On this episode of DevOps Dialogues: Insights & Innovations, I am joined by VP and Practice Lead, Bob Sutor, for discussion of the impacts of quantum computing on AI, DevOps and AppDev.
Our conversation covered:
- What quantum software development is, what people use as their SDK, the platform, rise of Rust with a Python wrapper and WASM
- Quantum, AI and AppDev overlap and market space, including qualification
- TEV, ROI and the economic models
- Why this is important, and why prospects should care
- How this relates to the topics of quantum, DevOps, AI, AppDev, etc
These topics reflect ongoing discussions, challenges, and innovations within the DevOps community.
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Disclosure: The Futurum Group is a research and advisory firm that engages or has engaged in research, analysis, and advisory services with many technology companies, including those mentioned in this webcast. The author does not hold any equity positions with any company mentioned in this webcast.
Analysis and opinions expressed herein are specific to the analyst individually and data and other information that might have been provided for validation, not those of The Futurum Group as a whole.
Transcript:
Paul Nashawaty: Hello, and welcome to another edition of DevOps Dialogues: Insights and Innovation. My name is Paul Nashawaty and I’m the Practice Lead for AppDev and App Modernization here at The Futurum Group. And today I’m joined by VP and Practice Lead Dr. Bob Suter. Bob, would you like to introduce yourself?
Bob Sutor: Sure. Hey, Paul. Well, I joined The Futurum Group, just to be clear, two months and two days ago, so I’m relatively new in this role of analyst but I’ve been around the industry quite a bit. I spent almost four decades at IBM on the business side, but also in IBM research, doing quantum there. I was with Infleqtion for a couple of years, they do all sorts of quantum things. I do a bit of an AI background though within IBM, some of the more advanced topics I would say. Not everything in AI is off-the-shelf. Sometimes you need a bunch of people to figure out how to do the problem the first time and maybe the only time.
So I have a broad range of experience in these technologies, and I was really looking forward to being on this because I was thinking back a little bit to my programming experience, my coding experience, as we like to say. And I don’t want to scare anybody, but I’ve been doing it for 50 years and I started when I was a teenager. But that does not mean I’ve been coding in COBOL for 50 years, I want to be very clear. I’ve used one of everything.
Paul Nashawaty: That’s great, Bob. And I mean, this is an exciting topic and we’re here to talk about the impacts of quantum computing on AI and DevOps and AppDev. And you certainly bring a plethora of knowledge to this space, I mean, talking about the background in this space. And I’ve had the pleasure of interacting with you for several years now in different capacities. So I’m really excited that you’re part of the Futurum team, and the quantum practice is an exciting space.
I think we should just jump right into it. And when we think about quantum and we think … Some people, some organizations, are thinking, “Well, what does quantum have to do with DevOps and AppDev? And why are you on this show?” And when I think of it, I think about it in the context of when you and I have spoken and where the future’s going. Why don’t we tell the audience a little bit about the quantum practice and what’s your vision?
Bob Sutor: Okay. Well, quantum computing is one of these things, people joke, it’s always 10 years in the future. “Don’t worry, it’ll be really useful in 10 years.” Well, the good news is we brought that down to five years. It’s just always five years in the future. Quantum computing has gotten quite a bit of attention in the last almost 10 years now, since IBM put the first small quantum computer up on the cloud a little bit more than eight years ago. There’s a lot of buzz about it. Quantum is this magic word. Somehow you say this and people say, “Tell me more about it?” And they think of Quantum Leap or, what was it, Ant-Man, Quantumania and things like this.
So it’s something that almost sells itself as a conversation starter, but it’s a very different type of computing. And really the point of it is to solve certain types of problems that we cannot do today with what we call classical computers. And we talk a lot about the great progress that’s being made in GPUs, for example, and underneath AI and all these super computers, but there’s still problems that would take these super computers tens of thousands of years, or even millions of years. So there’s this great hope and promise that quantum may be able to give us some breakthroughs. Yeah, an AI maybe eventually, but healthcare, chemistry, all these sorts of things, financial services.
It’s something that is of increasing importance to a lot of Futurum Group clients. They want to know about it. All the attention though seems to be on hardware. And hardware is great, but it’s software, software is what implements the applications that people use. It’s much less known about. And that’s why I think from a DevOps perspective we can talk a little bit about what this new world is becoming for actual developers.
Paul Nashawaty: Yeah. Bob, that makes a lot of sense. And certainly there’s a lot of interest around this, and it’s really in that education phase, it’s that kind of understanding what’s happening. You wrote a book, Dancing with Qubits. That’s a good way to explain how this all comes together. But I know when I wrote a blog a year or so ago, within a few months, like two months, there was over 10,000 views and it just all was focused around the research and what’s going on in the quantum space.
So there’s just a lot of interest. And I think you nailed it. If organizations are thinking about how to solve problems, maybe not with classical computing, but with other approaches and different approaches, quantum may be the approach. Maybe we can touch a little bit about in, say, Horizon 1, Horizon 3, or whatever it may be, what is the use cases that would be the right use cases that we would think to in the short term to use quantum versus maybe what’s out later?
Bob Sutor: Well, let me first say, there are going to be vendors that are going to argue with what I’m about to say. And so what I’m about to say is that, and I’ll try to be a little polite about it, is that for the most part, none of the quantum computers we have today can solve these critical problems that we care about. They are just too small. They’re not powerful enough, they’re too small in terms of the number of qubits. And for those of you who don’t follow this, just as we have bits, zeros and ones classically, we have quantum bits, which are very strange objects and behave according to the rules of quantum mechanics. One of the most confusing, crazy, but evidently true models in physics itself.
So when you’re thinking about coding and you’re thinking about these new applications, it’s not just like, well, what you used to learn and you learn a little bit more, or it’s not like a new language, it’s a completely different way of thinking. We don’t talk about what quantum is doing for us today, we talk about what we hope it will do for us in the future, and we can divide that into really three areas as we’re thinking about use cases. So anybody out there who’s maybe doing classical development and wants to get into quantum or works for certain types of companies and certain industries. So here are the three.
The first is optimization. Now, optimization is a very general type of field. Basically you have some constraints. You want to do something as well as possible. Okay, that’s one. There’s some quantum methods to do that. There’s been a little slowdown on those. We might need a little bit more, but interesting topic. Very useful in financial services, risk analysis, all those sorts of things.
Second is AI, because where would we be if we didn’t somehow connect this to AI in 2024? Everybody’s doing AI somehow, some way. And yeah, before we think too much about GenAI and all that, we got to go lower than that for quantum. AI is math. When you get down to the basis of it, it’s math. So do you have something that can do the math better? And the particular type of math is called linear algebra, if anybody’s had that, basically big matrices. So you think of these huge tables, for example. If you’ve done video games, it’s the same sort of math. You can’t do it with GPUs, but it’s the same sort of thing.
So if quantum can be, if you will, a super math calculator for AI problems, that would be great. Maybe it can help us find patterns that are just not possible classically. And oh, by the way, it’s the same math that’s used in computational fluid dynamics. Now isn’t that weird. Here I’m thinking about AI and all the GenAI and all the stuff people are talking about, and over there I’m talking about air flowing over an airplane’s wings or over a car or water over a submarine and things like this.
The final area, and probably the first really useful one is chemistry. And you might say, “Well, I don’t do anything with chemistry.” Well, if you shampooed your hair this morning, you used something that was a result of chemistry. Material sciences, right, new things in your car. Eventually new drugs, new antivirals and things like that. And that’s because quantum mechanics is what seems to model the universe, it’s very small. And when we get down to the atomic level and trying to figure out what’s going on and interesting reactions and trying to come up with fertilizers, for example, a relationship to agriculture, that we can create using much, much less energy, we think quantum will have a role there.
In this Horizon 1 or phase one, it’s mostly figuring out how might we do this, what might the use cases be? Phase two is going to be a little bit more of, all right, now let’s get practical here. We’ve been playing around for a while, we’ve been experimenting. We have a few key problems we have to get through. We have to solve these problems, so we’ll be knocking those off. And eventually phase three, which I think will be about 2030 or so, that’s when we’ll start really having the use cases.
But for the developers, and I’ll conclude this question with this, quantum is not easy. Don’t let anyone convince you. Quantum made easy, quantum in five minutes. No, no, no, no, no. Over these next few years, if you’re going to get into it, you got to get into it, you have to start thinking in a quantum frame of mind. It’s nothing that you’ve ever seen before, unless you happen to have done a whole lot of physics in your life.
Paul Nashawaty: It’s very interesting, Bob, because when I think about today’s challenges, what’s happening in classical computing, and I think about DevOps, and I want to tie it back to the kind of DevOps and even the CI/CD pipeline and the STLC and such, and whenever I’m talking about my practice, I’m talking about it in the context of past, present, and future. So there’s applications that are heritage that need to be migrated to modernize, and they look at today’s world, and I usually talk about in the context of cloud native and microservices and such. And that’s really cool and that’s great.
But when we start looking at future, there’s a development cycle, there’s that tactical, what has to happen today, the business KPIs, push code out the door faster. And what we’re seeing in our research is there’s just two to three times more applications being created today than just a few years ago with a fraction of the resources, people resources, that were around, which introduces the context of adding in AI and adds in AI for software development, because there’s just not enough people or hands to write the code.
So if we have more AI and automation, it allows for that. But thinking about future, where organizations are looking at quantum and how quantum looking, can we talk a little bit about maybe some of the SDKs that are used, the platform or Rust and Python, how it works within the quantum, the quantumverse. How’s that?
Bob Sutor: Yeah, definitely. And in terms of using AI, GenAI to write code, I wrote up something about two weeks ago for Futurum, it’s a very simple problem. I mean, if you think of this, on HTML page, somewhere either at the top of the screen or maybe on the tab it says the title of the page. It’s not profound. And I said, “All right, write me some Python code that just extracts that title from the HTML,” the original source. And I went to five online services and, boy, I got five different answers.
One was wrong completely. I said, “Read a file,” it didn’t read a file. Two I thought were really pretty good for a while and then I realized they did no error testing whatsoever. And then there was finally one that did the right thing. So today, to be clear, classically, we’re only at the beginning stages of being able to create code you could trust without going through line by line. I said before that quantum is hard. Well, quantum coding is extremely hard and it’s much more like the assembly language sort of code that people did 40 years ago.
We have some high-level languages and yes, you can create things in Python. And here, as you mentioned, the SDKs, the number one is called Qiskit, or something. It’s called Qiskit, it’s Q-I-S-K-I-T. IBM started, it’s open source, but it has had literally more than 100,000 people use it. It’s very good. It just went to version 1.0. It’s been through iterations with the community, you can write your quantum code. Second one’s called Cirq, C-I-R-Q, that’s from Google. A little bit less powerful, but very interesting design, as you would expect from Google, from a very strong computer science perspective. And then there are a couple of other ones, but fundamentally they’re both Python libraries. All right.
So that gets you thinking as a coder, DevOps. All right, well Python’s a good direction. What’s your tool chain there? What can you use in writing and deploying? Oh, by the way, Paul, you can’t actually debug quantum programs because you can’t stop them and look at them. So any developers who are concerned about debugging their code, that’s a minor little problem. Research to be done here.
So this is what they have to get into, you have to start learning these new APIs. But we’ve gotten to the point … Because remember, we talked about the different phases. In this second phase we are scaling up from really tiny problems where you can make something that works in your SDK, yeah, it does kind of a good job, but suddenly things start multiplying, many more qubits doing many more operations.
And so the tools themselves are starting to mature. Underneath the covers, and IBM has started to do this with Qiskit, is to replace some of the Python with Rust. So that is, you look at the efficiencies in your tools. Yes, you want great applications, but to get them and get them quickly, get them efficiently, you need faster, more powerful tools. So you’re seeing DevOps, if you will, being applied to the tools themselves in quantum, which is an excellent sign that the industry is maturing.
Paul Nashawaty: And yeah, that just takes the conversation really to that future state. So we’re building applications, even though they’re being built in classical design, but building them that they’re future-proof or being used … And I don’t really like that word “future-proof” because nothing is really future-proof, but at least it gets you to the point of utilizing where the platform’s going, how it’s going, and thinking about what’s the next generation of technology that’s going to work.
Bob, I want to pivot the conversation a little bit. We talked about use cases, we talked about … One of the things that didn’t come up, and not to throw us a curveball here, but when I think about quantum, one of the things that I hear often is the impacts of AI, but also the impacts of say like cyber, cybersecurity. And when I think about it, there’s rules and regulations that people are putting in place, nations are putting these rules in place. And quantum is really, really powerful for some of these deployments, or maybe really powerful.
What stops, and this is kind of more of a theoretical conversation, but what stops a rogue nation from getting their own quantum setup and just break every rule that’s out there. And how do you defend that?
Bob Sutor: Well, nothing. First of all, I’ll answer that question, nothing, but there is this idea that we better get there first. And let me set up the problem. And the problem really is, I’ll phrase it very generally, cybersecurity. Hey, you’ve got some sort of encryption and decryption scheme that is widely used. Someone else comes along with a new technology that can break that. What do you do? Well, we can talk about crypto agility. “Hey, we’ll just shift to something else seamlessly.” It’s not always quite so seamless.
But that other thing that can maybe break some of the types of encryption are extremely large quantum computers, by no means the ones we have today. So there’s this thought that eventually quantum computers may be powerful enough to break, well, certainly RSA of reasonable size keys, elliptic curves, types of crypto, and then various other ones like this. So various people have gone together. A lot of companies, but also NIST, the National Institute of Standards and Technology, have come out with some standards saying, “These are new protocols, that if you use, we believe,” it’s different from a mathematical proof, “but we believe will not be breakable by a quantum computer.”
So people have to have, as always, an up-to-date, modern cybersecurity infrastructure. The fear is, of course, that people will go out, these bad actors in rogue nations, whoever they’re, and collect data today using today’s encryption schemes, save it, and in five, 10, 15 years decrypt it using a quantum computer. You know what I’d say, well, Paul, do I really care if 15 years from now somebody can read my medical record? Yeah, I probably do care, but it’s not that big a deal. Things have been changed. Well, there are many, many secrets, national secrets, government secrets, military secrets, that have histories or lifespans that are decades, half-century, codes, different sorts of things, information.
So you have to get a smarter scheme today to avoid this gather-now type of situation so people can decrypt it. It’s not so much of a quantum problem, if you will. Quantum will cause the problem maybe, but this is why security is always front and central. Those rogue nations of yours, or bad actors, it’s always about security. It’s more of the same. So yeah, get on it.
Paul Nashawaty: I think that this is a good topic for the audience to keep in mind, as they’re thinking about the next generation, as they’re thinking about looking at those standards, that NIST standards, that’s the good location to use as a source of truth for right now. And if you-
Bob Sutor: If I may, and we have colleagues who specialize exactly in this and cover the so-called post-quantum crypto.
Paul Nashawaty: No, absolutely. It’s definitely something that the audience should be aware of as we’re looking at future state, future thinking, keep in mind. Well, Bob, we’re coming up to the end. I have one more topic I want to talk about, and that would be the AI and the impacts of AI and what that means to AppDev. How does quantum impact AI? We talked a little bit around it, but what’s the market overall, including qualifications around? What is happening in the AI space today with quantum?
Bob Sutor: Well, with quantum itself, very little. It’s really at the research phase of using quantum for AI. A lot of PhD theses are going to be out there. People are beginning to get little hints of, “Oh, maybe this will be useful, but not really practical.” We can however turn this around and say, is AI, is machine learning being used within quantum? Quantum computers are extremely noisy. And one way of thinking about this without getting too technical, is think of static. You’re listening to some sort of music maybe on a plane, and there’s all this extra noise. And your brain’s pretty smart, you can just make it out. You have noise-canceling headphones. Well, AI will be and is being used for that kind of noise-canceling for quantum computation.
So the interesting connection here is that we will use AI to make quantum computing better and more powerful sooner so we can apply it to AI. So it’s this total story here like that. In general, if I might just say, I’ve been in a number of client briefings recently, and of course AI is a big topic for that. And something that hit me was the best AI is the AI you don’t even know that’s there. Where you’re trying to do a task and maybe you’re doing something today and you say, “Why is this so annoying? I’ve already told you who I am. Can’t you figure out what I want to do?” And you’re more likely to say, “Oh, this isn’t working,” as opposed to saying, “Gee, that was wonderful. That was a great experience. I did it really quickly.”
But that’s where the AI is going to be this glue between all the information and the processes you’re trying to accomplish. And that’s going to be the best. Right now, as we always do in this industry, we are geeky. We use technical terms like generative AI, very fancy, technical things. And in a few years, “Why is it good for me? What’s its value to me?” So there’s a lot going on right now, there’s a lot of sorting out, but there are some truly innovative companies who are using it for these real problems that we need to solve sooner rather than later.
Paul Nashawaty: Yeah, maturity in this space is definitely on multiple ends of the spectrum. I mean, on one side, when you were talking, I was thinking about going to an appointment and having to fill out paperwork and entering the same information on physical paper and pen on the same form. That’s so tedious. And then you have the other side where you have full automation and it’s working pretty well.
But there’s a bell in the middle and I think the learning is going to continue to occur here and it’s going to continue to grow, especially as we look at that past, present, and future of taking the old and migrating it with the new and bridging the gap between the two. But with that, as we come to the end, Bob, I’m going to give the mic to you and see if you want to leave the audience with any final words.
Bob Sutor: Sure. Well, what you and I just talked about, this kind of seamless, don’t be annoying, use AI to make my life easier, well, you do have to have some guardrails. There’s a question of privacy. I do have to give explicit permission. If I want you to make my life easier I don’t want you peeking into things you shouldn’t be peeking about. The usual topics of bias. I think right up there with what the technology is, is that whatever you call it, responsible AI is a good term for this, it must be one of your primary and first considerations.
I’d mentioned, I’ve been programming for just a little while. At some point, a few decades ago, we started to tell people, “You must think of security at the very beginning.” We’re still telling that. One of our colleagues, I saw something, said that this morning. We’re still telling people this. It’s not an add-on. Well, neither is responsible AI, neither is privacy, all these types of things. And in fact, it’s going into laws. So whether we have quantum that’s going enhance AI, ultimately it comes down to data, where does the data come from, and you have to have a fundamental set of principles from what you start.
So it’s an exciting period. We are sorting all this out, and it’s absolutely a remarkable time to be any sort of developer or to be in a company that’s creating new products. We’ve never had this power, we’ve never had … I mean, between quantum and AI, we’ve never had such brand new innovative things, tools like we’ve never had before. So it’s going to be a little lumpy for a while, but the possibilities are extraordinary.
Paul Nashawaty: And we’ll figure it out. We’ll figure it out together. Bob, it is amazing to be working with an expert like yourself in the field and learning from you from this. This is definitely a growing field. And we just hit the tip of the iceberg with this because it’s really just a little bit of a teaser for the conversation. If anybody wants to follow up with Bob and myself, feel free to reach out to us at The Futurum Group.
But I also want to thank the audience for watching today’s session. This is, like I said, just the early stages of some of this conversation, and we’d love to continue the discussion with you. So with that, thank you, and we appreciate your time and have a great day.
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Author Information
At The Futurum Group, Paul Nashawaty, Practice Leader and Lead Principal Analyst, specializes in application modernization across build, release and operations. With a wealth of expertise in digital transformation initiatives spanning front-end and back-end systems, he also possesses comprehensive knowledge of the underlying infrastructure ecosystem crucial for supporting modernization endeavors. With over 25 years of experience, Paul has a proven track record in implementing effective go-to-market strategies, including the identification of new market channels, the growth and cultivation of partner ecosystems, and the successful execution of strategic plans resulting in positive business outcomes for his clients.
Dr. Bob Sutor has been a technical leader and executive in the IT industry for over 40 years. Bob’s industry role is to advance quantum and AI technologies by building strong business, partner, technical, and educational ecosystems. The singular goal is to evolve quantum and AI to help solve some of the critical computational problems facing society today. Bob is widely quoted in the press, delivers conference keynotes, and works with industry analysts and investors to accelerate understanding and adoption of quantum technologies. Bob is the Vice President and Practice Lead for Emerging Technologies at The Futurum Group. He helps clients understand sophisticated technologies in order to make the best use of them for success in their organizations and industries. He is also an Adjunct Professor in the Department of Computer Science and Engineering at the University at Buffalo, New York, USA. More than two decades of Bob’s career were spent in IBM Research in New York. During his time there, he worked on or led efforts in symbolic mathematical computation, optimization, AI, blockchain, and quantum computing. He was also an executive on the software side of the IBM business in areas including middleware, software on Linux, mobile, open source, and emerging industry standards. He was the Vice President of Corporate Development and, later, Chief Quantum Advocate, at Infleqtion, a quantum computing and quantum sensing company based in Boulder, Colorado USA. Bob is a theoretical mathematician by training, has a Ph.D. from Princeton University, and an undergraduate degree from Harvard College.
He’s the author of a book about quantum computing called Dancing with Qubits, which was published in 2019, with the Second Edition released in March 2024. He is also the author of the 2021 book Dancing with Python, an introduction to Python coding for classical and quantum computing. Areas in which he’s worked: quantum computing, AI, blockchain, mathematics and mathematical software, Linux, open source, standards management, product management and marketing, computer algebra, and web standards.
