On this episode of The Six Five – On The Road hosts Daniel Newman and Patrick Moorhead had the opportunity to sit down with key executives across IBM to talk about their full-stack infrastructure and the future of computing.
In this interview segment, Daniel and Patrick were joined by Jay Gambetta, IBM Fellow & VP Quantum Computing at IBM Research, to talk about how Quantum computing is shaping the future of IT.
Watch their other IBM conversation segments:
IBM’s semiconductor vision and ecosystem with Mukesh Khare, VP Hybrid Cloud at IBM Research
The benefits of fundamental science and technology innovation with IBM’s Ross Mauri, GM IBM Z and LinuxONE
How IBM’s Cloud fits into their full stack and impacts the future of computing with Hillery Hunter, GM, Cloud Industry Platforms & Solutions, CTO IBM Cloud, and IBM Fellow
Distributed infrastructure, AI, and how IBM’s vision of the future of computing extends to Edge with Nicholas (Nick) Fuller Fuller, VP, Distributed Cloud at IBM Research
Watch the full episode: IBM’s Full Stack Approach to the Future of Computing
To learn more about the IBM Research, check out their website.
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Transcript:
Patrick Moorhead: Yeah. So I feel like key message here is, listen, you have an architecture for your on-prem data center. You have your architecture for cloud. You need an architecture for the distributed edge and an architecture that ties all those together from a data perspective. So I think it’s a good way to end this here. Great topic, but hey, I am super excited because next up we have Jay Gambetta, director of research of quantum computing for IBM, and he is going to close out our chapter on the future computing and things that IBM is doing to lean in and lead and help its customers.
Daniel Newman: Yeah, it’s been great to see this full stack story start from the inner workings of research into semiconductors, move all the way through the cloud, the data center, the prem, we just got through the edge, and now we’re going to look at really the next wave of accelerator, which Jay will help us do. And it’s going to be a great way to wrap up our future of computing.
Patrick Moorhead: Let’s dive right in.
Jay, it’s great to see you again, and thank you so much for kicking off the quantum computing track at this year’s Six Five summit.
Jay Gambetta: Great.
Patrick Moorhead: Had a lot of people watching, it’s really exciting. But we’re here to talk quantum computing, but in the context of the big picture. We’ve been talking to a lot of your fellow compatriots about the future of computing, IBM’s full scale approach, but it’s time to talk quantum. You’re the last in this series of the future of computing, and let’s knock it out of the park here.
Jay Gambetta: Sounds great.
Daniel Newman: Yeah, Jay was a great guest at the summit. And so hopefully we can tie this together a little bit. It is a really big story, this whole full stack approach, and quantum is probably the one that people know the least about. And so I love maybe starting with that big macro view, Jay, of how IBM sees quantum fitting into its full future of compute and full stack view.
Jay Gambetta: Yeah, I think I would take it one step, I think the future of computing is not going to be computing without quantum. So if you think what quantum does, is it does some math that is really, really hard for classical conventional computers to do. So if we’re going to build a future of computing and it doesn’t have quantum, you haven’t got the future of computing. So it fundamentally has to be part of it.
Patrick Moorhead: We can just do the mic drop now, right?
Jay Gambetta: Exactly.
Patrick Moorhead: Interview’s done.
Daniel Newman: Well, I actually like something you said off camera though, because you said something about quantum computing and maybe using some other vernaculars there, because you’re alluding to that already about being part of the story and quantum accelerators and quantum. And I think that’s an important point to make early on in this conversation, is that part of what’s going to make it so important and such a big part of this future computing story is when people realize where it really fits in.
Jay Gambetta: Yeah, I think you’re exactly right. If you think of compute, it’s everywhere. You check your weather, you’re calling a compute. You do anything, it’s compute. Computing is in our lives, it’s everywhere. What quantum computing, and if I could get rid of the name computing, as we discussed offline, what quantum really does is it adds something new to computing. And that something new is something we’ve never been able to use. And there’s so many problems that we have trouble identifying, be it in business problems like optimization, chemistry problems simulating new materials, even going into some of the ideas in finance and math. There’s all these really, really hard problems, and we have new math. And when we can scale that math, that’s where it opens up a lot more business.
Patrick Moorhead: I think generally people understand that it is the next generation of computing. I really do appreciate the notion of quantum acceleration, maybe a QPU or something like that. It makes total sense, because when you look at the grand scheme of it, people understand accelerators and how to address them. So I like that a lot. I might take that and use it in the future.
Jay Gambetta: Please do.
Patrick Moorhead: So the other thing, people generally agree that this is so big that amazing things in the future that we’ve never even thought of can be solved. But I also get the question, “Hey, what can we do right now? What kind of tasks and applications can we do right now?”
Jay Gambetta: Yeah. So we’re in this really interesting time. So I would say we’ve been doing a lot of lab experiments and we put it on the cloud and we’ve got numbers that are really, really great of how many users doing it. But most of them are still studying the noise in the devices. If you want to get to do something of business value, we’ve got to move beyond that. And so what I’m most excited about is, I agree that there’s this thing called error correction, everyone is excited about is, I agree that there’s this thing called error correction, everyone talks about it, we’ve got ideas of error mitigation. But we’re charting a path where very soon we’ll be able to run these things we call quantum circuits faster than a classical computer can do it. And so we’re right at that tipping point of creating a tool that you cannot simulate with a classical computer.
So we’re doing that, we’ve charted that, and we’ve got technical roadmaps. But at the same time, when you create that tool, you got to start talking to the client. What problems map to that tool? And so what we actually see with all the clients we work with is, we are actually learning, they’re learning, we’re understanding their use cases, and we’re trying to understand how we can take that use case and map it to this new math that we know will have that tool.
So what they’re doing right now is they’re doing exploring, but they’re exploring this new type of math that does things like it changes machine learning with different types of, we call them kernels. Or it allows you to simulate quantum physics by emulating it with a quantum computer, rather than just using a big HBC computer that comes to an ad. But it’s doing everything a different way with a different set of math. And so we’re at that point, I think in the next year, where you’re going to see this breaking out, and how you use this tool, I think it’s going to be the exciting thing over the next few years.
Patrick Moorhead: That is exciting. And we talked in the green room too, kind of the flag plant of, you have to give them the tools that are useful to get business advantage out of, and we talked about 2023.
Jay Gambetta: Yeah.
Patrick Moorhead: Gosh, I think you did six or seven announcements at IBM Think, I think it was Kookaburra that was the flag plant.
Jay Gambetta: Yeah, so Kookaburra was 2025.
Patrick Moorhead: Oh, excuse me. I was getting ahead of myself. In 2023, you’ve set the table so an enterprise could actually take your system and create something himself. So maybe in 2025, they might see some value. And I’m just making this up on my own, adding two years. I can do this. I’m an industry analyst, I don’t actually have to do this.
Jay Gambetta: So, yeah. The one thing that’s important is, we’re thinking long term. So our roadmap goes beyond, right? We have the Heron, which is a 123 and we have multiple of them, and we have cross build and Flamingo, then Kookaburra as you said. And so I imagine keep building these up until really, really big systems so we can do more and more with it.
What’s exciting about 2023 for me is, if we can cross that point of being able to do something we couldn’t do classically, and then how we map it to clients, and that’s going to start. I agree with you, it’s going to take a couple of years to turn something into real business value, but my hope is in 2023, it’s not physicists talking about the noise in these systems, it’s us trying to understand how business problems can run on them, and the noise is all handled in the software.
So I agree in our roadmap, we talked about much more further in the hardwares, because we want to make bigger. But we also talked about simplifying it and making it easier for people to use. And so when we start to invent these things, like when we make quantum serverless [inaudible 01:06:20] run time, these things that start to abstract away the noise, so physicists are not characterizing, and you can start to actually use it by sending… When you use a classical computer, you don’t worry about the voltages.
Patrick Moorhead: Exactly.
Jay Gambetta: You call a library, and that library does the math that classical computers or GPUs are really good at. So it’s an important inflection point, because that’s going to be a point where I think we can talk much differently. I mean, it’s not about how many papers do you see or how many people talk about error mitigation, error correction. Hopefully all that gets buried and it becomes, “How are we using it?”
Patrick Moorhead: Daniel, you might like that. [inaudible 01:06:57]
Daniel Newman: I’m pretty sure that the first year of quantum briefings I took were almost entirely about error mitigation, gates and how [inaudible 01:07:04] do you keep a qubit? You know.
Patrick Moorhead: Yeah, and I think most of the people in the room when I was doing it, it was like PhD, PhD, PhD. They got to me, not PhD. But no, I’m super excited. 2023 to me is the flag plant where, not that it all starts, but this notion of enterprises having the tool, not focusing on error correction, but maybe working on a security application, maybe working on something like that. So.
Daniel Newman: So classical computing and just computing, because we don’t normally call it that except when we talk about quantum, tends to be built with a vibrant ecosystem. You’ve got startups, you’ve got big players, you’ve got a lot of collaboration. Quantum’s kind of interesting.
So you’ve got this full stack story that IBM’s trying to tell. You’ve got this full stack quantum approach that you and your team are working on building. How do you balance trying to take the whole problem on, from the hardware, the software, and all the other abstractions that you mention, and at the same time create that vibrant ecosystem and be inviting? Because that’s what’s going to make this really practical, is when the right applications mapped to the right customers become readily available to be run on quantum circuits.
Jay Gambetta: I agree. I think the first part is, yes, I call it classical computing, quantum computing. We’ve got to start calling it computing. And when we get that quantum in it, I actually think when we say full stack, we’re talking pretty low in the stack. You can totally imagine a startup creating a library or a software application that calls computing.
So I envision us creating something very similar to accelerators, NVIDIA and things like that. There is software. It’s not just hardware that gets that accelerated to work. We have to create that software, because we know our hardware best, that gets that to work. But if we’re going to create this industry you’re talking about, that software’s got to connect to their software. It’s got to connect to data. It’s got to connect to other clouds. And all of that has to work together.
So we see ourselves creating, yes, some verticals all the way up, but really focusing on a compute layer that includes software and hardware interacting very much together. And I think this is what’s different about accelerators to CPUs. Traditionally CPUs is you build your hardware, and then someone builds the operating system. When you have an accelerator like a GPU, there is software there. Quantum’s going to be the same. You got to have that software that gets the most out of that accelerator. That is how you build this full step.
Daniel Newman: I love that analogy by the way. The GPU is such a better analogy than the CPU for quantum.
Patrick Moorhead: It is. And by the way, before that, there are hundreds of ASICs through history that have done the same thing, they just didn’t get enough of that play. But I think for understanding purposes, I love it. This is an awesome accelerator to do some cool stuff.
So I’ve been interacting with IBM probably since the mid nineties, IBM semiconductor. And you’ve developed a lot of IP around semiconductors, but also around high performance computing. I was struck at IBM Think with many of your announcements. I’m thinking, “I think I’ve seen this before. I think I’ve seen something that’s similar to that.” And then big company like IBM, I’m wondering, “Gosh, big company really changing the game, versus maybe a smaller company.”
And I’m wondering, is this an advantage for IBM, versus maybe a startup that doesn’t have a whole lot of IP and semiconductors and HPC?
Jay Gambetta: The short answer is yes.
Daniel Newman: Leading the witness.
Patrick Moorhead: I think I led the witness on that one, so yeah.
Jay Gambetta: But if you look and you look at the details that go, the reason we’ve accelerated so much on the packaging and the things that go is, we can leverage everything that we’ve done in semiconductors in the past. We’re taking semiconductor physics, superconducting materials and physics, microwave technology, and we’re merging that. And so that semiconductor history, we’re using it all the time, be it from bump bonds to through-substrate vias, to all the things that make traditional computing work really well, we’re leveraging and putting it [inaudible 01:11:43].
I think this is what gives us an advantage and is why I am confident, and we’re working so hard to win that sort of accelerator space. But I do think there will be startups that will come up with key IP in the stack that work with us, or work calling those accelerators. But to compete in the accelerator, it’s going to be hard to compete with the rich history of all the semiconductor knowledge and all the infrastructure that is needed to build these.
Patrick Moorhead: Well, and for years, IBM was king of the hill in HPC. How does HPC relate to this? I don’t want to put words in your mouth again, but I look at the scaling and things like that. How does that help?
Jay Gambetta: I think it comes back to what is the future of computing? I’m putting this word out and starting to see if it sticks, of quantum centric super computing. And the idea there is, we’re going to think of our accelerators, but then we want our accelerators to work with HBC or some more advanced general-purpose classical computing to be able to do more.
So how do we actually start to make workflows that call a HPC and call a quantum accelerator, and how do we integrate that tightly? Can we learn from where classical has gone with serverless on these other technologies to do it? So I think the story of future of computing is quantum HPC, AI, all of these things converging.
Daniel Newman: Yeah, there’s a really strong symbiotic relationship between what… you want us to now say classical computing and quantum. Stop. We’ll stop there. And I think it’s important for people to understand that. You’ve done a nice job here of doing some of the mapping, talking about how, A, the R&D and historic intellectual property development of IBM… Which by the way, I think often doesn’t get enough credit.
Patrick Moorhead: I think, not that patent counting is the ultimate way to view it, but IBM’s been top in the number of patents for, I don’t know, 30 years.
Daniel Newman: A lot of innovation, though, in transistor technologies and IP for semiconductors. But I’m also a guy that likes to talk to markets. I like to talk about practical business value, and you kind of started going down that path. But let’s fast forward a couple years ahead.
We talked about 2025, ’26. What are some of the things that you’re advising to the ecosystem of customers that are going to be adopting this? Financial services, healthcare, chemistry, and of course academia, but all the places that want to really put quantum to use. What does that next few years look like as they prepare themselves for a quantum future?
Jay Gambetta: Yeah. It’s one of the things that we’ve tried to do differently in our IBM Quantum team, is how do you create an offering where you can work with a client that is not research based? Traditionally, if it’s this type of technology, a lot of them start like, “Let’s get together and research on algorithms.” That we still do and is needed, but what’s more important to a lot of clients, they’re asking, “How does quantum fit into my future? What use cases will map to it? How will I be able to explain to my customers quantum, how will I be able to explain to my external stakeholders quantum?”
And so we’ve tried to develop a way. We actually brought people from IBM Consulting, and we made a small team inside IBM Quantum, which we mixed with a few researchers. And they’re exactly doing this with a lot of clients, and why it’s so important is you’ve got to answer all those questions. “How’s quantum going to matter for my business model? How’s quantum going to matter, sorry, for my business. How am I going to communicate that I’m using quantum to my clients? How am I going to get internal stakeholder understanding the value of this?”
And this is all a discussion and relationship coming backwards and forwards. And at the same time, we are learning what use cases matter for these industries. And then our researchers, which are researching these algorithms, they get a bit of guidance of, what type of algorithms should we actually be determining the quantum circuits for? Because we can start to connect those dots. So right now it’s about connecting dots. As we said, our goal is 2023 to have something that’s useful and keep scaling beyond, but it takes a while to connect dots. And I agree with you ’25, ’26 of when it really starts to matter to business, but it’s connecting dots and understanding what is the long term return.
And so I think of the future of quantum computing is going to support various different businesses. We’ve talked about the compute one. It’s going to be an accelerator as well. There will be companies that will have expertise in chemistry. Will they be able to use this compute to come up with a new catalyst, and then be able to use that compute once, but then use that catalyst in many different places? They really got to get the expertise of how to use that compute. And so are the chemistry companies getting involved to work out the compute? But eventually they will want to be a solution, provide some type of solution or create something.
And this goes all the way across finance. They may want to consume the compute to redo calculations, or they may want to do some logistics optimization, and then so they may want to create solutions. So I think we’re going to see all this emerge, but right now it’s about, “How do I understand the value of quantum, and how can I map it to all my stakeholders?”
Patrick Moorhead: That was one of the most understandable, “What do I do next?” So first of all, thank you for that. Maybe it’s just because I have to hear it two or three times to fully understand it. That’s a possibility. But I think it’s a lot of your ability to put it into simpler words. And I think as part of what we need in quantum is maybe a simpler vernacular. And I think naturally we’ll get there. Again, ’25, ’26, I think I’m super excited about.
But Jay, I really appreciate the time. Once again, an incredible discussion about quantum. I learned a lot, which, I guess that’s a good thing, right? Or a good thing or a bad thing if I didn’t study the notes upfront well enough. But I just want to thank you very much for closing out our discussion of the future of computing and what IBM’s doing about it.
Jay Gambetta: Thank you very much.
Patrick Moorhead: Thanks. It’s great talking to Jay. I mean, my biggest takeaway was when things are going to happen. And I know it was on the slides, I know I got the briefings, but 2023 is when the technology’s going to be there for enterprises, not physicists, to start to create something, to provide business value. So we can extrapolate that out to ’25 or ’26. That was my biggest takeaway.
Daniel Newman: Yeah, that really resonated with me as we were sort of looking for that answer. I like to find a way to always create a thread between the technology and the business value. Otherwise it’s pretty nascent, it’s a science experiment, it’s for academia. But we know, as we saw the rise of AI, of HPC, of accelerators, we know that the goal, especially with a lot of things with complex math, is to be able to go faster.
And quantum is sort of the next, well, quantum leap in terms of being able to make workloads go faster and solve some really complex problems that historically classical computing have not been able to solve, or solve very quickly. And so that’s really exciting. And also Pat, thought it was a really nice way to sum up the whole future of computing discussions that we had here at IBM.
Patrick Moorhead: Sure, and it’s interesting. I do like the notion of a QPU, right? This is an accelerator, right? It’s not going to run on an operating system. Sure, you’re going to have APIs and low level things like that, but this is an accelerator similar to a GPU, which then when you come out and you dial out, it’s just another version of… It’s the biggest quantum leap in computing, but it’s heterogeneous computing, which I think we can all relate to, with GPUs as accelerators.
And then if I take it a step further to how accelerators help the cloud and the cloud model, the quantum cloud model that IBM is creating, where if you have an API and you can use it even if you’re not booting up an IBM server or something like that, but you can get access to the IBM quantum accelerator and put it into your application. Maybe even application that you’ve had for 10 years, that you just want to accelerate and make better.
Daniel Newman: Yeah. Well, the future of computing is tying all these components together. Right? It starts with the R&D, it starts with the stuff happening in the labs, building out the next technology innovation thinking five years and 10 years out. And then it’s a lot of the stuff you and I talk about every day. It’s the more practical stuff. It’s the data centers full of compute and GPUs, and then applications and data, and companies, businesses being able to do something meaningful with it.
And so what I really liked with this last conversation was basically how it just tied together everything else. A future is all about getting more from our data. It’s getting more from our systems, it’s being able to solve the bigger, more complex business problems. And so hopefully everybody that joined us for these sessions really got that. They saw how all these threads start to tie together to create real meaningful enterprise value through the utilization of a full stack approach.
Patrick Moorhead: That’s right. Hybrid cloud, the edge, AI, semiconductor IP and technologies, and here we are with quantum. Great place to end. This is a great, fun day. I love Compute, and I know you do too.
Daniel Newman: Absolutely. So thanks everybody for tuning in, we really appreciate you joining The Six Five on the road here at IBM.
Author Information
Daniel is the CEO of The Futurum Group. Living his life at the intersection of people and technology, Daniel works with the world’s largest technology brands exploring Digital Transformation and how it is influencing the enterprise.
From the leading edge of AI to global technology policy, Daniel makes the connections between business, people and tech that are required for companies to benefit most from their technology investments. Daniel is a top 5 globally ranked industry analyst and his ideas are regularly cited or shared in television appearances by CNBC, Bloomberg, Wall Street Journal and hundreds of other sites around the world.
A 7x Best-Selling Author including his most recent book “Human/Machine.” Daniel is also a Forbes and MarketWatch (Dow Jones) contributor.
An MBA and Former Graduate Adjunct Faculty, Daniel is an Austin Texas transplant after 40 years in Chicago. His speaking takes him around the world each year as he shares his vision of the role technology will play in our future.
