Technology, Connected - Nvidia Is Building The AI-Quantum Supercomputer

Episode Date: March 8, 2026

Nvidia is treating quantum computing as the next stage of accelerated computing, not as a separate machine sitting apart from AI supercomputers. Sam Stanwyck from Nvidia and Pranav Gokhale from Infleqtion explain how NVQLink connects QPUs and GPUs with low-latency, high-bandwidth communication, allowing quantum computers, GPU supercomputers, CPUs, CUDA-Q, and AI software to work inside the same computational workflow. The conversation moves from logical qubits, quantum error correction, material science, battery design, and Hamiltonian simulation to quantum sensors in space, NASA, gravity mapping, edge GPUs, and why the first useful quantum systems may arrive as hybrid quantum-classical supercomputers.--⁠⁠Listen to every podcast⁠⁠Follow us on⁠ ⁠Instagram⁠⁠Follow us on⁠ ⁠X⁠⁠Follow Mark on⁠ ⁠LinkedIn⁠⁠Follow Jeremy on⁠ ⁠LinkedIn⁠⁠Read our⁠ ⁠Substack⁠⁠Email: ⁠[e-mail]⁠--Chapters(00:00) Trailer(01:20) Why Nvidia(02:52) NVQ-Link(09:29) Quantum computer vs the GPU(12:33) AI helping quantum(16:56) Building a space elevator (20:09) The quantum algorithm zoo (22:04) From noisy qubits to logical qubits (24:00) How much energy does a quantum computer use? (27:05) The no-cloning theorem(27:20) The biggest unanswered question in quantum computing(30:47) A $20M NASA program (33:32) What do we want humans to be?

We're never going to harness the power of nature and put it in a computer. We disagree. Today's guests, Pranav Gokal, CTO of Inflection and Sam Stanwick, group product manager for quantum computing. At Nvidia are going to help us understand why. And the central question, if the biggest company in the world are investing time and money in quantum computing, perhaps the idea isn't so silly. We get into QPUs, GPUs and the bridges between them. We learn how quantum processes are augmenting supercomputers.

We get into space gravity, NASA partnerships, qubits, space elevators, and much more. It's a heck of a ride if you have any interest in quantum computing. Stick with us. Enjoy the show. And in fact, it's so forbidden that if you could copy and paste quantum data, you could also travel faster than speed of light. And so in 2024, the entire industry of quantum computing went from packets to data. They went from noisy information to actually useful information. It means that for the first time, we can really start to envision GPU being used real-time

at the same clock cycle, if not faster, than our quantum computer. Video is an accelerated computing company, and the through line for all of that is how can we make computers more and more and more powerful and tackle harder and more important problems. What is the biggest unanswered question in quantum computing today? Disruptors and curious minds. I'm Jeremy. This is Mark. Be disruptive.

Stay curious. And keep thinking on paper. Sam, I want to start with you. Why is Nvidia investing so much time and money in quantum computing? NVIDIA is in the news for AI. People associate NVIDIA with GPUs. People used to think of NVIDIA as a gaming company. That's all true.

But none of that is fun. fundamentally what Nvidia is. InVIDIA is an accelerated computing company. And the through line for all of that is how can we make computers more and more and more powerful and tackle harder and more important problems? And so we did that when we introduced a programming model called Kuta to make GPUs useful for scientific computing and develop a new type of supercomputer. computer. We did that when we started accelerating AI for recommender systems. We did that by

partnering to create the LLMs that we're all familiar with. We're doing that now for physical AI and robotics. And we see quantum as a really important part of the future of accelerated computing. And so, of course, we want to be there. And of course, we want to invest early. And, And just like we partnered with OpenAI a long time ago, long before ChatGPT, we want to partner with the leading companies, the leading researchers developing quantum computing and figure out how in video and help them make quantum computing as successful as possible, as fast as possible. One of the big threads we get to on thinking on paper, there's a lot of themes that have emerged over the last 300 episodes, and one of those themes is the idea of bridges, right?

So when new technologies come out, it has to be rooted or connected in something that people have a firm grasp on. And that's why I'm so excited to talk about what you guys are doing specifically together, Nvidia and Infliction, with something called, I believe it's NVQ link. So talk us through what that is. Quantum computers and quantum technology aren't going to exist in isolation and be built in isolation. It's never going to be your AI supercomputer over here and your quantum computer over here, doing something completely different, they're going to be integrated and complementary technologies. And so a quantum computer is going to not just be a quantum computer, it's going to expand

the space of problems a supercomputer can solve, create a new supercomputer. And to realize that vision, we have to co-design and build them together and tightly couple them so that you can have a workflow that part of it is running on your AI supercomputer, CPUs and GPUs, part of it is running on your quantum computer, and you're not bottlenecked by needing to send data over the internet or something. And so we developed NVQLink to solve that problem. The name NVQ Link is analogous to a technology we already developed called NVLink. And what NVLink did is make it possible to treat a whole supercomputer like a single GPU

and use it to run a problem where you're not bottlenecked by the bandwidth. And NVQ Link extends that to quantum. And so it's a low latency, high bandwidth, universal interface, architecture, interconnect to connect a quantum computer to a GPU supercomputer, AI supercomputer, and get the most out of both. So what do you do on the quantum computing side with that handoff and what makes it so special? Yeah, sure. and I'll maybe start by just continuing the thread from the previous show you had with our CEO Mac Concella, just to refresh that inflection, we build quantum technology out of neutral atom,

qubits or quantum bits. I have a little prop for those who end up watching this on video. This is our ultra-high vacuum glass cell that we manufacture. And this is basically the core, the heartbeat of every technology platform that we build. Whether it's the atomic clocks and the other quantum sensors, maybe we'll chat more about those, or the quantum computer, the QPU. And I really like the word you used of bridge because NVQLink is that bridge

from doing computation on QPU to doing it on GPU and going back and forth. One of the really important pieces of how quantum computing is going to come out to industry, to scientific discovery, etc., is exactly what Sam said, which is that it's going to be co-processing alongside GPU.

And until recently, there were open questions of how do you manage things like the latency challenge? To get a quantum computer to co-process in real time with the GPU, you need to have really fast round-trip communication. And so what Sam's team at NVIDA has done is shown four microsecond of round-trip latency between the GPU and QPU. And that's a very impressive number. It means that for the first time,

we can really start to envision GPU being used real time at the same clock cycle, if not faster, than our quantum computer. And so where the quantum computer fits in is that there's certain problems, not all problems, but certain applications that run exponentially faster on the QPU. Just to give you an example, if we want to simulate the structure of a molecule or how that molecule evolves, which in turn is really industrially important for drug discovery, for material science, and so forth, that problem is fundamentally hard for a normal type of computer, whether it's a GPU or a CPU. But it turns out to be much easier for a quantum computer. So when you think about these application workflows that

are coming in, we're going to be able to slice and dice them so that the pieces that are really good for the quantum computer go to the QPU, like the one that inflection is building. And the other pieces are likely going to go to GPU, which is a large amount of computation, things that are big data problems, things that involve very large parallel workflows. Those are incredible for GPU and really bad for QPU and it's really the combination of them with NVQLink that makes these problems start to be addressable. Will that be a computer or a person who decides where they go? Where does the workload, which workload goes to the QPU and which goes to the GPU? It'll be software and this is a really important thing because the pace of clock cycle speed that quantum computers and GPUs

are running at means that it has to be done real time with very fast logic. And, And maybe this is a good time to just reflect on in December of 2024, inflection teamed up with Nvidia to announce the world's first ever realization of a material science application using a bit of technical jargon, but logical qubits, basically usable qubits. And that was an exemplar of a workflow that involves software kind of slicing and dicing pieces that go to GPU versus QPU in real time. In fact, this paper that we put out was just published a month or two. to you go. And in a nutshell, what the software does is there's two levels of abstraction.

What is, even before we start to run the problem, we want to be damn sure that it's going to run successfully. And so that's where we have kind of a handoff with the GPU stack for simulating glimpses of what the computer is going to do and make sure that runs effectively. And that's kind of a, that's through this KutaQ software stack, which maybe we can touch on where NVAs enabled GPUs to be used to simulate quantum computers very efficiently. And then when the problem actually runs, there's actually multiple strands where a GPU and QPU need to co-process. One of them, just to give an example, is called decoding. And this is where every time the quantum computer runs, you get a result out of it. But to make sense of that or to decode

it, you need to pass that to GPU. And that's a software stack decision where rather than me sitting in the middle and slowing down traffic, software can do a very fast, switching between left and right path A versus path B of how to run the next step on the quantum computer. And so that is very low latency software that can now run in four microseconds round trip, which is very fast. I'd add to that from the absolutely right. And from the user perspective, the people who are going to be getting value from these systems are chemists, biologists, physicists and, you know, they're not going to be saying, oh, you know, run this part on the GPU, run this part on the QPU. They're going to be saying, how do I solve this type of problem?

And so there is, you know, just like there is today for AI, where when you, you know, prompt a chatbot, you don't kind of specify where it's running. There's a compiler stack that, that ProNo and software stack that Pronov talked about, which is, you know, has to be very thoughtfully designed. It's one of the most important parts of our partnership. And then there's probably AI on top. So you're probably talking to the computer in English or whatever your language is.

But at the top level, it's probably going to be a scientist talking to an AI, talking to this hybrid system and figuring out the best resources for the job. That's a symbiotic relationship. That is AI helping quantum, quantum helping AI. They're working together. How does this change the AI landscape? A quantum computer is a completely new type of computer physically.

It's quite an amazing scientific instrument. But to get the most out of it is a lot of work. So because of the quantum nature of quantum computers, errors are unavoidable at much, much, much higher rates than the computers we're used to. make. And so this concept called quantum error correction is like as fundamental as the quantum computer itself for getting this to work. And there's been incredibly exciting progress in using AI to solve quantum error correction. So in real time, as the quantum computer is doing something without interrupting the flow, figuring out what errors have been made, diagnosing what corrections

need to be made and updating it all while the quantum computer keeps going. So you can take this noisy error-prone but amazing thing and turn it into something that can run for hours or days without having an issue. And then as you go up, it's everywhere. How do you calibrate a quantum processor? How do you control a quantum processor? The algorithms that are typically going to be best for for a quantum processor often involves AI on the classical side. That's what the GPUs are doing. And then all the way at the top,

discovering new quantum algorithms is a great use case for AI and programming quantum computers is a great use case for AI. That's a lot of the value we're bringing, but then we're also very excited. We see value in what quantum can do

to enhance AI as well. If you guys could humor me a little bit and let's step back before we add the AI jet fuel on the top of this. Let's walk through how this workload separation and coming back together may look for a researcher. So Mark is a researcher. He's got a pretty challenging problem. He's trying to fix. He knows classical compute could help with some aspects.

He's heard quantum compute could help with some aspects. He's going to jump in and use your technology. Can you walk us through from, hey, here's the problem statement. and here's the separation of workflow. Here's how it comes back together. Can we try to simplify that as much as possible? Let me make it one step even more specific to now I'm going to make Mark a material science researcher.

And so one of the things... I want to build a space elevator. But you're building a space elevator, perfect. So it's a bit strong. Awesome. So for that space elevator, you're probably going to want batteries because that space elevator

is sometimes going to be on the other side. of the Earth way from the sun, it's not always going to have solar power. And so you want to be able to store the electric inputs, the solar inputs for 24 hours before the sun comes back or 12 hours. And so one of the frontiers of where quantum computing and GPU are coming together is for designing batteries that last much longer in a much more small form factor. And the reason that right now you can't just computationally simulate that is because when you, you really boil down to the computational problem, it involves simulation of electrons. So really, what Mark is going to have is a description at the level of electrons, not so

dissimilar from your high school chemistry class when you learned about carbon and other atoms having a certain number of valence electrons, you're going to have that picture in front of you, but for right now, you're kind of stuck. You can't really do anything computationally with it because simulating those electrons needs a quantum computer. And so what, mark is going to be able to do with this emerging workflow of quantum computers and GPU and CPU coming together is to take that problem of there's this potential material for battery that lost 10 times longer, et cetera, pass it off to an application software front end. And that application software front end is going to be able to break this problem into two pieces. Now,

this is getting into a little bit of jargon, but there's something called GW theory, which is basically how these structures of molecules interact with each other. And some of that doesn't need quantum mechanics because it abstracts the way those electrons. And so that runs on existing GPU software stacks, hardware stacks that are really good at maybe that 95% resolution of that problem. But unfortunately, you need that 5% or fortunately for us, you need that 5% extra. And that's where specifically there's a subroutine of quantum computing called Hamiltonian simulation. I promise it's the most jargon I'll give you at that level, but Hamiltonian simulation is kind of like a key primitive for quantum computing.

It's sort of like Google Search is based on page rank, or the Netflix sorting algorithm has an algorithmic primitive behind it to show you the best content on Netflix. That algorithmic primitive is going to run on a quantum computer, and then you're going to have a orchestrator workflow that performs consistency between the two. So it's a maybe slightly more technical response than necessary, but the high level is there's specific applications in material science, chemistry, drug discovery, etc., where there's known primitives that work really fast in a quantum computer, and everything else is already really good and good enough on the world's biggest GPU clusters, et cetera. And then finally, the thing I'd add is consistent with what Sam said.

In the past, we might have needed to have six months of application engineers build. building out very specific, like drag-and-drop menus and software tool flows for building all this slicing and dicing of the problem. A lot of this is now as easy as running opening eye codex or cloud code, which under the hood, of course, runs on NVIDEA stack so that we're able to reach more applications than ever easily thought before in the click of a button or a few prompts, which is changing everything about how we're programming these applications. Quantum compute is often linked to material science, and it's going to create these new

materials. Could that be tomorrow? Could that be in 20 years? Like how long before I can build my space elevator with a new material that quantum helps discover? Historically, one of the critiques of quantum is that it's always been five years away. I generally think it's different this time. And the reason is that for the first time ever, with credit to Google for being the pioneer here, we have as an industry stumbled upon, not just stumbled, engineered our way to what is called a logical cubit. And I guess the quick summary of what is the logical qubit is it's kind of like when you're using Wi-Fi or 5G, it's there you get packets into your phone or your laptop or router, but those packets have error. And that's kind of like the cubits that we had pre-20204. You have them,

but they were error-prone and you couldn't just use them raw. And of course, Wi-Fi and 5G end up giving you pristine actual information, 5 megabits per second of download speeds, etc, or 10-mecabits per second. And that's the actual data that is pristine that you get out at the end of the day. And so in 2024, the entire industry of quantum computing went from packets to data. They went from noisy information to actually useful information. And so today, very few companies have crossed this chasm. Infliction is one of them. And in fact, the first time we did it was this paper that we put out with NVIDIA showing two logical qubits. Two is not a lot, but it's a lot more than zero, which was what we had for the first 35 years of quantum computing. And so last year in

September, we've now hit 12 logical qubits. Our publicly announced road map has us getting to 30 logical qubits this year in 2026 and 100 logical qubits in year 2008. So to answer your question head on, we think that at 100 logical cubits, you reach this tipping point towards some of the first applications. Not going to be every application, but some of the applications, especially to fields like material science. And so around the 2028 time frame is when we start to think that this flips from purely being kind of researchers testing this to understand how to maximize the juice of the future machines to actually getting juicing those future machines. And that's about year 2020. And the last thing I'll say is there's upside surprise that has emerged

in our field because of AI, because things that used to take six months, honestly for me just to figure out how to clone a repository of code with games. or GitHub, that's now the press of button. And so there might be room for upside surprise too, but in general, we think that 2028 is a realistic time frame. Maybe this is a good time to mention that Nvidia doesn't build its own quantum computer. And what we do is partner with companies like inflection to try to pull in that roadmap as much as possible. We don't think about it in terms of when is it going to happen. I don't know if there will be one moment, but how can we make everything happen faster.

Pranav, what you said a little bit ago, as we were trying to parse, you know, what goes to quantum and what stays in classical land and used the two words known primitives. And it related to the quantum computing side, is that like, is that, could we think about that in this nomenclay, this word might be wrong, but like as a template of a process that is known to be successfully completed in a quantum state versus not? Yeah, that's a good way to think about it. And there's many of these templates. In fact, it's grown to be so wild that there's even a website called the quantum algorithm zoo. And zoo is the right term because there's just a muck of different algorithms that are

always being invented. There's also competition where classical traditional competing technologies come up with their own algorithms to sort of fight back. But there's some that have really withstood the test of time, some templates, if you will, to use your expression. And there are ones where the most fundamental research in math and computer science has shown that you can never build a classical computer that can beat this. And it comes down to the most mind-boggling, almost philosophical differences between quantum physics and neural physics, just to give you one to perhaps scratch a fun side of the brain for the viewers. There's something in quantum computing called the no-cloning theorem, which says that you can't copy and paste quantum data.

data. And that's just bizarre because for any software engineer in the world, or even just someone using Microsoft Word or Microsoft Excel, copy paste is like the most fundamental thing that we do. Everyone knows the keyboard shortcut for it, et cetera. But in quantum computing, it is forbidden. And in fact, it's so forbidden that if you could copy and paste quantum data, you could also travel faster than speed of light. And so these kinds of special rules of the quantum world, are what endow these templates, these algorithms, these primitives, with certain confidence that there's never going to be a classical algorithm that's going to beat it. It's always going to be secure for a computer.

I want to go back to the NVIDIA role in this and the QPUs and the GPUs. Jensen Huang said, quote, every NVIDIA GPU supercomputer will be a hybrid quantum classical at some point in the not too distant future. There's a lot of traditional GPUs out there. First of all, what happens to the manufacturing infrastructure that exists today when everything changes? The most fundamental point is the GPU supercomputer doesn't go away when the QPU is added. It's an augmentation, not a replacement. And so GPUs are very, very, very good for a lot of problems.

The set of problems that quantum computers are great at is almost completely different. They're actually surprisingly fortuitously complementary technologies. Quantum computers are amazing, high precision simulators of nature. GPUs are data parallel, high throughput AI, matrix multiplication machines. And so they're, you know, they're complementary technologies and the way we're thinking about it is we keep doing exactly what we're doing with GPU supercomputers, scale them up, scale them out, make them better. But now we have this brand new resource, which is an amazing resource and can do completely

different things. And so it will extend these supercomputers to be able to solve a whole different types of problems. GPUs notorious for their consumption. Will these QPUs require much energy? How will it compare to the H-100s that are running today? Our quantum computer uses about 25 kilowatts of power, which is a small, small number in relative scheme. It's about 10 hair dryers worth of power.

So if you're going to home, if your home can turn on a few hair dryers, you're almost all way there. That's going to stay the case, even at much larger numbers of qubits. Right now we have done 1,600 cubits, physical qubits, which is a commercial record, and our future systems are going to have even more. But the power consumption will stay pretty close to 25 kilowatts, maybe a few kilowatts more, but not fundamentally different. So this is one of the exciting things about quantum computing is that some of these problems

that we're talking about, material science, chemistry, etc., we can address at very cheap power costs. Now again, it's going to need to sit alongside GP clusters, which are actually incredibly energy efficient, especially relative to previous ones. And maybe one kind of twist I can throw in here in addition is, as you viewers may have heard from the podcast suited with Matt Kinsella, there's also quantum sensors that reflection is building. And those quantum sensors need to be at the edge. So they need to be on airplanes and drones and other devices that don't have the luxury of having. a power grid connected. And even those need GPU next door to have the classical compute side handled in real time. And this is maybe a good chance to pull out my second prop, which is this is a NVIDIA Jetson edge GPU. So this is similar to the capabilities of gigantic GPUs that existed just a few years prior, but it now fits in a very small form factor in my palm and runs on a very

small amount of power. And so I think the GPU industry has also made phenomenal progress in becoming very, very energy efficient in terms of per floating point operation, et cetera. And so these are pretty convergent paths in my view. Just for the listeners, if you're listening on Spotify, go to our YouTube channel to see the props that Pranav has bought in. I think Pranav hit on the key word or the key term, which is energy efficiency. So how much energy are you using and how efficient are you being relative to the importance of the problem that you want to solve? And so, you know, a GPU supercomputer uses a lot of energy for training a large language model. If you try to do the same thing on a bunch of CPUs, it would use way, way more. And, you know, we've,

we've all decided that the value that you get from that training is worth the energy cost. And so we are, we're always looking at how to make things more energy efficient and that's done by using the right tool for the job. So training an LLM you need a supercomputer for inference on the edge. You don't. You need Jetson or Spark or or something else. And quantum computers are amazing at energy efficiency for the problems that they solve. I'm aware that you have to jump in viable 10 Sam. So we're going to do a little timed round of questions and you have 30 seconds or he's wanting to speak about. the concept for 30 seconds.

Sam, there'll be a couple for you as well. So 30 seconds, error correction. Go. So error correction is the major pivot point that's happened in quantum competing in the last three years or so in terms of what's become experimentally viable. It's basically taking, let's say, 10 cubits

that are B plus cubits and turning them into one cubit that is A plus. And that's by taking the errors that you find in the B plus, you're grading the papers on their test scores or whatever, and then combining the best of them to get that A plus qubit. That brings us to what are called logical cubits. And getting to 100 logical cubits is the inflection point to really important applications quantum computing. Today we're at 12. 100 is coming soon.

QPU's data centers in space. Honestly, I don't know why that has to happen. at least at least near term the the the first the first useful QPUs will be as parts of supercomputers and the most important problems to solve for for both are scale and fidelity and and speed of the of the quantum processor and you know the the the problems that are solved by putting them in space are down the road a bit. And so, you know, maybe it's an, it's an evolution where we start with with supercomputers. We move to commercial data centers and then we have to go to space. And maybe it's less the data center and more quantum sensors combined with edge systems that make a ton of sense to be in space.

Pranav, back to you. What, in your opinion, is the biggest unanswered question in quantum computing today? It's a really good question. My gut instinct is what is the best way to combine quantum computers with quantum sensors? Everything about classical computing is combined with sensors. We just don't necessarily talk about it that way. But today, you and I and Sam are on this call because of Wi-Fi cards that are in our laptops. And what is that going to look like for quantum computers? What does quantum Wi-Fi look like with a quantum RF sensor built into your quantum computer? I think that's a really important part of how this field is going to involve. Sam, is there a Moore's law equivalent for QPUs?

I think we're starting to see it. And it's one of those things that only time will tell. And what's exciting is we're now scaling logical QPUs. And so, you know, inflection is at 12, targeting 100 or more in a couple years. There are other companies who, you know, have Google for one, who have also demonstrated logical cubits and are scaling them up. And so we'll see. And, you know, at least based on the trend in the last couple years, what's really exciting is it seems to be much faster than doubles every year or doubles every year and a half.

And Jeremy, I'll let you ask Panana have a final quantum question. I know you've got some burning away there in your head. You've got an expert for 30 seconds. All right. This might take a little longer in 30 seconds, but I'll do my best. You guys recently had a pretty awesome announcement about putting a

very particular sensor into space related to quantum gravity and for measuring things. So quantum sensors can measure really small things, right? So is this thing designed to point back to the earth and measure the effects of earth on gravity? Like, is that where we're headed?

Because if so, that's pretty interesting. Explain it to me a little bit. Yes, and thank you for giving me an excuse to pull out my final prop. I wasn't sure if I get a cancer race flat. For the listeners, what I'm holding is a new type of glass cell, which is the type that we'll be setting to space. We just announced on Monday a $20 million program with NASA where we're sending this quantum gravity radiometer.

I'll come back to what that means. But what this is is a new type of welding together titanium with glass, which is an incredibly challenging thing to do. We use something called an ultra-fast laser system to do that, but we can actually bond together the titanium with the glass. The reason that matters is these are going to go on a spaceship that has to withstand tremendous launch pressures. It is very hard to build things that withstand that launch pressure,

especially sensitive delicate objects like sensors. On top of that, it needs to have a really good vacuum. So even in space, which itself is a vacuum, you have an even better vacuum within this glass piece. So back to what we're doing in space, it is exactly the case that we're going to be measuring gravity from satellites. And the reason this matters is that as there's changes on Earth or underground, where we can't observe, that has impacts on the direction of the era of gravity.

Is it slightly this way or slightly this way, left or right? And for instance, when aquifers are depleted, which is really important for farmers, they plan out agriculture, it's helpful for them to know that in advance, how there's going to be changes to the water table. And that's just one example. There's many, including ones that are important from national security perspective, ones that have relevance to mining and critical minerals, et cetera. So having this observation capability,

which has the potential to be much more sensitive than traditional sensors, is a real breakthrough. And we're excited to work with NASA to put this in space. Yeah, that's pretty cool. That's maybe like a telescope for underground. All roads converge on quantum, Jeremy. That's what it feels like to me. All roads lead to quantum in the end.

Brilliant. Thank you for that. Gentlemen, any questions that we haven't asked you that you're dying to answer or you wish someone would ask you? Maybe I can just make the final reflection that the field is, as you've pointed out, moving really fast. And it is in large part because of how much AI and the underlying GP compute technology have accelerated things. Yeah, excited to see where the next year evolves to in terms of connecting the dots from GPU and AI to Kwan. I'm closing thoughts for yourself. We're very excited about the concrete applications of quantum technology that we know and understand. We're also just excited about the process of solving a really hard problem and being curious and going places humans haven't gone before and the benefits that always come from that.

And so, you know, the same reason we looked at an ocean where we didn't know what was on the other. other side and it was probably death and decided to go. The same reason we climbed Mount Everest, the same reason we went to the moon. We want to understand and control and harness quantum technology and build computers out of it because it's the hardest thing we can imagine to do. And there's valuable applications that we're aware of and now confident in, but we expect it that there's going to be a whole bunch of other things and a whole bunch of other benefits that were, that we're impossible to foresee. So it's a really exciting time in quantum all around.

The George Mallory of technology, because it's there. I love that. So we always end with Kevin Kelly. He let us a question. We ask every guest, what do we want humans to be? And how does technology help us get there? I think every momentous shift in humanity has been on the backs of new physics. We started with classical mechanics for levers and pulleys that built

ancient civilization. Then we got to thermodynamics, which built steam engines and trains, et cetera. Then we got to electrovagantism, which unlocked the internet age, computers, radio communications, et cetera. But the final frontier that humanity has not unlocked is what can be built with quantum physics and quantum mechanics. And so that's what makes me incredibly excited to come to work every day at inflection and build towards a future where that is now in the arsenal, the toolbox of humanity. Guys, thanks for joining us. Great discussion. please keep us in the loop on how work is going with NVQ link and inflection. I want to know more about the quantum gravity sensor up in space.

So Pranav, please keep me posted on that. We'll do. And thanks so much for having us, Jeremy, Mark. Thank you. And you can learn all about gravity, quantum, and invidia inflection.com. Thinking on paper is available wherever you're listening to it. And if you've been listening to this and you've got questions, write them in the comments. This is an interactive show.

Me and Jeremy will answer them or better we'll get Pranav and Sam to answer them. We've got some awesome guests coming up, Jamie. We're talking about hotels in space. We're talking about the philosophy of AI. We're talking about moon mining again. So there's a lot of cool things happening. But until next week, be disruptive. Stay curious.

Keep thinking on paper.