Technology, Connected - IBM Just Took Quantum Computing Out of the Lab

Episode Date: May 6, 2026

Scott Crowder, Vice President of IBM Quantum Adoption, joins Thinking on Paper to explain IBM’s approach to quantum-centric supercomputing.Rather than replacing classical computers, IBM expects quantum processors to work alongside CPUs, GPUs and high-performance computing systems. Each type of hardware handles the parts of a problem it’s best suited to solve.In this episode, we discuss:What quantum-centric supercomputing meansHow quantum processors, GPUs, CPUs and HPC systems work togetherIBM’s roadmap towards fault-tolerant quantum computingWhat IBM Starling is designed to achieve by 2029How superconducting qubits workWhy quantum error correction is essentialThe role of Qiskit and open-source quantum softwareWhich quantum algorithms could deliver practical valueHow quantum computing could support chemistry and materials researchIBM and Cleveland Clinic’s work on protein simulationIBM’s collaboration with RIKENHow Nvidia GPUs fit into hybrid quantum systemsWhy accessibility and real-world adoption matter as much as hardware progressScott explains why useful quantum computing will depend on more than increasing qubit counts. It will require reliable hardware, error correction, strong software tools, integration with existing data centres and developers who can apply quantum systems to real problems.This conversation examines IBM’s plan to move quantum computing from experimental hardware towards fault-tolerant systems that can contribute to scientific and industrial computing.-Thinking on Paper is a technology podcast about AI, computing, science, and the systems shaping the future.🏠 HQ: www.thinkingonpaper.xyz📺 INSTAGRAM: https://www.instagram.com/thinkingonpaperpodcast/🎧 Spotify: https://open.spotify.com/show/00volKqMsQntToeho35W47🎧 APPLE: https://podcasts.apple.com/us/podcast/thinking-on-paper-technology-moves-fast-think-slower/id1713227258--Mark x: https://x.com/markfielding99Jeremy: https://www.linkedin.com/in/jeremygilbertson/–Chapters(00:00) Trailer(01:20) Quantum computing(02:40) IBM Reference Architecture(05:05) Superconducting (06:47) Algorithmic Discovery(12:34) Cleveland Clinic(13:44) IBM's quantum-centric supercomputing architecture(16:07) Quantum computers today(17:58) Quantum and classical converge(22:28) Richard Feynman (25:25) Data centers(32:01) Quantum computers in space(42:19) Qiskit, NVIDIA, and open source

just a question of who and when, not if anymore. Like if you talk to people like five, 10 years ago in quantum, you know, other vendors were talking about like having to build like this gigantic football field size quantum computer. You know, we don't need to do that anymore.

That's why we are so confident in the 2029 date. There's a lot of people talking about quantum computing that have never built a quantum computer, but still hype it like they have. And then there's some of us who have built quantum computers and made them available for people to use. I actually believe this is not a good idea. I don't know what Elon is.

I mean, in fairness, I wouldn't need to think through his hypothesis for why that is. Disruptors and curious minds, we were back in the realm of quantum. Listen, here's the question. Can scalable quantum-centric supercomputing ever tackle the toughest scientific and humanitarian challenges? IBM says it can. We have a gentleman on the show that's going to help us unpack it. Scott Crowder is the vice president of IBM quantum adoption and business development. Before that, he spent seven years as the CTO and vice president of strategy of IBM systems.

So he knows infrastructure. He knows the history of computing. This is a show you will not want to miss. So I guess the message today is that quantum computers are real. The concept started out 45 years ago, so it's not an old concept. It's been about 10 years. It's been 10 years in a couple of weeks, actually. since IBM put a really baby five-cubit quantum computer on the cloud,

and people could actually run a program on a quantum computer and get a result. So it was kind of like sci-fi come to life. And then I think there's a lot of hype and confusion in the field. There's a lot of people talking about quantum computing that have never built a quantum computer. And then there's some of us who have built quantum computers and made them available for people to use. where we are today is that we've now have quantum computers that can run a quantum algorithm that is too complex to run on any classical hardware.

The confusing part, and this is why you get the bipolar response, is that doesn't mean it's better than running a classical algorithm that can run on that classic computer to solve the problem in a different way. And that's like the next step in quantum computing is when the quantum way of doing it is better than the possible way of doing that. And because we're not there yet,

you still have two camps, in my personal opinion. And until we hit that threshold and prove it to people beyond a reasonable doubt, then those camps will converge into one. This is where we're headed. I mean, we have, and I want to ask you a couple of questions historically, why certain reference architectures worked

and why certain reference architectures got pushed to the side. So example, TCIP, TCPIP, everyone got on board, right? Unix, everyone kind of got on board. OSI, everyone kind of got on board. Vetterbushes, Nemex, not so much. It was early, but it ended up being the kind of the same architecture that Berners-Lee did with the World Wide Web. So what do you think makes a reference architecture powerful, compelling, and one that'll serve as scaffolding moving forward? Yeah, I think it's probably two things.

One, it actually has to solve a problem. like so that's kind of like fundamental and some of the examples that you gave did solve a problem but it didn't have the second one is it needs to get adopted for us we're in the fairly early days of leveraging you know quantum computation to solve problems in a practical real way and for us the quantum centric super computing like reference architecture is kind of our perspective on what computational resources and how you put them together do you need to to actually run a problem and solve a problem. And because it's early days in quantum computing,

I think part of the fuzz, like you mentioned before, in the two camps, you got a similar fuzz in terms of what a quantum computer is. Because for a lot of people, we're still in this, like, if I come to life, the excitement about flux capacitors and spooky action in a distance and teleportation, like being used to do computation, people lose sight the fact that at the end of the day, all we're doing here is building a different kind of

computer that runs a different kind of algorithm, which is for particular subroutines and a larger workflow. And quantum centric super computing for us is how do you take that acceleration for that subroutine that quantum computation is really good at and make it part of a larger workflow? You have these different modalities. You have trapped eye and you have spin qubits. You have superconducting. Again, those examples that Joe mentioned, one survived, best didn't. Is quantum computing the same in that one modality will survive,

or is it different in that all the modalities could come together and solve those problems as one global quantum computer? Or will they fought by the wayside too? I think that probably, maybe it's my background, but to me, the best analogy is probably Silicon versus Germanian versus Gallium arsenide versus Indian phosphide, etc., etc. and it's not like germanium, which was the original basis for transistor radios back in the 50s, ever completely went away or Indian phosphide for specialized things ever really went away. But the silicon transistor, CMOS transistors, just became dominant. I'm old enough to just joined IBM at the beginning, at the end of the debate between bipolar and CMOS.

And I think it's more similar to something like that, where, you know, in my opinion, yes, one of the modalities is probably, probably going to become dominant. And once it does, it's going to be hard for the other modalities to catch up just because of the infrastructure, et cetera, et cetera, that's built up around it. For us, superconducting qubits is the right tradeoff of being, you know, fairly easily manufactured because he uses silicon fabrication techniques and also fast, which makes it feasible as a computational platform. You're not going to hear about athletic greens.

we have no electrolyte drinks or AI agents to sell you. The show you're listening to, Thinking on Paper, is funded entirely by me and Jeremy. So this is an advert for us, because we sponsor ourselves, and we need your help. So please subscribe wherever you're listening or watching thinking on paper.

And if you're feeling really generous and kind, please leave a comment. And now, back to Scott and IBM. How important, you mentioned, you referenced in this too, how important is accessibility to the technology? From our perspective is critical. From overly simply, from our perspective,

there's two things we need to do to take quantum computing and make it to unlock its value. The first is we need to build more and more powerful quantum computers, that's both hardware and software. That's kind of obvious. That's kind of mostly on us. But the second one was equally important is algorithmic discovery of stuff you run on quantum computers.

I think we take it for granted that for binary math, we've been developing algorithms for centuries as humans. And when digital classical computers came along in the 40s and the 50s, there was this huge increase in algorithm of development in the 50s and 60s that kind of like underpin a lot of what we run on classical computers today. We're really early stages in humans thinking about algorithms that run using quantum information science and how to apply them for applications. Part of it is the algorithmic discovery.

So part of it is getting the computers out there so people can explore and make sure that it works. What is, I think, a little bit less appreciated, and bear with me is probably a long answer, but a little bit less appreciated is how do I think about the problem and break up the problem so I can leverage this tool effectively? and there's been a huge, like, sea change in that in the last couple years that I think people are not appreciating quite as much as they should. So that's how, like, what's underneath the need for quantum-centric super computing is,

okay, I've got this new computational tool as quantum. It's good at certain things, but it's not big enough yet to run the entire problem on the quantum computer. Does that mean I wait for 20 years or 10 years or however long? From our perspective, no, because people didn't wait for classical for that either. And what you're seeing now is people are starting to run real-world scale problems on quantum computers. So, for example, Cleveland Clinic published a paper where they basically show that they can simulate a protein of interest in life sciences with 303 atoms. And they did that by breaking up the problem and taking the really hard parts and using a quantum computer to simulate the really hard parts and using classical to basically break up the problem and stitch it back together. So they can basically simulate that protein, which is important for life sciences because the oxidation of that protein is actually one of the things that causes vaccines to lose their shelf life.

So it's a real problem that they're really trying to simulate. and they can simulate it on quantum computers today. So it's that work on how do I think about the problem? How do I apply quantum algorithms and how do I break up the problem to run it? And you can't do that without access to computers. It's kind of like the AI world, right? You can't do chat GPT without access to GPUs today, I mean, primarily. like it's the same thing.

You're not going to be able to develop the quantum application, quantum-enabled application is quantum algorithms without access to the underlying technology. How complex of a protein is that Cleveland study simulating? So that one's 303 atoms. Make sure I get the pronunciation right.

It's a tryptophan. 303 atoms. I don't know how complex that is. So if you wanted to compare it to... It's a relatively moderate size protein, but the exciting thing is that you can leverage the same approach to run 10,000 atoms. So now that people have figured out how to break up the problem and leverage on a computer, they can actually scale it significantly larger. And I highly anticipate that

you're going to start seeing publications around those larger scale very soon, like, I mean, in the matter of month. Is this kind of like learning how the tool works, or is it the computational power of what you're able to do at the moment that is preventing or keeping it at 300 before it gets to 10,000? So it's a little bit of both, but it's primarily the first. So like I said, before people like we're thinking of this as how do I fit the whole problem into a quantum computer? This is like five years ago and algorithms to do that. People are now thinking about now that the computational tool is real, but one of these quantum computers are real. People are thinking

about how do I best leverage them and realizing that if I use similar approaches that I use classically, I can use these quantum computational tool more efficiently and more effectively. Like the Cleveland Clinic example, they were doing like eight atoms, 14 atoms, as recently as nine months ago. And then they had this breakthrough in algorithmic discovery on a better algorithmic approach, which improved the accuracy, and then they had a breakthrough in figuring out how to break up the problem. So how do I construct the problem differently, which allowed them to increase the size of the atoms?

And there's no reason why they can't, now that they figure this out, go from 303 to 11,000, 12,000, and now not just simulate this protein, but simulate that protein insolvent in a much more complex thing, which is what is needed to do the real-world digital twin kind of stuff. that they want to do in healthcare life sciences. Let's talk more about Cleveland Clinic and what they did, because I think whenever someone talks about computers and proteins,

they tend to gravitate right towards, oh, alpha fold. But I think what is interesting about what Cleveland Clinic is doing, they're taking it beyond structure, and they're actually talking about the model works on the electronic structure between the molecules working together. It's more complex than just, hey, here's the studs and the framing. It's actually how things communicate. Can you unpack that a little for us?

Yeah, I mean, I might be oversimplifying this a little bit, but you can think of they're using quantum simulation to kind of like do more of like a digital twin-ish kind of thing of like simulating, simulating it, whereas Alpha Photon AI is basically taking the known information that we've got and trying to interpolate and find, you know, better ways to like use that data to make a better prediction of what might work. So in one case you're basically doing the actual simulation

of the molecule. And the other case, you're trying to use known human data that we've like wet benched or whatever and basically try to predict what might work better based on that. So we're here to talk about this exciting announcement that you guys have.

So it's a three-phase architecture. Can you help us go through this phase by phase and help us understand it from a high level? Basically, you can think about it as we're going to have quantum processing units or quantum computers that are going to run those quantum subroutines and we're going to have classical resources that we know and love today, CPUs and GPUs that are going to continue to be good at what they're good at. So how do you build an architecture that allows you to run the subroutines on the right

underlying processing units in a way that's sufficient? And that's fundamentally at its heart, you know, what the architecture is about. The choice between what part of the problem is done by which, by either the QPU or the GPU. Is that what you mean? Exactly. And there's levels here. And this is why it makes it a little bit complex. But there are levels of the onion here.

There are things that need to be done to create a large-scale fault-tolerant quantum system that, need to run on the quantum processing unit, and there's pieces of it need to be run on classical accelerators, whether they be A6 or GPUs or CPUs. So that's kind of like the inner part of the onion. But then there's a second layer of classical resources that need to run the classical parts of the workflow. So you're very similar to CPUs and GPUs working together today or GPU heavy nodes and CPU. So you need some way to basically coordinate the work across those and you need some kind of architecture that kind of lays out how are those connected, what bandwidth requirements, are there what latency requirements on there, those kinds of

things. So the fault tolerant scalable thing is kind of the down the road like, hey, we're trying to point that direction. What's happening in the near term, not today in the next year or so, that is exciting and about this architecture. And we can even go in the technical weeds a little bit here. Like, what are you excited about in that realm? Yeah, so what I'm excited about is like some of the work that we did with Rican, which is, you know, a scientific research institution in Japan that has the Japan's largest HBC cluster.

And we actually have a quantum computer in the same building, which allows us to have, you know, connectivity, direct connectivity between. being the quantum system and the classical systems. You have to really explore this QCSC architecture. And what they're doing today is figure out, how do I orchestrate these two resources so they can work well together? And it's a little bit more challenging in the sense that,

you know, a lot of the like orchestration today is you've got like a cheap resource and an expensive resource. And you're trying to like, you know, don't care too much about like managing the cheap stuff, but like how do I optimize use of the expensive thing? In this case, in a quantum centric supercomputer, you've got your HBC, AI resources, and your quantum resources, and they're both kind of expensive. So you basically want to come up with a way

to orchestrate them so you're not wasting time on either side of it. And what we worked on with Riekeen was the workflow in the orchestration for a chemistry experiment similar to what I described with Cuban Clinic of how do you run a chemistry workload that roughly runs about half the time on the classical resources, runs it half the time on the quantum resources in such a way that I'm not like letting either side of them just sit idle. And I'm using, fully using the computational on both sides. Just the phase one, phase two, phase three, does that correlate in any way to the IBM chip? So you're talking about Starling in 2030? Two, I think, is on the roadmap. Do the phases relate to that?

2029. Thank you for correcting me. Oh, I'm too pessimistic. Yeah. So, yes, because we're trying to intercept our roadmap for the quantum piece of the quantum-centric supercomputer with the classical piece of the quantum-centric supercomputer, how they work together. So yes, the phase three is kind of aligned with the Starling 2029. Is that still on?

Because 2029 is rolling up very quickly as I look at my camera. Yeah, that's cool. That's why I basically, yeah, yeah. Are you still on track for 2029? We are. So for the reasons we mentioned before, like you've got two camps, you've got a lot of hype. There's lots of different kind of like confusion hype. Like I mentioned the like, you know, apples to oranges.

confusion. There's the fault tolerant. What really fault tolerant is confusion. There's the, you really have a quantum computer or you just have PowerPoint and like making it sound like you've got a quantum computer hype. But we've put out basically a roadmap of what we're releasing to our clients year by year by year. And we put out a roadmap of key internal development milestones year by year by year. So you can like follow along at home of like, are we on track for 2029? Right now we're on track for 2029. The question I always get is like, what's your error bar? And, you know, what I would say the error bar is, like, I don't think we're going to pull it in more than six months. Like, I think there's like some really challenging engineering work necessary to put it all together.

And I don't think we're going to miss by more than a year. And I don't think it's going to be fundamental. It's going to be like, there's a lot of engineering work that we got to do. That's going to be all over the internet, Scott. So my little thought, and then I'll leave it to you, Jamie. I was just thinking we started the conversation speaking of. about solving a problem and then how that technology is adopted. And I'm thinking about chemistry and material science and molecular simulation and where that happens. So it happens in the universities. It happens in the big pharmaceutical companies.

It happens in clinics. And then I try to connect that to the technology that you're building. And I think about learning curves. And I think about removing what already in place and what's been working for these research institutes. for so long. And I think about the scientists in the lab and how much say they have in what the, how the computations are run. And how do you change, update, evolve that system that seems very embedded culturally. Or maybe I'm completely wrong on that because I'm not a scientist, but that's what I was thinking as you were speaking. I think there are two levels here, right? So

there's the level of the people who are building the fundamental algorithm approaches and building the software assets or wherever you want to call them that instantiate that that are repeatable. And then you've got the people who are going to like leverage those models or leverage the software assets, right? So we're currently at the state where we're still in the algorithmic discovery and application research piece where you just, need to basically still improve building those assets.

So like in the Cleveland Clinic, you know, part of what they did was kind of use raw, out, you know, raw concepts. We may have had some software assets that made it easier, blah, blah, blah, blah, blah. But it wasn't like a black box that they could just put their input, get the answer out and, and use. We're going to get there. Like, we're going to get to the point probably in the next three, four years where you're going to have chemistry solvers that are more like black boxes

that like a wider set of computational chemists can basically just use as a tool as opposed to using your pick pick a pick a tool today julie blah blah blah blah blah you know tool today that they use so I think we'll get there but in the short term really the work is having you know the people who have domain expertise and math expertise you know, building these algorithm approaches for quantum, proving that they work, running them on real quantum computers, et cetera, et cetera. Scott, let's have some fun with this one. So let's imagine somehow Richard Feynman comes back to life

and he comes across this paper and he reads the paper. Imagine what, how he would react to. Let's just have some fun. There's no wrong answer here. Like, how would he react? I think you probably, so it's not, this one may be less, Feynman, but like, you know, they announced the Q4 bio winners today. I mentioned that Cleveland Clinic one.

So I think those, I think he would be more excited about because like he had this postulation back in 1981 that like, let's use quantum information science for, you know, computation. The fact that people are doing that for real world examples today, I think, I think it would be a little mind-blowing for him, honestly, even though he was the, one who like postulated it. You know, there's the half-Mobius thing. There's the neutrons guy.

There's now a lot in the last, this is kind of my point before. In the last five months, you've seen like a major uptake in like people simulating stuff on quantum computers that are real problems they want to solve. So, you know, we put out this paper on this half-mobius molecule thing that like the researchers at Zurich had this built, you know, build this molecule that doesn't exist in row, in in nature with this you know crazy half-mobius property we go around it like those halfway around like you know it crazy stuff but they then simulated on a quantum computer to kind of prove that they had built it you've got the neutron scattering experiments like the DOE where

they use the quantum computer to simulate the experiment that they ran you've got the Cleveland clinic thing simulating the protein so I think this is the thing that's like Feynman's vision come to life. I mean, I didn't know him. Like, I don't know how excited he'd be about, like, you know, optimization for finance. But I'm just guessing he'd be more excited about the, like, his concept that you can use this to really compute stuff

that physicists and computational chemists are interested has come to fruition. Did Feynman write about this classical quantum unification? Or was he very, very, much of that you'll have quantum computers and there'll be a separate idiom on their own? Would he be disappointed by the reliance on the classical infrastructure? I doubt it. Like, I mean, you know, like I said, I didn't know him.

So complete speculation. But I think just the fact that you're leveraging quantum information science in order to simulate quantum mechanics on a real physics problem, I think would be pretty damn exciting to him because, like that was like the big picture concept. No. Let's, can we transition to data centers for a second, Mark?

Are you good with that? And could I, before data centers, just one more question before we get onto data centers. Have you changed your mind about anything in the past year about quantum computers that you didn't think you would? I don't think I've changed my mind. I think, um,

I think I've been surprised by how quickly. some of these algorithmic approaches to do larger scale problems have really picked up but I mean I'm living it daily so I'm, you know, I've been less surprised by other things. I am continually surprised by some of the hype

and how the hype is like interpreted. I shouldn't be at this point. Sometimes you just shake your head at all of the hype and all of the nonsense and say, oh man. Probably can't comment on those, but yeah. Jeremy, Data centers.

What is this architecture going to do to the future of data centers is my question? Yeah, I mean, the good news, bad news is that the rise of AI mega data centers have made it really easy for you to plop a quantum computer in any of those data centers. Our requirements are tamed by comparison, actually.

I think, you know, people assume like quantum computing really kind of complex, et cetera, et cetera, et cetera, that it must be like massively power hungry, like all these requirements, et cetera. So basically we require water cooling for our systems and not to get the heat out primarily, but to keep the temperature steady across everything because variations and temperature leads to variation in signal propagation, which leads to timing kind of situations. So that's why we water cool. It requires very minor power compared to, to AI.

So rule of thumb is a state of the art quantum computer is about the same amount of power as one rack of AI. And that's true today. It's a little under actually today. And that's true for the system in 2029 also. On the rack of H-100s, like how much is that draining off the grid?

When you get into like 2029 timeframe, you know, they're estimating well over a megawatt per rack. Okay, let's pause. right there, just for a reference mark, probably 10 years ago, five kilowatts a rack was like chunky, was like pretty big, right? And then we've gotten the 20 kilowatts and 50 kilowatts, 100 kilowatts, but you're talking about a megawatt in a single rack, which used to be like the whole data center, right? Yeah, it's a little bit insane. Yeah. So about the water. What about the water consumption? If we get on the cooling, could we get a figure on that? I don't know what the AI data

centers, water consumption are these days. For the quantum, yours, if you put it in a day. Oh, ours is fairly tame. Like, you know, if you got water cooled in your data center, we're like a blip on the, you know, and then. Less than the water for your coffee pot, right? Yeah. And then the weight of the system, again, is the weight of these, like, AI racks is, like,

intense. So, you know, if you can solve that problem, you know, the weight for our systems is not an issue. So the only constraint that we've run into a couple times is our systems are higher. Again, so these AI mega data centers, there's no problem whatsoever, but in like a traditional data center, and even a traditional, like, big data center, but like a departmental data centerish kind of thing, you know, our systems tend to be a little bit higher. What do you mean by higher? Like, high, physically high. Physically height.

That's it. They're taller. So in most of our deployments, it hasn't been an issue at all. all. But it's usually the one that is the question mark. The other one is that design choice or is that just necessity? It's a little bit of both. It's a lot easier to get the signals up on the top, up over the top, because there's some advantages, you know, of basically your cryogenics like hanging down as opposed to coming up. Oh, there's somewhere behind me.

But if you have some show and tell, Scott, we'd love to, if you could, I don't think you could. Oh, yes. Yes. So what are we looking at? That's kind of like an example of the thing that hangs down inside what we call the fridge. So that's like where the cubits live. So that's where the cubits live. And they live there because like overly simplistically, they need to be isolated from the rest of the universe.

So, you know, you do this quantum information. I love that sentence so much. I love this one of my favorite sentence in quantum, I think. Yeah. So, like, this is the sci-fi come to life part that's like, you know, for us, it's like 1.5 milo-calveen. Um, so it's really, really freaking cold. But it's also like light isolation, vibrational isolation, et cetera, et cetera, et cetera. Yeah, it's like a bond movie.

Like either you're, you're, you're cryogenic at some point, like even the trapped ions are cryogenic to some temperature. And then it's either just cooling it or you shoot frigging laser beams at it. It's like one of the two in order to get the entropy out. So that's kind of like the sci-fi come-to-life part of it. But anyway, so in order to basically get the signals most efficiently in and out, it's easier to do it over the top. If you're doing it over the top, it means that it's a little bit higher. So cubits, cubits are very distractible. They require some focusing, right?

Which you're talking about. That right universe. The trick is you want them to be distractible because if they're not distractible at all, you can't program them. or they're really, really slow. So it's trying to find the right balance of how distractible do you want your cubits and like giving them the right medication so they don't lose focus. What a reference. Wow.

All right. Well, so speaking of being higher and having the computation happening higher, let's talk about orbital data centers and let's talk about quantum in space. And, you know, I know you probably don't think a lot about that. but my job is to weave and connect the dots on the show. We talk a lot about space tech, talk a lot about orbital data setters that are StarCloud and others. Is there a place for quantum computing in space?

I think it would be similar. So overly simplistically, I don't think it's for cooling or anything like that. So the real question is, you know, space, energy, et cetera, et cetera, et cetera. Honestly, it's not something we've looked at very carefully. So when Elon Musk is on Twitter saying, oh, they should put quantum computers on the South Pole of the Moon in the dark shadows of the craters.

Is he saying that just to get likes, or is he saying that because he believes that that's actually a good idea? I actually believe it's not a good idea. I don't know what Elon believes. I mean, in fairness, I would need to think through his hypothesis for why that is there's a bunch

floating around of like helium sources etc etc etc but well helium three I'm sorry to interrupt your train of thought but is helium three needed in the quantum computing industry it is for

if you're using cryogenics to isolate it down to the temperatures that we are, it is. You know, it's a real thing. It is something that we need to address. Do you have a shortage of it? It's not a short-term problem. It's more of a, if you believe this technology is going to scale and you've got lots and

lots and lots of quantum computers by the middle of the next decade, then it is something that we need to consider. So the answer to the question is, yes, it's not a problem today, but it is something that we need to think about it. Let's try to, let's try to land a plane a little bit. We talk about not just technology, but what technology means for humans, humans solving problems, humans doing work, that sort of thing. We do have one question that Kevin Kelly left us to ask all of our guests. And I want to ask you this question, a little bit, a little bit social question, a little bit tech question, but what do we want humans to be? And how does technology like quantum computers,

potentially help us get there. I always say we want humans to be happy and productive. And I think it's, again, for me, quantum computing is just a new computational tool when you really get at the heart of it. So the question is, like, what kinds of applications are we going to be able to do better with quantum computers?

So, you know, from my side, I think there's a lot of societal benefit in doing a better job of stimulating materials, whether that's chemistry for life, you know, chemistry for fertilizer, et cetera, et cetera, et cetera, et cetera. And then there's the, like, doesn't sound so great for societal good.

It's, you know, I'm not sure it would be on the UN, like, list of things that they want to use a quantum computer for, but there are a lot of things in optimization that can save money or make more money for, you know, a lot of industry. where if they could optimize better, they have happy customers, higher return portfolios, lower risk,

all those kinds of things. So I think it's a combination of those kind of things. But yeah, I mean, I think it's more in the line of just the more meta question of how can we continue to leverage computation, to make humans happier and productive, with all the, like, ethical thinking about how to not use computers for the opposite. Do you have any other show and tell, quantum show and tell, so anything that you could hold physics in your hand to show us,

that would be stunning, because that always is well appreciated. Dave's going to come in here, but, So state of the art quantum computer, circa 2018. So this is system one. This is like the first one that we did from a design point of view to kind of show it like all in one

effective really, really pretty box to show that basically this is a computer it's not a lab experiment. The early days, like we've got like archival pictures of like, you know, we, form generator equipment and wires flying all over the place and looks like a lab experiment. This block, which was like at the bottom of that quantum computer

back in the day, there has turned into the thing that I'm showing back here, which has flex cables running hundreds of lines in and out of this thing in order to program it. Yeah, I've got the flex cable and the heron. We multi-purpose here at IBM. So this is actually the flex cable that we're talking about, like a prototype of it. You know, one of the things that we need to do is get more microwave signals in and out of the cryostat. So and also for manufacturing reasons, you can't have people like one by one plugging in wires like this anymore when you've got like thousands or 10,000 lines coming out of your computer.

So we need to come up with wave. that we can like significantly increase the the amount of connections coming in at and being able to just like click it into the computer from a manufacturing point of view as opposed to like hand connect it that's like a ribbon cable right that we were looking at yeah yeah yeah yeah so what if so you mentioned mobius before what happens if you if you had half turn full turn the ribbon yeah if it doesn't break I think you're okay yeah I mean The trick on these things is that it looks pretty standard, but yes, but it needs to be superconducting. And obviously it needs to like not break as you're cooling it down to really low temperatures and heating it up occasionally.

So it has more demands than like the typical like flux cables. So it's like example of kind of like an example I was giving of like, you know, where do I put the risk on 2029? you know, it's basically lots and lots of piece parts, very similar to that. It's not just one thing. It's like this entire engineering stack that we need to not only get the right quality on, but we need to get the right quality on at scale. And we need to do all of that, like, together. And that's why it's taking us several years, even though we've demonstrated,

like, all the piece parts on our run-nap, we need to put it all together. Yeah, so this is kind of like a picture of what the 2029 system, you know, roughly is going to look like. So it looks, you know, kind of similar to what our system is today. But, you know, this is not a football field anymore. Like, if you talk to people like five, ten years ago in quantum, because of the overheads in error correction at the time, you know, people were talking like, you know, other vendors were talking about, like, having to build, like, this gigantic football field. size, like, quantum computer, like, you know, we don't need to do that anymore. And that's why we are so confident in the 2029 date, because we believe we've come up

with the architecture and all the piece parts to build something that is a reasonable scale for us to deliver. Is the idea of this architecture to remove Nvidia for the reliance on Nvidia, or is it to build the relationship between IBM and what role do or will the Nvidia QPUs when they play? Invidia GPUs are a really important

part of the larger computing thing and GPUs will be a critical part of quantum centric supercomputing. It is the right platform to run certain kinds of work. Those certain kinds of work

are definitely part of building a fault on quantum computer and is part of building a quantum-centric supercomputer and as part of like AI megaparms, right? And all those are going to exist in 2029. This is not about like competition, et cetera, et cetera, et cetera. The one thing I will say, though, is that we firmly believe that in order to drive adoption, it needs to be based on truly open source software that isn't tied to any one like back end, whether it be the GPU back end,

the CPU back end, or the quantum back end. So that's why we would say that the layer for QCSC from an orchestration point of view or from how you program the quantum elements of it needs to be open and needs to be, in our perspective, cloud native, because a lot of the usage or accessibility to your point making it accessible

means right now making it cloud accessible. It's just a lot easier to get boarded option if it's cloud accessible. From our perspective, it needs to be open. You know, KISKIS, I still think this is a true statement. The only software development kit you can run on Google, Microsoft, and AWS. Obviously, IBM also.

We fundamentally believe that that's the case both in how you connect down and how you connect up. Connecting into a video, is absolutely part of what's going to have to be the case. We just want to make sure that quantum computing and how quantum computing links into all the other forms of computing is done in a way that people have flexibility and what hardware they choose for the different elements.

Did we miss anything really fundamentally important? Yeah, I mean, I think the question you hit on on, you know, the accessibility and the algorithm development like threads, you know, are really, really, really, really important. Like, I think that's, there's, um, we obviously believe we're in the lead here. Like, we think that we're ahead of everybody else. We think we've right. The technology choices, obviously we're biased.

And we have confidence we're going to hit 2029. Um, but there are enough other players with enough other money behind it that, you know, even if we're off. I don't think they're going to be 10 years behind us. So we do believe that there's enough money in this field that crime computing and fault on chronic computing is going to be a reality. It's just a question of who and when, not if anymore. And I think the big thing is going to be how much algorithmic discovery, how much application research,

how much focus on how do you use this tool is really going to be the, a bigger unknown, in my personal opinion, which is why we're so, like, passionate about, you know, getting a technology out there, getting people to use it, et cetera, et cetera. So there's been, like, a big tick up, like I mentioned in the last three years, but the, the curve is actually even increasing, but there's still a long way to go. Like, the number of people thinking about quantum algorithms is still small compared to the number people thinking about classical albums. Scott Crowder and Vice President of IBM

Quantum Adoption. Thank you for thinking on paper with us today. If you enjoyed this show, please subscribe where you're listening to it and share with one quantum curious friend, one quantum curious person in your life who needs to hear this. And until next week, stay disruptive, be curious.

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