In this interview from the theCUBE + NYSE Wired: AI Factories - Data Centers of the Future event, Matthew Kinsella, chief executive officer of Infleqtion, joins theCUBE’s John Furrier to discuss the company’s milestone NYSE public listing and $550 million capital raise. Kinsella reveals how Infleqtion is accelerating the commercialization of quantum technology, transitioning it from research labs directly into enterprise and defense applications. He dives into the strategic cross-currents between quantum processing units (QPUs) and GPUs, highlighting Infleqtion's work with NVIDIA to expand context windows and process high-velocity edge data. By loading quantum-inspired software onto classical GPUs, Infleqtion is already unlocking performance advantages for real-time edge sensors and laying the groundwork for the next era of AI scale and distributed computing.
The conversation also explores Infleqtion’s aggressive roadmap and financial momentum, noting $29 million in 2024 revenue and $50 million in awarded 2025 bookings. Kinsella unpacks the company's continuum of quantum products, from commercially available quantum clocks and NASA-bound gravity sensors to the race toward 100 logical qubits by 2028. As AI factories reshape enterprise infrastructure, he predicts QPUs will soon operate alongside CPUs and GPUs to solve complex material science workloads and generate synthetic training data for large language models. The discussion concludes with a candid warning about "Q Day," urging organizations to adopt post-quantum encryption now to defend against "harvest now, decrypt later" cyber threats.
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Matthew Kinsella, Infleqtion
In this interview from theCUBE + NYSE Wired: AI Factories – Data Centers of the Future event, Glean co-founder and CEO Arvind Jain joins theCUBE’s John Furrier to unpack what’s really working in enterprise AI today and what comes next. Jain explains why knowledge access remains the first successful AI use case at scale and how Glean’s enterprise search brings AI into everyday work. He details the past year’s lessons with AI agents – from the need for guardrails, security, evaluation and monitoring to democratizing agent building so business owners (not just data scientists) can create production-grade agents.
The conversation dives into Glean’s vision of the enterprise brain powered by an enterprise graph, highlighting the importance of deep context, human workflows and behavior to reduce “noise” and drive outcomes. Jain outlines core building blocks – hundreds of enterprise integrations and a growing actions library – that let agents securely read company knowledge and take actions across systems (e.g., CRM updates, HR tasks, calendar checks). He discusses how organizations are standing up AI Centers of Excellence, prioritizing “top 10–20” agents across functions like engineering, support and sales, and why a horizontal AI data platform that unifies structured and unstructured data – accessed conversationally and stitched together via standards like MCP – sets the foundation for AI factory-scale operations. Looking ahead, Jain says Glean’s upgraded assistant is evolving from reactive tool to proactive companion that anticipates tasks and accelerates productivity.
In this interview from the theCUBE + NYSE Wired: AI Factories - Data Centers of the Future event, Matthew Kinsella, chief executive officer of Infleqtion, joins theCUBE’s John Furrier to discuss the company’s milestone NYSE public listing and $550 million capital raise. Kinsella reveals how Infleqtion is accelerating the commercialization of quantum technology, transitioning it from research labs directly into enterprise and defense applications. He dives into the strategic cross-currents between quantum processing units (QPUs) and GPUs, highlighting Infleqti...Read more
exploreKeep Exploring
Why did the company go public/complete this transaction, and how will the $550 million raised be used to advance its mission of commercializing quantum technology?add
What is the company's recent business progress and momentum, including revenue/bookings, funding/runway, and the status of its quantum sensing and quantum computing products?add
How did you ensure Inflection transitioned from a research-focused organization into one with strong commercial and engineering focus that consistently ships products?add
How do you plan to use the funds you recently raised?add
>> Welcome back. I'm John Furrier, host of theCUBE, here at theCUBE's NYSE Studios. Of course, we got our Palo Alto studios connecting Wall Street and Silicon Valley. This is part of our AI Factory series, this is a series exploring how AI infrastructure and the future infrastructure of computing is going to impact how we live, how we work, how we play. Then, societal benefits are multifold as new use cases emerge. Matt Kinsella's here, CEO of Infleqtion, just went public this morning on the NYSE Exchange. Part of our NYSE Wired program, a CUBE original. Matt, thanks for coming back. Thanks for being in the network and congratulations.
Matthew Kinsella
>> Thanks, John. It's great to be back.
John Furrier
>> So, you're on the podium, ringing the bell this morning, opening up the stock market on your IPO. Congratulations. What's it feel to be up there?
Matthew Kinsella
>> Well, it was an incredible feeling and we actually had a first in the more than 230-year history of the NYSE Stock Exchange. We wore flight suits and dressed up like Top Gun for the bell ringing.
John Furrier
>> The need for speed, right?
Matthew Kinsella
>> Need for speed, that's right. Yeah.
John Furrier
>> Need for speed. I'll be your wingman anytime.
Matthew Kinsella
>> You can be my wingman anytime.
John Furrier
>> Well, thanks for coming on. And this brings up a good point. I mean, it really highlights the speed side of it. Quantum, it feels like it's been a science project moving at the glacier speed because of standard bodies, readiness, but now you're starting to see real alpha engineers really starting to roll out the early stages of the core software. You're starting to see devices, the role of GPUs, accelerating quantum computing, use cases coming. You guys have had a lot of success. Tell us what's new with you guys as you're public. You got to deliver the results, so you're now a public company. We're going to see everything. Tell us what's happening.
Matthew Kinsella
>> Well, the reason to do this was it's financing, right? Going public as part of the financing, but we raised $550 million as part of this transaction. And I look at this as now we've got the fuel in the tank to accomplish the mission. And our mission has always been to take quantum from the research lab and take it out into commercialization. And we've been following NVIDIA's approach to commercialization in and that with this very powerful core quantum engine that we first pointed at some near-term use cases that we actually have quantum advantage on today, like timekeeping, like sensing, and we can get into some of the applications of those products. And then, building to the point where quantum computers are going to do all the amazing things that we're all excited about, which we think will happen in about 2028.
John Furrier
>> It's funny you mention NVIDIA. At GTC in October in DC, which was kind of a special event for them because it had a lot of politics involved. Obviously, you had the China story. Earlier in the year in March, GTC, he talked about quantum mainly because he made a comment at CES earlier in the year and he's commented to me when I talked to him about quantum, he said, "John, every time I say quantum, the NVIDIA stock price goes up." He didn't poo-poo quantum. He was really more like, "It wasn't ready." He wasn't as bullish as he normally is, but he's had it on the roadmap for a while. So, talk about your relationship with NVIDIA. How's that integrated to you guys? Because he was all guns blaring in October in DC around the role of quantum, the role that NVIDIA is playing in quantum, how that's creating a progression. Talk about the relationship with NVIDIA.
Matthew Kinsella
>> Well, they're an absolutely wonderful firm. A couple of their folks were here with us as we rang the bell today, which was really exciting. And you spoke about NVIDIA's GTC DC. Hilariously, they had put together this pregame show, which they hired a bunch of ESPN college game day producers to do and I was the quantum talking head on that. And while I was up there, Jensen came up and we started a quantum chant.
John Furrier
>> I was there for that. I saw that.
Matthew Kinsella
>> That was something else. But I'll tell you what they put out into the world at that show was really interesting. It's called NVQLink, and it's a way to link their GPUs to quantum computers because the cross-currents between quantum and GPUs are massive. And so, this is just a way to accelerate the ways for GPUs and QPUs to work hand in hand.
John Furrier
>> Talk about the relationship again, because most people don't understand the relationship with NVIDIA is doing to quantum. Even though their GPUs aren't quantum, the scientists working on quantum are using them as a tooling, but also platform. Talk about that piece.
Matthew Kinsella
>> It's a very, call it, self-reinforcing circle, the benefits between AI and quantum. And so, how does AI help quantum? Well, a lot of the work we need to do to make quantum computers useful is identifying where the errors are in our systems. And so, we have enough qubits, they're just error-prone. And actually, the identification of the errors is relatively easy, but tracing them back to their root cause is actually an inference problem. So, as we can utilize GPUs to do the inference to trace back to the errors, we can accelerate the correction of those errors and get to useful quantum computing. And then, I don't ever think quantum is going to replace GPUs. I think why Jensen's excited about it is he looks at it as a way to sell more GPUs, and it's going to be quantum processors working hand-in-hand with GPUs to solve new problems.
John Furrier
>> It's not only picks and shovels for the creators and the scientists, it's also a key part of the system, you're saying?
Matthew Kinsella
>> Yeah, absolutely.
John Furrier
>> All right. So, talk about how quantum perform... You were last on theCUBE last fall. Milestones you had mentioned before we came on camera, some qubit milestones. Let's jump in the weeds for a little bit. Tell us about some of the technical accomplishments.
Matthew Kinsella
>> Yeah. So, we have two parts to our business. We have the sensing piece of the business, then we have the compute piece of our business. On the sensing side of our business, we already have quantum advantage, these are products that do things that classical products can't do. And we announced a big contract with NASA two weeks ago, where we're sending some quantum sensors into space to sense gravity on the Earth's surface and in fact, changes in gravity on the Earth's surface with the extreme precision. And you can infer some pretty interesting things about what's happening on and below Earth's surface if you can see changes in gravity, digging of tunnels, movement of heavy materials, like nuclear materials, a lot of interesting things. On the quantum computing side, it's really a race. We need to get to 100 logical qubits. That is when quantum computers become useful. We announced in October of last year that we're at 12. Our roadmap calls to get to 30 by the end of this year and do 100 by the end of 2028, and that's when we'll start to see some of the early applications of quantum computing.
John Furrier
>> Share a little bit of what that looks like, that picture. Share what you guys are thinking as you look at your milestone out with 100 qubits, what does that look like?
Matthew Kinsella
>> So, at 100 logical qubits... And just to define the terms, logical qubits are error corrected, pristine qubits that can do interesting things. Physical qubits are error-prone, you can't use them. So, 100 logical qubits will be more than 100 physical qubits, but let's just focus on the logical qubits. 100 logical qubits, at that time, we think we'll start to be able to do some interesting things in material science. And so, material science is quantum mechanical in nature, you're smooshing molecules together, the electrons are coming together. That's quantum mechanical. That's very challenging for a classical computer to model, easy for a quantum computer to model. And so, it's the development of new materials, like a battery that would make your iPhone last for a year instead of a day, that kind of thing. So, we'll see-
John Furrier
>> What I love about your company is one, I love the NVIDIA connection, that's why I wanted to highlight that. But two, you guys are taking a very strong innovation approach by shipping and moving the needle and commercializing it. So, you got the space deal. What's it like on the business front, right? Can you share a little bit about some of the progress and momentum?
Matthew Kinsella
>> Sure. So, in 2024, we did $29 million in revenue. We announced that in 2025 we were booked or awarded $50 million in bookings or awarded business. That sets the stage for good growth going forward. And so, we're really commercializing the quantum sensing side of our business. We've sold three quantum computers, even though they're not yet commercially useful. And then, we're marching to the point where those quantum computers are going to get useful.
John Furrier
>> Yeah. And so, the funding event, obviously, there's more money in the capital markets here. You have the runway. Product focus and the product focus on the sensing side. How's that look? What's the progress there? And the quantum... You gave that kind of roadmap. What's the roadmap look like on the sensing side?
Matthew Kinsella
>> The sensing side? The nice thing about our technology, which is the neutral atom quantum modality, is it's very flexible. And so, our quantum core, we use the same underlying technology for all of our products. And so, the most commercialized of our products is a quantum clock, and that keeps time about 1,000 times more precisely than any other clocks out there. The next step is a quantum RF antenna. Same underlying technology. You might say, "What the heck does a clock and antenna have to do with each other?" In the case of quantum, they're basically the same thing, just slightly different usage. And then, the sensors, like the one we're putting into space, are probably the most robust thing you can do with quantum sensing. And then, it's just a hop skip and a jump to turning that into a computer, same underlying technology. And so, it's this continuum of complexity, call it, from clocks to computers on what you can do with quantum technology.
John Furrier
>> So, you have upgrade headroom in space?
Matthew Kinsella
>> Yeah, basically. Yeah.
John Furrier
>> What's it like in the company? What's the culture like? Obviously, a lot of development breakthroughs. What's it like? What's the-
Matthew Kinsella
>> I mean, I'll give you a day in Infleqtion. I'll tell you when I walk in the door, the average IQ plummets because these are the smartest human beings in the world.
John Furrier
>> Exactly.
Matthew Kinsella
>> But I mean, our founder, Dana, will be on his wall signing, solving Maxwell equations. I'll walk in and be like, "Dana, you forgot to carry the one." So, I'd say it's a mix of a lot of fun, but really, heavy, heavy both science, but importantly, engineering and commercial focus. And so, I was the first investor in this company back in 2018, before I joined full-time as CEO. Having been an investor for a long time, I know it's very challenging to transition from a research-focused company to a commercial-focused company. So, I made sure that commercial DNA has been embedded in the business from the moment I invested in it. And so, we are always focused on shipping product on time. You can't send a team of physicists with each product. You can't turn the screwdriver to make it better. You have to put pencils down and ship. That's a hard transition to make, and so that's embedded in our DNA going back for eight years.
John Furrier
>> I have to ask because you might not be the smartest guy in the room when you're in the company, but I'm sure when you're out with your friends with the kids and other families, it's like, "What does he do? He's a rocket scientist." Demystify quantum because people think it's something different than it really is. So, they'll be like, "Well, it's a bunch of scientists and it'll crack all the codes." Where are we? What's the reality of quantum computing today? How would you describe it?
Matthew Kinsella
>> At the highest level, when we talk about quantum, we are talking about the world of the very small, so the atomic and the subatomic, and there's a whole different set of rules down there called quantum mechanics. And when someone says, "I work in quantum," it means they're trying to turn the properties of quantum mechanics into useful products that will help us as humanity. And so, what we do is we turn those properties of quantum mechanics into clocks, and RF antennas, and sensors, and computers. So, really that at the end of the day is what it is. If you look at history, all forward progress usually starts with some breakthrough in physics. We had the classical physics that were levers and pulleys and things like that that built the classical world. Then, you had the laws of thermodynamics, which led to the industrial revolution. The laws of electromagnetism, which led to our computers and our internet. The one unexplored area of physics is quantum. And so, this will lead to a whole new generation of human progress as we can start to tap into that power.
John Furrier
>> And I like how you brought in NVIDIA, I want to go back to that. And so, now talk about the distinction between GPUs and what NVIDIA is doing with what they've done with high-performance computing, which was a slow-moving industry of power workstations, basically, to distributed nodes of massive supercomputing capability. How does that vector into the synergy of quantum?
Matthew Kinsella
>> Well, there's that self-reinforcing loop where GPUs will help us get to useful quantum computers faster, but when I look at the future and I think about what the data center looks like in the future, it's been fascinating to watch how GPUs have just proliferated into a data center that was dominated by CPUs historically, right? And now, GPUs are a major player, but you still have CPUs. I believe QPUs, quantum processors, will come into the data center in a very similar way and work in tandem with CPUs, GPUs and QPUs. And you're going to send problems in the form of workloads into data centers. It's going to get chopped up and sent to the appropriate part of the stack to be solved by what solves it best. No one's going to care that it's quantum, but you can just do new things that we hadn't been able to do from-
John Furrier
>> Yeah. One of the big things about the AI wave that I love, generative AI wave, certainly NVIDIA's enabled... Others are doing the frontier models, is it abstracts away a lot of the interfaces and constraints around usability.
Matthew Kinsella
>> Yep, absolutely.
John Furrier
>> But what you're saying here is that quantum will be in the data center, but it's also going to accelerate distributed computing as we know it.
Matthew Kinsella
>> Absolutely.
John Furrier
>> So, edge is now on the table-
Matthew Kinsella
>> Very much so.
John Furrier
>> I've been cheering from the top of my lungs, it's going to come to the edge with telecom convergence. What's that look like from a customer standpoint? How ready are they? That's a big topic in quantum, quantum readiness, mostly from a security private key and making sure the post-quantum encryption market. What's the market and customers doing? Obviously, at the high end, NASA, I see that clearly. And what are they doing? What's their back-end? What's the consequence? It's a moment of consequence for them. Now, they have all new capabilities. What are they doing? Are they reorganizing? Are they rethinking? How are they scoping their landscape of operating these, which will become large-scale, distributed networks?
Matthew Kinsella
>> Yeah, the edge is really interesting for quantum, and actually, not to introduce a totally new topic here, but what we've been able to do is take software that we wrote for quantum computers and start to apply it on GPUs. And it actually has shown to unlock some incredible capabilities in GPUs, particularly NVIDIA's Jetson edge-deployed GPUs. And what it allows you to do is actually to expand the context window of those GPUs, so they can ingest more information and process more information at the edge than they otherwise would be able to, it would get overwhelmed. And so, we've started to actually deploy our software on NVIDIA's GPUs and monetize that in the form of edge-deployed sensors for the Army and the Navy, allowing them to ingest either real-time streaming data from sensors or real-time RF streams that would normally overwhelm an edge GPU. And so, it's been incredible to see a quantum-inspired software loaded onto classical GPUs and showing performance advantages. So, it's an interesting fast forwarding to quantum advantage in the form of software.
John Furrier
>> Yeah, you're pulling forward the capabilities. What I like about that is that if you think about the edge, and this is why I love what NVIDIA's doing, what you're doing is that the data coming in is new data.
Matthew Kinsella
>> Yeah, that's right.
John Furrier
>> Images, video.
Matthew Kinsella
>> Exactly.
John Furrier
>> It's a lot of volume.
Matthew Kinsella
>> A lot of data. A lot of volume.
John Furrier
>> High velocity, ton of volume. It's not trained.
Matthew Kinsella
>> No.
John Furrier
>> It's new data. It's fresh. It needs to be trained, understood, inferred, reasoned on. Agents are coming in. So, a whole wave of new intelligence software is coming.
Matthew Kinsella
>> Yep, that's exactly right.
John Furrier
>> Explain how that's all going to get... Because it sounds simple to understand, or maybe not, but it's actually complex.
Matthew Kinsella
>> Yeah. Well, your point is exactly right, and that's actually one of the bottlenecks of large language models has been training data. And we're running out of stuff to train these models on and quantum will unlock more training data. And in that you said, we're ingesting more data, those real-time, it's brand new, it hasn't trained, but also quantum computers will excel at creating synthetic data. So, new data to train these models on. And again, that's another great cross-vector of quantum and classical computing.
John Furrier
>> When you guys look at your finances, obviously, what we were just riffing on in real time in theCUBE here is that this is more headroom for you guys. You don't know what you don't know. So, how do you think about that as CEO? Obviously, a lot of R&D going on. How are you prioritizing, watching how the world turns seeing around the corner? How much of your time is thinking about these things and how do you guys approach it?
Matthew Kinsella
>> Yeah, I try to spend a lot of time imagining what the world is going to look like in the future, but having to come at it with a great degree of humbleness because no one would've anticipated what classical computing could do. Who would've thought when the Apple IIe came out that you'd have GPUs doing what they do? So, I have to understand that it will be hard to predict what humanity will do with quantum. But our strategy has always been let's focus on the things that are obvious in the near-term, and that's quantum sensing. And then, let's get these quantum computers useful enough to get them out of the developer community and then have them start to figure out-
John Furrier
>> So, basically your strategy is keep your options open, knock down what you can knock down on the commercial side, take what you can get with the market, ride that wave, and see what happens?
Matthew Kinsella
>> Yeah. And eventually, it'll become more clear as exactly what to focus on, but right now, we're still pretty early in the market development of quantum.
John Furrier
>> Okay. So, the IPO's today. Stock rang, I heard the second bell, and you guys then made the first trade. What's next? What's on the roadmap? What's the use of funds? What's the strategy? What are you optimizing for?
Matthew Kinsella
>> Well, it's really continuation of our existing strategy, maybe with some slight acceleration, but part of this is to make sure we have a war chest on the balance sheet to bring this important technology out into the world. But if I had to classify the uses of this funding, it would be in two big buckets, R&D. And R&D comes in two flavors for us. We have one, think of it as like an up and to the right, and the other is to a down into the right. The up and to the right for our computing, build more qubits, make those qubits more powerful, get to 100 logical qubits. The down and to the right is for our quantum sensing equipment. Those are already quantum-advantaged. So, it's really about making them cheaper and smaller. And so, that's the down into the right curve. And then, sales, marketing, there's a huge opportunity out there, we've got to invest behind it.
John Furrier
>> So, you got a tailwind on the sensing, get commodity type pricing for volume. Get the tech up high, kind of like NVIDIA's strategy. Kind of like a similar path.
Matthew Kinsella
>> I've told this to Jensen, we are copying their commercialization strategy. They went after graphics first in the form of gaming, and then crypto mining, then physics, and ultimately, crown jewel of large language models. We're doing clocks, QRF and ultimately, quantum computing.
John Furrier
>> Yeah, Jensen is one of my favorite interviews I've done. Andy Jassy's another one I've interviewed. Andy Jassy had an expression called, "If you want to pioneer the future..." there's so many ways he says this, but I'll paraphrase, "you have to be willing to be misunderstood for a long time."
Matthew Kinsella
>> Yeah, I like that.
John Furrier
>> Jensen kind of said that almost same thing, but in a different way. That CUDA's been around for years. This is not new for NVIDIA and just now the world's waking up. So, I guess my final question for you that what are you misunderstood about and what are you okay with?
Matthew Kinsella
>> Well, I do think there's a huge misunderstanding that quantum is just this thing that's going to happen in the future. The quantum sensing market is real, and we are addressing it in size today. And I think the other thing that is misunderstood is useful. Quantum computers are coming sooner than people anticipate. And so, we all need to prepare for Q day. We all need to prepare for the days when quantum computers are going to be able to break encryption, and we will get there, but this is happening.
John Furrier
>> And what should people do to prepare?
Matthew Kinsella
>> Well, start to get to post-quantum encryption, for sure. And unfortunately, there's this scary thing called harvest now, decrypt later, where people are stealing encrypted data and sitting on it and then we'll-
John Furrier
>> Waiting for the day. Waiting for Q day.
Matthew Kinsella
>> Yeah. So, there's not much you can do about that, but new data, you start to shift to quantum encryption.
John Furrier
>> Matt, congratulations on the IPO.
Matthew Kinsella
>> Thanks, John.
John Furrier
>> Great to see you again.
Matthew Kinsella
>> Thank you.
John Furrier
>> You got a spring in your step. It's a moment of consequence here at the NYSE. Thanks for coming in.
Matthew Kinsella
>> Thanks for having me.
John Furrier
>> Great to see you. Congratulations on your IPO. Again, another tech IPO here. theCUBE's got you covered here. The NYSE Wired program, a CUBE original. We break down the leaders between capital markets and technology. Of course, theCUBE's doing its part. I'm John Furrier. Thanks for watching.
John Furrier