Technology, Connected - IBM Starling & Fault Tolerant Quantum Computing - Oliver Dial, IBM Quantum CTO

Episode Date: June 17, 2025

IBM has announced a $30 billion investment in quantum computing. Today we ask why. In this episode, Oliver Dial, CTO of IBM Quantum, speaks with Jeremy and Mark from the Think Lab in New York.This is the inside story of how IBM plans to make quantum useful and how quantum computing could reshape material science, chemistry, and the architecture of technology itself. It's all written in their roadmap. Starling in 2029, fault tolerant quantum computingPlease enjoy the show.And subscribe if you haven't. Cheers, Mark and Jeremy--Links and resources:IBM Quantum: ⁠https://quantum.ibm.com/⁠Starling press release: ⁠https://newsroom.ibm.com/2025-06-10-I...⁠Blog: ⁠https://www.ibm.com/quantum/blog/larg...⁠IBM Roadmap updates: ⁠   • 2025 IBM Quantum Roadmap update  ⁠Follow Thinking On Paper:Thinking On Paper: ⁠https://www.thinkingonpaper.xyz/⁠--Chapters(00:00) IBM's Quantum Investment(02:11) The IBM Roadmap to Quantum Advantage(05:00) Error Mitigation vs. Fault Tolerance(07:47) The Hybrid Quantum-Classical Approach(10:18) Physical vs. Logical Qubits(12:45) Magic States and Universal Quantum Computing(15:15) Loon, Kookaburra & Future IBM Developments and Milestones(17:12) The Future of IBM Quantum Computing(19:11) A Day in the Life of an IBM Quantum CTO(21:25) Building a Quantum Workforce(23:37) The Intersection of AI and Quantum(25:40) Practical Applications of Quantum Technology(28:02) Why Quantum Matters to Humanity

Disruptors and Curious Minds, CEOs, founders, technology lovers. Welcome to Thinking on Paper. I'm Mark. This is Jeremy. And this is Pocket Edition. A shorter, more concise exploration of a single technology news story of the moment. And we have a very special guest from a very special location today. IBM making the most powerful quantum computers on the planet.

They've just announced a $150 billion investment in technology as a whole in America. A 30 billion of that directed into their quantum program. the goal, fault-tolerant, scalable quantum computers by 2029, the Starling Quantum Chip. And today we have Oliver Dial, who is the chief technology officer at IBM Quantum. Welcome to Thinking on Paper, Oliver. Thank you very much for thinking on paper with us today. Where are you? And what is that behind you, Oliver?

So I'm at the IBM Research Headquarters in York County Heights, New York. And I'm actually in our Think Lab, which is a technology showcase that we use with clients and researchers from around the And behind me, we actually have IBM's quantum system two. This is the biggest, the most powerful, the most sophisticated quantum computer anywhere in the world. And it's something that we're actually really excited that it's not just here in Yorktown Heights, New York anymore, but it's something that we can actually put on premises with and with interested researchers all around the planet. Very exciting. And Jeremy, I'm sure I'll let you go first. But last week we had Katia Moscovich on it. She spoke about the first time she saw a quantum computer. It was in IBM. In Australia, it was a big golden chandelier. That doesn't look like that. So the chandelier that everyone loves to talk about is actually what's inside of this. The quantum cubitor itself needs to be protected from the world.

We're trying to do computation with some of the most sensitive electronic circuitry on the planet. And so we need to keep light away from it. We need to keep radiation away from it. And we actually cool it to very close to absolute zero to get that circuitry to work. So if you gave me two or three days to warm this system up and let the air back in, we could open the door and show it to you. But I don't think that the researchers using it would be very happy with me. Fair enough. Fair enough.

Well, let's talk about this announcement that just came out. And some of the things that jump out to me, there's been a lot of great research and innovation in quantum, especially as of late. I think there's a exponential scale that we're starting to hit with a lot of the exciting work that you guys have been doing for a long, long time. What jumped out at me is the roadmap here you announced will actually have these quantum machines being 20,000 times more. powerful. And in order to, like, show what this system could do with today's computers, we need did I see 10 to the 48th time size memory just to demonstrate what the possibility of these things could be? I mean, those blow my mind a little bit. Is that somewhat accurate? That's actually very accurate. That the reason why people are interested to invest in quantum computers is there are some problems

that scale exponentially on classical computer. Every time you take the problem, make it just a little bit bigger, gets twice as hard for the classical computer. For some, Some of those problems, they don't scale exponentially on quantum computers. So although these things are slow and finicky and expensive, which trust me is somebody that works with these things every day, all three of those are true. The fact that there are some problems that are simply unsolvable with the classical computer that are possible with a quantum computer justifies all the investment that everyone's making in these things. The cool thing about this announcement too, and there's a lot of interdependencies with this stuff, I realize. I mean, this is very tricky technology to be able to put forth. But IBM has put a flag in the ground and said, hey, here's where we're headed in 2029.

And we believe we have the steps in our roadmap to get to this computational superpower down the road. So step us through the roadmap a little bit and tell us why you guys are so confident in these steps that you've laid out. So we've actually been keeping published roadmaps for a number of years as part of making it clear to people that we kind of know where we're going and what we think is reasonable because there is honestly a lot of height in this space. There are two parts to a roadmap. One is near-term devices. We passed a milestone a couple of years ago that we call quantum utility at the point where you can no longer directly simulate these quantum computers on a classical computer. And if you think about it, that's the bare minimum bar.

Until you can hit that point, then you'd be better off using a classical computers. It's cheaper. In the next couple of years, we're going to be showing advantage. And we're going to be doing that with systems like the quantum system two behind me. What makes that possible are a set of techniques called air mitigation. Quantum computers make a lot of mistakes. typical error rate for operations is someplace between one in 1,000 and 1 in 10,000. So imagine trying to do a computation on a computer that one in a thousand calculations is just

wrong. It's a ridiculously high error rate. How does that error rate compare to classical computers? I don't actually have the air rate for a transistor off the top of my head. It's something on the order of 10 to the minus 15. Enough orders of magnitude between them that it's just an unfathom old number. In practice on your classical processor, you typically don't do anything to create. correct for the errors other than maybe in the memory and the communication. So to do calculations today, we use something called air mitigation. And what that means is we run the same calculation a lot of times. You run small variants of the same calculation. And we can use that to statistically correct the errors. So even though each individual run had mistakes in it at the end of the day, we can tell you

what the answer would have been without those. That process is pretty expensive. You end up needing to run it a exponential number of times in the size of the circuit. And so you have the same overall scaling that you do with a classical computer, but the pre-factors better, so you end up winning. So this is sort of something that you can do to get into the game. We think it's going to work well enough that we'll show quantum advantage this year or next year. And that would be the point where we're doing something better, faster, cheaper on the quantum computer than we can do on a classical computer. So is that what you mean by quantum advantage?

Is that essentially the definition of that? We talked about quantum utility first and now quantum advantage? Yep. Quantum utility, something that you can't directly simulate, quantum advantage, something done better. There was a quote in the new announcement. So, quote, advancing error mitigation capabilities will be essential for reaching advantage before fault tolerance. So for our regular listeners who aren't into quantum computers, what does that sentence actually mean? What is fault tolerance?

It was a big highlight in a lot of the announcement, fault tolerant, fault tolerance. Could you unpack that for us, please? So air mitigation, we're correcting the air statistically after the fact. Fault tolerance, you correct them as you run. And that is such a huge difference because it means the errors aren't compounding over the course of the computation. Fixing them as you do, the calculation gives you ability to go to enormously larger circuits. That's that factor of 20,000 that we're talking about in 2029 with Starling is the difference between air mitigation, recting after the fact, fault tolerance, recting as you go. And that really shows up in our roadmap as an absolute step function in what these machines are possible or capable of.

So in quantum, I see a lot of quantum and classical bridging, meaning, you know, you kind of get something happening in the quantum world and you bounce it to classical to take advantage of that. Is the error correction, the stuff you're talking about error correction mitigation bouncing back to classical or is there still staying in the quantum realm? So it's a mixture of quantum and classical that when you're doing this correction of the errors on the fly as you go, you actually need a substantial classical computer to work out what the errors were and to correct the calculation going forward. and you have to do that real time. So the classical computer has to be able to keep up with the quantum computer while you're doing that. This is, if you look at our roadmap, what we're talking about when we say real time decoding in 2026 is demonstrating the capability to have that classical part of the compute keep up. But there's more bouncing back and forth than that.

For both the advantage applications and for these long-term fault-tolerant applications, anything you can do on a classical computer, you should do on a classical computer. We really want to reserve the quantum computer, this pressless resource, for just the parts of the problem that it's really the only thing that it can solve. And so this concept of tied together a big quantum computer with classical high-performance computing is just a super important part of this technology going forward. It's something we talk about when we talk about quantum-centric supercomputing. When you say it's a hybrid classical quantum, is a ratio, as a percentage, is it possible to

say how much goes to which? Geez, there's such different resources. It's kind of hard to put your finger on that. One application a lot of people like right now for advantage is something called SQD. It's a technique for simulating chemistry. And in it, you use the quantum computer to choose what we call a basis, to choose basically how you represent the electrons in the chemical, but then use the classical computer to actually work out what the energy levels are.

And the reason why it works really well is the classical computer, when it does that actual calculation of the energies, does that calculation accurately. And so you don't need to correct for the kind of faults on the fly like you normally would. And so it's sort of an interesting example of air mitigation. In that, we're typically seeing runtimes on the quantum computers that are measured in hours and runtimes on the classical computers that are also measured in a few hours. But you can trade them off against each other a little bit. If you have a lower noise quantum computer, you can also make the classical part cheaper.

If you have a noise, your quantum computer, you can make up for that by throwing additional classical supercomputing power at it. That's part of why it's such a neat application in the near term that you can trade the two off. Very cool. Now, I know we don't want to get down into the nitty-gritty of cubits and in and of themselves, but tell us, talk to us a little bit about the physical to logical qubit thing and how you guys are connecting them, connecting these to scale the performance. You can't say connecting, Jeremy.

You have to like entangle is the new-that's right. That's right. Yeah, yeah, yeah, yeah. Oh, the world of quantum puns is just so huge. So much, so much. What's your favorite? I don't know. I'm neither here nor there on that one.

Wow. Okay, that got a groan. Well, at least we got it. It means we're learning something, Jeremy. That's right. That's right. Okay, so when we're doing large-scale fault-tolerant quantum computing, we're doing this quantum error correction. The way we correct the errors is we use more than one physical cubit to store each logical cubit. And that lets us do parity checks. We can compare the values on the physical cubits and see if one of them is glit and fix it. The ratio of the number of physical cubits you need to the number of logical cubits you get, people call the rate of the code.

and it's super important, because the bigger that ratio it is, the harder it is to build the quantum computer that you want. If you look at the best air correcting codes people were talking about a couple of years ago for a useful logical cubit, you need it on the order of a thousand physical cubits per logical cubit. To put that in perspective, this big machine behind me right now, that guy has about 450 cubits in it. So you needed something about twice as big as that in terms of number of cubits to store even one logical cubit. and one logical qubit is useless. I can simulate that on my iPhone. You really need about 100 of them

before you're even doing anything interesting. So part of this announcement was an architecture for this computer built with a code that's about 10 times more efficient. And so that means for this code to store, in this case, a dozen logical cubits that come in 12 packs, we need about 400 physical cubits.

So again, referring to the system behind me, that's about a 12 pack of cubits. If I had about 10 of those systems, I would have something that's more powerful, than the most powerful classical computer in the world. Now, to put that in perspective, this isn't the lobby of our building, right? When people are talking about large-scale fault-tolerant quantum computers, a lot of time you'll see pictures of like a football field

and say, if we fill this with a quantum computer, we can make this work. And I'm telling you, it's a few times bigger than this guy we've got in the lobby. And that's why we're saying we're so excited about this because it seems practical, right? That this is not a piece of science fiction to make this into reality. It's still a bunch of really difficult engineering problems. But it's engineering problems that we're confident we can tackle, and we can tackle them on a timeline. So physical and logical cubits, the physical cubits are behind you, tucked into this nice little arrangement that you guys have a protective environment for them. And these logical ones,

is that where the classical quantum bridge lives? Because they're simulated, right? So am I thinking about that right? You're on the right path here. So each logical cubit is made up of a bunch of physical cubits, and together we're using them to correct the errors on each other. So there's the quantum part of it, which is that collection of cubits. And then there's a classical part of it measuring all these parodies constantly figuring out what errors happened and correcting the calculation for those errors. And so it's this interplay between this real-time decoding and this bucket of physical cubits that makes 12 logical qubits. And the logical ones are doing double checks on the physical ones, kind of going,

hey, they're wrong, real-time error correction, kind of making sure they stay on the tracks, right? Yeah, that's pretty good. I mean, imagine I was trying to seduce you data. and there were a lot of mistakes. One thing I could do is instead of sending each number once, I could send it to you three times. You could look at it and you could say, well, you were trying to send me seven because I see here seven, seven, six. The six is probably wrong. So in this, the numbers that I'm sending you would be equivalent to the physical cubits are kind of extra copies of things, but quantum says you can't copy, so it's a little bit more complicated.

And then the logical cubit is a group of three together that you can then figure out what mistakes I made. That makes sense. That makes sense. What can you tell me about a magic state? Because that seems to me like we got pulled into quantum mechanics a while back and just are so enthralled by what quantum is that is so different from what we see in the real world. So the magic state, you know, tell us about that. Yeah.

There are some quantum computations that are easy to simulate. And again, if you can simulate it on a classical computer, you should use a classical computer. And it turns out for each one of these air correcting codes, whether it's the service code, that old one or the gross code, our new one, there's some set of gates that are easy to do. They're called transversal. You can do them without unpacking the numbers, basically. For all of these codes, the set of gates that you can do for simple codes are also easy to classically simulate. So I've gone to all this work to make this air-corrected quantum computer, but at the end of the day, the only complications they can do are ones that you can run on your laptop. You need one extra ingredient that comes from outside of the

air-correcting code to make what we call a universal quantum computer. Universal quantum computer is one that can run any quantum algorithm. And the most well-known way of doing that is something called magic states. And it's a way of adding sort of one extra operation that comes from outside of the encoded space that lets you lift you up into this magical regime of being able to do these super complicated calculations. And so the way that you do that is you do prepare the magic state on physical cubits. And then you keep on protecting it a little bit more. You build it into a small air correcting codes. You fix some mistakes. You build it in a vigour air-correcting code. You fix some mistakes until ultimately get it into the air-correcting code that you want it to be in. So the,

A capability of protecting data, logical memory, doing these easy computations. In our case, they're called Clifford computations. They give it Clifford, the big red dog, easy. And then adding these magic states to get you to the universal computation are sort of the three super important steps for going from a physics experiment to a computer. And we have a plan for all three of those. And that plan is Starling by 29. Blue Jay by, is Blue Jay the end goal?

Well, Blue Jay is as far in the future as we feel confident we can plan. The end goal suggests that someday we're going to be done and we'll have made the final quantum computer. We'll just be like, yep, that was fun. Let's go do something else. Like we think these things are going to keep getting more powerful, more complicated, cheaper over time. But yeah, Blue Jay, 233. But it's kind of important to realize we're not just saying, well, we're going to sit here and work really hard in 2029. We're going to pull a sheet back and show this to you. We have some steps along the way that we want to demonstrate to show to the world that this is not just. a real plan and a practical plan, but they were executing on it. So later this year, we're going to have a device called Loon. It's being worked on by a guy we call the chief lunatic, actually. And the job of Loon is to demonstrate the cubits and all the stuff that they need to run these air correcting code. So it's basically an engineering demo. It's not really going to do anything. Next year, we want to make a device called Cucoburo. That'll be a logical memory. And so it's going to show us able to protect quantum information using all these parity checks, using this real-time decoding for seconds. That's, to It means is just sort of amazing to talk about a quantum state that's like lives for long enough that we can have a conversation about it.

Then the next year, we're going to be building a device called cockatoo. And the key thing for cockatoo is I mentioned that in the gross code, our qubits come in 12 packs. 12 is not enough. We're going to demonstrate getting two 12 packs to work together. Logical communication. That's really the building block in going, that's really key to building a modular computer is the ability to connect the modules together. Is that the L coupler that's a reference? That's an L coupler.

Got it. Exactly. And that is such a bizarre thing. running a quantum gate across a cable. Like you can actually pick this thing up with your hands and you can bend it. You can connect two things together. We can get quantum information to go across that. So much fun.

And then finally Starling showing this magic state distillation, bringing us to universal computation and then getting it out to the world. Because all those steps building up to it, there's great technology demos. They're going to show memory. They're going to show this clippard computation. But they're not going to do anything that your classical computer couldn't do. They're not going to be useful for our clients.

And so at the same time that we're doing all of the, this, we're going to have these near-term air-mitigated devices like the one behind me, doing more and more useful things, in quantum chemistry and material science and optimization and showing quantum advantage in the next two years. It's almost like the proof of performance for some of these things you're putting together that are the building blocks of POQ, proof of quantum. Proof of quantum. There you go.

No, absolutely. This is super exciting. So, all right, you're in a spot where a lot of researchers come together, a lot of scientists come together, a lot of clients of yours that are interested in leaning into quantum. What do you call it the think lab? The think lab, yeah. Think lab. So what are the researchers buzzing about this announcement?

Because they start probably thinking about this is how we could maybe use quantum, you know, in 2029. What's the buzz? What are you hearing? Well, what's Oliver buzzing about? Because I don't think we've ever had a happy looking guest sitting before us. You've seen genuinely excited and genuinely happy with what's happening. What do you do to your guests that they're all so unhappy?

They all work in crypto and AI. No, seriously, yeah, my background, I'm a physicist. Condense matter physics study, like, weird quantum states of matter. And I got into quantum computing to build that Blue J system. It was like the dream. You know, this large-scale fault-taller quantum computer, the first quantum computer that can really run any quantum algorithm that you want to throw at it. And so it's always been kind of a game of, well, we're working really hard on the cubits,

or working really hard on small air correcting codes, all these little technology proofs, or these air mitigated systems that they're computationally useful, but they're tricky to use well, in all honesty. And to have the technology have advanced so far that at this point in my career, I can say, you know what? I think that in eight years we'll have built that thing.

That's just amazing, that it's watching this entire field come to a pinnacle in the next decade that has me just delighted at pickled tink. Pickled pink. So I was going to go off on a little detour. Like, what's a regular day for you? So you arrive in the think tank or wherever your office?

What does a regular day look like for the CTO of IBM Quantum? That's a horrible question. There's so many meetings. Okay, let's take the meetings out of the equation when you're not in a meeting. No, no. Seriously, we have a really complicated system here, that we have quantum hardware that gets manufactured in semiconductor fabrication facilities, a lot like what you do is for a CMO. yet. We have classical control hardware that we manipulate these things with a whole bunch of

microwaves, and so it looks a lot like an enormous number of cell phone transmitters, huge rack of them. We have software to tie it all together. We have software to let users take their problems, translate them to quantum circuits, send the quantum circuits to our computer over the internet and get the answers back. Yeah, by the way, this whole thing's cloud accessible. And somehow has to make sure all of these things work well together. And so ultimately, my main job is taking the hardware, taking the quantum hardware, taking the software, making sure it all plays nice together and gives our users the results that they expect. And this is the accessibility through the KISKit. Yeah, so if you wanted to run a quantum algorithm. Kisket is a software package.

It's written in Rust but has a Python API. It has the tools to take your problem, translate it to a quantum circuit. Then Kisket runtime gives you tools to take your quantum circuit, send it over the internet to one of our machines, run it, get the answers back. Building up a great, group of people that know how to operate these machines is incredibly important to us. To the extent that actually everyone on the world, including you, gets 10 free minutes a month. You can actually just log in and play with the system behind me for a little while if you want to. Beyond that, we have what we call the IBM Quantum Network, which is a group of 300 companies, academic labs, government labs, that are getting more time on these systems as well as support on how to map

their applications into quantum circuits and get the answers they're interested in. And that's all about making sure that when we get to Quantum Advantage next year, when we get to Starling in 2020 and we will get to Blue Jain 233, we have users that will know how to use those systems. Because if some alien came down and dropped one of these systems in front of us today,

I think we'd all sort of look at it to go, huh, what now? Power is nothing without control. Who will those customers be? We've mentioned materials, science, chemistry, physics. In 2029, beyond, who will be the IBM quantum

customers. The goal is to have these systems in 29 and 2033 look a lot like what we already provide today, that you'll use the same software to access them. You'll just be able to do more. And so the expectation is that the customers are going to be, well, hopefully a bigger group than those 300 people we have right now. That includes big companies you might recognize the names of, like banks, aerospace contractors, includes national labs. Part of what I have to juggle here is some of them really want to brag about the fact that there are clients. Boeing falls into that category. And some of them would rather keep it a little bit quiet because they want to build up

some advantage for themselves in the meantime. So we get it. So, all right. So if we're casting it, you mentioned the fact that it's super important to have people on your team that will be able to manage and work and support this kind of compute as you're building it up into 2029. If you're talking to the folks in school right now that are hearing about quantum that are computer science majors or theoretical physics, physics major,

what do you want to tell them about, you know, a direction to point their learning, their understanding of where this is headed? Well, it's really just a hugely multidisciplinary effort. Like I told you, my background's from physics. You have chemists on the team that are really more focusing on the processing of the device, as well as applications that you can run on it. A lot of software developers, a lot of electrical engineers. I think the most important thing I'd say is pick something you love and hack away at it. If you want to join our team, the best thing we could ever see is that you found a technology that you cared about,

what you went out on your own, you learned how to do something with it, whatever that is. Oliver, that echoes everything we say nearly on a weekly basis that curiosity can power a lot of things, including a great career in any field. So that's really cool.

Quantum curiosity. The IBM announcement was $150 billion across technology in America. I don't think we can have a conversation without mentioning the two-letter or three-layer word if you're talking plows. Does AI play a role in the IBM roadmap? and if so, how? We talk about bits, neurons, and cubits. Classical computer, AI, and quantum is being what's going to power the future of computing.

And at the end of the day, IBM and IBM research in particular is all about the future of computing, figuring out how it's going to influence all of our lives together for the better. I think there's going to be some really interesting stuff in quantum plus AI, actually. We talked a little bit about scaling and quantum advantage and be able to do something faster, better with a quantum computer than a classical. There are actually some very. specific AI problems for which you can prove that a quantum computer would have an advantage. And that's sort of surprising to be. I mean, AI is very heuristic. It's very much we do all this

stuff and it kind of works, but it's really hard to prove why. And so beyond that, as we get quantum computers big enough to explore AI problems, I think we're going to see heuristics where quantum and AI play together very nicely as well. So it's definitely a thing that we're researching both ourselves and with our partners inside of that IBM quantum network. Those two unknowns are quite AI has it. So Sam Haltman will come on stage and say, we don't know how AI works. And then perhaps in quantum, there's a lot. You don't understand how it works. So there's quite a nice synergy that maybe the two can work each other out. Well, I mean, we do know how quantum mechanics works. It's just, it's hard to talk about all the possible applications when right now for a lot of them,

you have to prove that it works on a chalkboard. There's also, by the way, a flip side of this, that is always a little bit slower to come to mind for me, which is also that we use AI a lot in quantum. We talked a little bit about compiling quantum circuits. And it's a lot like a classical compiler for software that we have an optimizing compiler. It finds places where you have redundant things inside of your quantum algorithm, simplifies them so that it runs faster. Right now, because we have such high air rates, if your circuit runs faster, it runs better. That's super important. And actually, we have a lot of AI-powered, what we call transpiler passes that go through and improve quantum circuits. So the reinforcement goes in both directions. And it's good to think about it in that

way because a lot of it, a lot of the mainstream media is thinking the opposite of that. So it's good to hear that. Did we miss anything important in this announcement that we have on uncovering? Well, I mean, one thing that's really fun about it to me is I mentioned, you know, this is the lobby of our building, basically. This is a system that we think is going to fit in a reasonable amount of space, even Blue Jay. We're actually building a data center in Poughkeepsie, New York, which is about a 40-mile drive up the road from here, that's designed to house system two's today. So those terms machines for near-term quantum advantage, as well as start. and Blue Jay in the future. And it's actually an absolutely beautiful space, but it's also sort of, again, an indicative, this is practical. Like, this is something that fits into an old building that IBM had off to the side that we could remodel into a data center, as opposed to having to build some massive warehouse out in the Midwest to house a system like this.

Yeah, so the form factor in the footprint of quantum data centers could are, is looking very different than traditional data center footprints. And it's looking very different from how anyone would have predicted that would have looked two years ago. that this advancement in the air correcting codes and the idea of building these modular fault-toler and quantum computers is really bringing this from the kind of large-scale engineering you'd think of like a space program or something like that into something that looks much more like a traditional IT sort of system. Even looking at the backdrop behind you,

it doesn't look like a futuristic alien technology. Our designers will be kind of disappointed to know you don't think it looks futuristic. Well, yeah, it looks like something from 2001. But that having been said, again, Starling is only going to be about three times as big as this guy. It's something very human, very

approachable. Maybe in a hundred years it'll be something in a handheld situation like we've seen in form factors where old school computers were, you know, giant rooms, vacuum tubes, a whole nine yards, and now we can see stuff in our hand. Probably the

cycles will repeat themselves maybe just a hair differently this time. Hopefully so. But, you know, in the meantime, Yeah, one question we could ask a lot is, why should this matter to me? Like, this is some huge computer that could be sitting in a data center in New York. It's not on my phone. You know, it's not on my laptop. And what the reality is, when you're using AI today, it's not on your phone or your laptop anyway.

The communication is so good that it almost doesn't matter where the resources are that you can take advantage of it. That's, you know, what we're doing with Kisket with least 10 free minutes a month. It's what we're doing with AI when you use some large language model to, you know, write your homework for you. Sorry, my kids are in high school. using AI. They're not cheating. Of course not. You just mentioned, you just said, why does it matter to me? And I think that that's probably the best question for most of our listeners. IBM investing $30 billion in quantum. Why does that matter to me? You're at dinner party. What do you say to people with that? Why quantum? Why does it matter to me? Why does it matter

to humanity? I really like to pick on just one of those applications that I mentioned material science. How do we simulate semiconductors, superconductors, strange novel materials? The reason why I like to pick that one is, what are the ages of man named? We have the Stone Age, we have the Iron Age, we have the Bronze Age. That is how important materials are to us because it dictates what we can build. And so if quantum computers can bring forward material science so we can develop new light or stronger materials, superconductors that work at higher temperatures, any of those things, just one, one run that invents one thing, has the potential to change everything.

for everyone. And, you know, going back to why am I smiling, you know, it's great to be involved in a project that has that kind of potential. One last thought that always swims around my head when we talk about quantum computing and quantum mechanics, right? So nature does quantum really, really well. Like natural systems do quantum really, really well. And we're trying to kind of emulate those natural systems. And isn't there like some kind of cyclical thing that, you know, our quantum computing could help us figure out nature, but nature could help us figure out quantum. Do you ever think about that circle? That is, I've got to say this is the first time anyone has ever asked me that question.

You know, at the end of the day, like many people on the team, I'm a scientist. That's studying nature, studying what it gives to us and studying kind of some of the really surprising and complex things that happen is really core to what I do. And indeed, there's a lot of science in these computers. We didn't talk about what the cubits were at all, but, you know, At the end of the day, our cubits are made out of superconductors, which is this really highly entangled novel state of matter that doesn't pop out on a day-to-day basis. We would not be building these computers without having had a lot of people

do a lot of very basic research on things with no obvious applications. Now we're trying to turn this into a means of computing, and yes, I absolutely expect one of the huge applications of these systems to be going back and studying nature. In fact, one of our partners in that IBM Quantum Network is studying high-energy physics, with these things, trying to study the basic interactions that give rise to reality using these quantum computers. And so, yeah, I absolutely expect that loop to close.

Amazing. Well, Oliver, this has been a fantastic conversation running through this. Thanks for stepping us through the roadmap. I think it's really cool to be able to have the confidence based on your research, based on the work your team has done for a long, long time to kind of say, hey, here's where we're headed, and here's how we're going to get there. And here's why we're confident that we're going to get there. So really cool. We look forward to So following those steps and those milestones, maybe we can jump back in together and when you hit some of these performance metrics and see how things are going. I would love to do that.

Thank you very much. We'll have to do a live show from upstate, New York. Thinking on paper. At XYZ, we have some great quantum conversations there. We actually have some of Oliver's colleagues. I'll put the links in show notes. We have a book club, which we're reading at the moment, which follows on from Oliver's last thing about studying quantum is the building blocks to consciousness.

an existence. So come and join us for that. Otherwise, anything to add, Jeremy? So, Oliver, have you read Irreducible yet? I have not. It's definitely going to go next under my list. Fascinate. I think you would love it. You would love it. It dips into the consciousness side a little bit more, but I would love to hear your thoughts if you choose to read it. We do it as a book club when we unpack it. You'd be doing it for the last month or so, but there's my wreck for you. Thank you. Appreciate you joining us. All right, that's it. Thinking outpaper.xy-Z. Be curious. Stay disruptive. Keep thinking on paper.