. Thanks so much for doing Inside the Ice House.

Matt Kinsella:

Absolutely, great to be here.