Bob Sutor - VP of IBM Q Strategy

(Joel Beasley at 00:00:00) Hello, my friends. Today we are talking to Robert Sutor, the VP of IBM Q Strategy and Research, and we discuss advancements made in quantum computing, knowing who your target audience is when delivering a message, and identifying the characteristics about yourself that will give you satisfaction in what you do. All of this right here, right now on the Modern CTO Podcast. Here we go.

(Joel Beasley at 00:00:34) This is the Modern CTO Podcast. Hello.

(Robert Sutor (Bob Sutor) at 00:00:38) Hi, this is Bob Sutor.

(Joel Beasley at 00:00:40) How are you?

(Robert Sutor (Bob Sutor) at 00:00:41) Okay.

(Joel Beasley at 00:00:42) I like those fish. Are you in a cabin?

(Robert Sutor (Bob Sutor) at 00:00:45) Yeah, this is a place up in the Northern Adirondacks in New York State.

(Joel Beasley at 00:00:52) Okay.

(Robert Sutor (Bob Sutor) at 00:00:53) So we're on a lake.

(Joel Beasley at 00:00:57) Oh, that's beautiful.

(Robert Sutor (Bob Sutor) at 00:00:59) And yeah, I just happen to have that picture of trout back there.

(Joel Beasley at 00:01:06) Is that your standard summer getaway?

(Robert Sutor (Bob Sutor) at 00:01:08) Yeah, yeah. We bought it a few years ago and when we first got it, it was kind of remote. There was this really pathetic satellite internet and a landline.

(Robert Sutor (Bob Sutor) at 00:01:21) And within a year we had fiber optic and cell service. So during the summer, I try to work remotely from here as much as possible.

(Joel Beasley at 00:01:30) Nice.

(Robert Sutor (Bob Sutor) at 00:01:31) Because in the winter it gets to minus 30 and I don't come here.

(Joel Beasley at 00:01:37) No, you stay far away. Where do you stay when it's minus 30?

(Robert Sutor (Bob Sutor) at 00:01:42) So my home is near Rochester, New York.

(Joel Beasley at 00:01:45) Oh, yeah.

(Robert Sutor (Bob Sutor) at 00:01:46) But I work out of the research lab north of New York City. But really the story is I go where I need to be. So if I'm with a client or I'm giving a talk or I need to be at the lab, I just make it work.

(Joel Beasley at 00:02:02) I spent a lot of time up there in between the Buffalo, Rochester, Syracuse area.

(Robert Sutor (Bob Sutor) at 00:02:08) Oh, okay. Were you in school up there?

(Joel Beasley at 00:02:10) No, just doing software. Software, yes.

(Joel Beasley at 00:02:14) I would eat at Wegmans or Charlie the Butcher when I was in Buffalo. I just spent time bouncing between those three cities. It was a good time up there.

(Robert Sutor (Bob Sutor) at 00:02:23) You know, it's funny, we joke about it, but if you're not from the area, I don't think you realize the impact that Wegmans has on quality of life up there. We live in a village about 30 miles south of Rochester, and my wife was a history professor, and so when she was interviewing for her job there, they actually took her to Wegmans to say, "Yes, this is the university, this is all this, but let's go to Wegmans to prove that you're not in the middle of nowhere, that you can buy bagels," things like that.

(Joel Beasley at 00:02:58) It's beautiful. The moment I came back to my town, we had no grocery stores like that. We do now, but I just was sad because I lived up there for three or four months while I was doing software, and it was just so useful. It was such a brilliant idea that then spread throughout the rest of the United States.

(Robert Sutor (Bob Sutor) at 00:03:19) Yeah, it's interesting because it's a family-owned company, which very much changes how they do things, why they do things. Obviously they have their own economic reasons for the expansion or doing what they do, but it's not driven by the stock market, which is different.

(Joel Beasley at 00:03:40) It's different. Now, were you born in Rochester?

(Robert Sutor (Bob Sutor) at 00:03:44) No, I was born just north of New York City in a city called Yonkers.

(Joel Beasley at 00:03:49) Okay.

(Robert Sutor (Bob Sutor) at 00:03:51) And then when I was about 11 years old, we moved a little bit further north. But I was part of the second generation born in the United States. So many people from Europe, they came over, they went through Ellis Island. They scattered, but there was huge concentrations of people near New York City. So my ancestors did that route, and then there was the diaspora as further generations got further and further away. So I was born down there, and in fact, the lab, the IBM lab, is 30 miles from where I was born, but I now live 300 miles. I just forgot to stop working there, if you will.

(Joel Beasley at 00:04:37) Now, did you get interested in technology when you were around 11? When did that first happen for you?

(Robert Sutor (Bob Sutor) at 00:04:44) It really happened when I was about 14 or 15. So, you have to remember the times. I'm 61 years old just to put this in. And so in the early '70s—so I was 15, 1973—this was when computers were first being made available to schools, and they were stunningly primitive compared to what we have. And they were really teletype systems. So somewhere there was this computer, it was remote, but you would have basically these teletype machines where you could program. In one case, we had an old adapted IBM Selectric typewriter which operated as a terminal. So everything was done on paper. There were no screens at all. So I had a math teacher, George LaRose, who brought this into the school, and I just started coding. And you stored your programs on paper tape, you might see in some of the old films, this tape with the dots punched in it.

(Joel Beasley at 00:05:57) Yeah, the punch cards.

(Robert Sutor (Bob Sutor) at 00:05:58) Yeah. So I wrote a word processor in APL around 1975.

(Joel Beasley at 00:06:04) And then you were hooked?

(Robert Sutor (Bob Sutor) at 00:06:06) Yeah, yeah. I always liked computing. The nice thing about it, I like building things. So I like building physical things, but programming and coding is like that as well. You can put things together, you can rip them apart, you can put them back together again. And that always appealed to a certain part of me. And then along the line, of course, I learned more about computers and computer science and mathematics.

(Joel Beasley at 00:06:35) Did that drive your decision of what college to pick, where you could get some compute, some processing time?

(Robert Sutor (Bob Sutor) at 00:06:41) No, I wasn't thinking in those terms. When I think about college, not a lot of people historically in my family had gone to college. And so I was doing very well in school and I had my heart set on going to the University of Rochester, oddly enough. And the main reason was I had their catalog. And back then you had paper catalogs, and it was this bible of, "Wow, you can learn all these different things." And I just thought this was amazing, and I wanted to go there and make beautiful photos and things like this. But then I applied to a number of schools, and one of the ones I got into was Harvard. And I said, "Well, I should probably go to Harvard. That seems like a good idea." And that was enough reason, if you will, to decide where to go.

(Joel Beasley at 00:07:32) Yeah, when Harvard invites you, you say yes.

(Robert Sutor (Bob Sutor) at 00:07:35) Yeah, I figured they could pretty much handle anything I sort of wanted to do.

(Joel Beasley at 00:07:41) And then so now you're doing a lot of research. You've got a lot of experience in quantum computing and AI. Tell me about what you're learning.

(Robert Sutor (Bob Sutor) at 00:07:49) So when I was back in school, undergraduate, then I have a PhD in mathematics, theoretical mathematics, not applied mathematics. Theoretical mathematics, which was for the beauty of it, the philosophical aspects, the intellectual elegance of it and things like this. So I very much, in particular, saw physics as applied math in a way, and so I wanted nothing to do with it.

(Robert Sutor (Bob Sutor) at 00:08:21) Now, on the other hand, the split part of my personality was I was doing computer science. And in fact, I divided up my graduate study where I did two years in theoretical math. I took a leave of absence, really for two reasons. One was I was doing more computer science, and the other was, frankly, my fiancée was starting graduate school 300 miles away. That's 300 miles. The reason why I said years—

(Joel Beasley at 00:08:52) You guys are time traveling over there, aren't you?

(Robert Sutor (Bob Sutor) at 00:08:54) Well—

(Joel Beasley at 00:08:54) In your research facility. Yeah, it's okay.

(Robert Sutor (Bob Sutor) at 00:08:56) A little slow. The reason why I said years was because today is actually our anniversary.

(Joel Beasley at 00:09:02) Oh, congratulations.

(Robert Sutor (Bob Sutor) at 00:09:03) Thirty-six years. Yeah. So that seemed to be a good decision to move to New Haven, Connecticut. But then I joined IBM. I'd been a summer student there, ended up in IBM Research, ended up in the math department. They sent me back to school to finish. But I had choices. I could have gone the professorial route, but I just really liked IBM. I like research, I like all the different things you could work on, and then eventually I did the business thing. So just to fast forward really to your question, it's somewhat ironic that 35 to 40 years later, now I'm learning a lot of physics, quantum computing. And it's fun. It's fun to be able to—you know, I still remember the math, and it's fun to get underneath it and to be thinking in those terms again. And I understand why, in fact, they did some of the theory in math because they needed it for the physics, which I didn't know back then.

(Joel Beasley at 00:10:13) So how would I explain quantum computing versus regular computing to a board or investors?

(Robert Sutor (Bob Sutor) at 00:10:21) So let me start by saying how you probably wouldn't explain it, and that is the way many of us have been explaining it for a long time. And it's as if—so I'm holding up my smartphone, and in this case it's an iPhone. And your question would be like, "How would I explain how to use the smartphone to an everyday person?" The way many of us in the past have thought about explaining quantum computing is saying, not, "Oh, well, here's the screen and these apps and they do nice things." It's like, "Well, really to understand your phone, we have to understand the physics of transistors. And once you're fully comfortable with the physics of transistors and we work completely up through the entire software stack, then I'll show you how to use an app."

(Robert Sutor (Bob Sutor) at 00:11:15) So with quantum computing, we spend a lot of time on the physics, what corresponds to the transistors. And because people are so enamored of certain phrases like superposition and entanglement—and they sounded amazing.

(Joel Beasley at 00:11:31) They do sound pretty good. I'm sold. I'm buying it.

(Robert Sutor (Bob Sutor) at 00:11:34) Yeah. You know, and it's like the nomenclature will sell you for some reason. What it boils down to is this: No matter how powerful your existing computers are, there are some problems that will remain completely intractable, and it's just the nature of the beast. When we say "a computer," we don't only mean those sorts of computers, the binary computers that we can build today.

(Robert Sutor (Bob Sutor) at 00:12:00) So what about these really hard problems? Are we hosed? Do we just never be able to do it? And the answer is, well, no, there's actually this glimmer of hope. And the idea is that if we have a completely different kind of computer, which is based on different principles, really from the very bottom all the way up, maybe this different type of computing is more amenable to solving these hard problems. In fact, that's exactly what quantum computing does. So now to the board, I would then say, "And now let me tell you a few of the sorts of places where we think quantum computing might be really useful." So you ground it in saying what's different about it, but from the perspective of computability, and then you talk a little bit about the use cases. And depending on who the board may be—and I do speak to boards, and you have to change the message—you then try to adapt it for their industry.

(Joel Beasley at 00:12:58) So I might choose quantum computing based on what I want to compute?

(Robert Sutor (Bob Sutor) at 00:13:04) Yes, the nature of the problem. That's right. And the easy area, if you will, to identify relates to problems that physicists sometimes refer to as having a quantum nature. And here we mean—let me give you an example.

(Robert Sutor (Bob Sutor) at 00:13:26) So we have amazing supercomputers on the planet. They're petabytes and petabytes of information, huge number of processing cores. They're stunning. Now I'm gonna give you a simple example of a molecule, and this is caffeine. And I use caffeine because everybody knows what caffeine is. It's a very small molecule. Now my goal here is to essentially take this caffeine molecule out of the coffee cup and the test tube and the wet laboratory, but completely represent it faithfully inside a computer. So thinking about every sort of chemical reaction you might do in a test tube, I want to be able to simulate on a computer because it's not a big molecule. How hard could that be?

(Robert Sutor (Bob Sutor) at 00:14:14) And if I could do that, if I could take molecules, which are atoms and things like this, maybe if I think about the future, I could create new drugs. So instead of discovering drugs, I could compute them. I could see in the computer how they interact with other molecules like those in your body. So therefore, okay, it's reasonable. Stick the molecule completely inside the computer, completely model it perfectly, and then away we go.

(Robert Sutor (Bob Sutor) at 00:14:48) Well, it turns out that even this caffeine molecule has way, way too much information than you can reasonably fit in any sort of computer we have now. And the estimate is that even to just represent all the information about the energy that holds the molecules together and all the electrons, you could use as many bits, as many zeros and ones, as about 10% of the size of the planet in terms of atoms. 10 to the 48th zeros and ones. And the size of the planet atom-wise is 10 to the 49th to 10 to the 50th. One molecule, one instant, a tenth of the size of Earth in terms of the amount of storage you need. That's not gonna happen. We're never gonna build storage like that of classical computers. So again, use my expression: Are we hosed?

(Robert Sutor (Bob Sutor) at 00:15:43) Well, it turns out you could store that much information in a quantum computer with 160 qubits. And the qubit is a quantum bit. It corresponds to the bit, but it's much more sophisticated in terms of what you can do with it and how you can store information. Today, our biggest computer is 50. If you kind of squint into the future, you can imagine, well, these guys seem to have been working on this for a while. With luck and hard work, we'll get to 160. We'll get beyond in the future. So you have something, a very simple example, completely impossible forever classically. Okay, it seems like we might be able to do that.

(Robert Sutor (Bob Sutor) at 00:16:23) So this whole notion of chemistry and everything involving chemistry, which, by the way, is everything in your body and everything in the room behind you—it looks like we might be able to do some interesting work with quantum computing as one of the areas.

(Joel Beasley at 00:16:38) I like it. And so what you're talking about when I'm just gonna show how little I know here. So when you're talking about representing caffeine, there's softwares out there that represent these digital structures, but you're talking about at a different resolution. Right?

(Robert Suter (Bob Sutor) at 00:16:56) I'm talking about not approximating it. That's right.

(Joel Beasley at 00:16:59) Right. Okay. So you're talking about actual.

(Robert Suter (Bob Sutor) at 00:17:01) Yeah. An exact representation. So 100% correct and complete model of that molecule.

(Joel Beasley at 00:17:10) Whereas the softwares today that drug research companies use, things like that, they're just using representations that are watered down versions of it in order to do the modeling they need to do.

(Robert Suter (Bob Sutor) at 00:17:22) That's right. To a few decimal points and only parts of the molecules. Got it. And it helps. I mean, there's no question. We'll certainly keep it.

(Joel Beasley at 00:17:30) We'll take it.

(Robert Suter (Bob Sutor) at 00:17:31) We'll take it. We're not gonna throw it away. In fact, one of the things we do on the software side of quantum computing through the open source project we're involved with which is called Qiskit, Q-I-S-K-I-T, we interface with existing chemical systems, so chemical software. So if you're very familiar, if you're a chemist, you're used to using this sort of software. Can we somehow, under the covers, start talking to a quantum computer so that, you know, you operate the way you like to operate, but suddenly, you know, your calculations become far, far more accurate than they were before?

(Joel Beasley at 00:18:09) That's amazing. So you're saying that there's hope for a bridge between quantum computing and classical computing.

(Robert Suter (Bob Sutor) at 00:18:16) Yes. And in fact, the future is such a hybrid. We're going to so, I mean, just going back to my smartphone example, you know, there's no reason to have a quantum user interface. Classical computing works very well. Thank you. So it's seeing, you know, which technology is best for which use.

(Joel Beasley at 00:18:38) So that, I'm sorry. You've just got me so interested. So is that storage density only for specific types of data, like the caffeine? Like because that's a huge storage density you were talking about.

(Robert Suter (Bob Sutor) at 00:18:51) So we have to move from, let's say, storage to the idea of working memory.

(Joel Beasley at 00:19:00) Okay.

(Robert Suter (Bob Sutor) at 00:19:00) So this is the idea. So with quantum computations, it doesn't give you, like, disk storage, hard disk storage. What it does is while you're in the middle of a computation, there are certain cases that just blow up exponentially. All the different possibilities you would have to consider. Right? And in this case, it's all the different electrons, the relationships among them, the different states they can be in. Right? That if you were to try to deal with it classically, you would either run out of room or run out of time. So another example that people are looking at, because they're not all chemistry, is, let's say you have a hedge fund. Okay.

(Joel Beasley at 00:19:45) Yeah. Everyone's been working.

(Robert Suter (Bob Sutor) at 00:19:47) I, it's my living project. Well, I'll bring it back home. You know? But so you have a hedge fund. And the idea is that, look, you've got a lot of financial instruments. You know, some of them are run of the mill stocks and bonds, but then you've got derivatives and things like this. But the point is that they're all kind of related to each other. Right? So in the last few days, we've seen international currency actions, which affect a lot of different stocks and instruments. If you have a hurricane that's about to hit Florida, well, the price of plywood. Right? You know?

(Joel Beasley at 00:20:29) Now you sound like Ray Dalio. Yeah.

(Robert Suter (Bob Sutor) at 00:20:31) Well, you know, they're related. Right? They are. And so the idea is that, you know, when you have this portfolio, it's not lots of individual, completely discrete things. And so if I want to assess the risk, right, for each of my financial position, or I want to even, like, how much is this thing worth at the moment? I gotta say, well, but what if it's affected by that? And what if it's affected by this? And this is affected by this? The combinatorial explosion of the relationships among these just you can't do this on a classical computer. So you choose a small subset, or you do a calculation that takes hours.

(Joel Beasley at 00:21:12) Mhmm. And so—

(Robert Suter (Bob Sutor) at 00:21:13) Now people say, well, you know, wouldn't it be great if I could do the whole thing for everything I'm interested in in five minutes? Because then I can run it many times a day, and I could get more accurate information. Now for you, okay, you may not have your personal hedge fund, but you may have a retirement account. Right? And all the different stock combinations and things like this. So the same techniques that could be used to optimize what the billionaires use in their hedge funds could be used to optimize on a much more frequent basis the blend of stocks and bonds and whatever you have in your accounts as well.

(Joel Beasley at 00:21:52) Now, you know what? This sounds like a great use for. So just this weekend, I geek out on the weekends. Like, I look up future technologies, things of that nature, and I came across storage and DNA. Have you come across this?

(Robert Suter (Bob Sutor) at 00:22:06) I've seen glimpses of it. Yeah. Okay.

(Joel Beasley at 00:22:09) The advancements are huge. Essentially, today, we can take a data center and put it into a sugar cube.

(Robert Suter (Bob Sutor) at 00:22:13) Mhmm.

(Joel Beasley at 00:22:14) Right? I think it's, like, 10 to the 19th of the storage density of what they currently have. I'll get you the actual company so you can watch their video about what they're doing. Okay. But it's primarily for long term storage. It's not for short term storage. It's, like, for long term backups, they would do this. And so they're building these machines that then write it to DNA, and then they actually store it. And they put Wikipedia into DNA. It's, like, amazing. But they were talking about extracting it and how all the information is kind of everywhere at the same time. And if you watch the video, for some reason, there's this big connection right now in my mind between the way you're describing how quantum computing is useful and the problems that exist that they're trying to solve in this long term DNA storage on how to access it, like, at the same time.

(Robert Suter (Bob Sutor) at 00:23:06) Mhmm.

(Joel Beasley at 00:23:07) So there may be something interesting there.

(Robert Suter (Bob Sutor) at 00:23:09) Yeah. I think people will be experimenting like that. So here you're talking about, I guess, what I would call tertiary storage. Right? So primary storage is, you know, what's on your phone, what's on your laptop, maybe you have a backup in the cloud, which you could access. So that's secondary storage. And the tertiary is go take it, stick it in a mountain somewhere, and I'll get it, you know, when I need it. In quantum, we're looking at storage as well. And that brings us back to a point about quantum computing, that the way quantum computers work today are different from what you read in textbooks. And that is there's a difference between the pure mathematics and physical descriptions of qubits and algorithms and all these things from the actual implementation of qubits. So we have, you know, we build what are called physical qubits. And these are, you know, basically, we create synthetic atoms or quantum particles, and we use them for computations. But because they're physical, there are some errors associated with them. Now they're not perfect. So you don't set a value and then go away, and you come back in a few hours, it's still there, like you're very used to in your laptop and phone and whatever else. So there's something called coherence time, which is the amount of time you have to work with a qubit, which is well less than a second. There are error rates. These things operate, our qubits operate close to absolute zero. So at absolute zero, nothing moves. And we use the Kelvin scale. Your body, if I remember this correctly, is something like 315 degrees Kelvin. Right? Outer space is 2.7 degrees Kelvin. We keep it to 0.01 Kelvin. That's how cold the thing is because of all the different quantum effects. So we have to, and the way progress is going to happen is to decrease these errors and eventually get to error correction. When we get to error correction, fault tolerance, we should be able to build quantum memory. And so all the storage I talked about with caffeine or in some way what you were talking about with DNA, we hope over the next few decades, we'll be able to do something similar as well. But we're not there yet. That's probably at least a decade away.

(Joel Beasley at 00:25:34) So right now, we can process. We just have a very short window that we can run computations?

(Robert Suter (Bob Sutor) at 00:25:39) That's right. That's right. So it limits the number of steps you can run, but research is showing that, in fact, you don't always need that many steps. So this is, I think a lot of people associate anything with word quantum, associate quantum with physics, but it's really become yes. It's physics, it's engineering, but it's computer science, it's mathematics, and all these things like this. So it's and software, very much so.

(Joel Beasley at 00:26:06) So when you're solving all these hard problems, I found in life that engineering the right questions is vital to your success. Like, how do you go about asking the right questions?

(Robert Suter (Bob Sutor) at 00:26:17) So, well, first of all, here's a little secret about quantum is today, even though we have some quantum computers, we cannot solve anything faster on a quantum computer than you can on a classical computer today. We hope to in the future, but they have to be bigger, and they have to be more powerful. And we use this metric called quantum volume. Just like you used to measure gigahertz, right, the speed of your processor, maybe 15 years ago. It's not just the number of qubits. Please never discount the number of qubits. It's a terrible way of seeing how powerful. You need very good qubits that can talk to each other, and you need to be able to program them and then find out what the result is and all these things like this. So there are lots of different pieces, and quantum volume measures this. So right now, what people are doing is exactly what you described saying, what are the use cases? Where do we think quantum might be useful? And as I described it a little bit earlier, there are these situations where there's a little bit of data, you know, like my portfolio. Okay. We can get going here. And then suddenly, inside the computation, there's just this blow up. You know, there's so many combinations to consider. So that's where we look for quantum. So what we do now is saying, okay. That's a potential use case. Can we get on the right track? What algorithms might we apply to this problem so that as quantum computers get more powerful, our solution will scale and then be applicable? We have estimated that within 10 years, we should get to that point, and that's what we call quantum advantage. And that's the phrase we use, and it's very specifically about useful examples. It's not about artificial benchmarks that no one cares about, no one will use. It's like in finance, in health care, in chemistry, in AI. Right? All these areas where it really shows a difference. So that's the conservative estimate. Quantum volume should be big enough in 10 years. We also, though, say, you know, sometimes things happen a little bit faster. There are no guarantees here, but we wouldn't be shocked in three to five years because we make progress sometimes a little faster. People are exceptionally clever. Not just us. Right? I mean, people out there using this. And we wouldn't be surprised if people came up with this. So a huge part of what we do is not just building hardware. Right? Not just running software. It's teaching people how to code in quantum. We started the IBM Q Experience three years ago in May, put it on the cloud. We've had 145,000 people register. They've run 27,000,000 quantum calculations.

(Joel Beasley at 00:29:17) Hold on a second. Now you're telling me I can go and run a quantum calculation and write some quantum code right now?

(Robert Suter (Bob Sutor) at 00:29:23) You have five minutes. Go and do it.

(Joel Beasley at 00:29:24) Get out of here. Tell me where do I go?

(Robert Suter (Bob Sutor) at 00:29:26) Just do a web search for the IBM Q Experience. Log in, and you can get an account, but also the normal sorts of things. If you have a Google account, you can log in to the IBM Q Experience. You can go to what we call the Composer. You can drag and drop, create a little circuit. You can simulate it, or you can run it on a real quantum computer. If you want to do coding—

(Joel Beasley at 00:29:48) I can run something on a real quantum computer right now.

(Robert Suter (Bob Sutor) at 00:29:51) Yeah. Yeah.

(Joel Beasley at 00:29:52) I go to IBM Q Experience. I search that, and then there will be some sort of, like, cloud quantum computing—

(Robert Suter (Bob Sutor) at 00:30:00) That's right.

(Joel Beasley at 00:30:00) Service.

(Robert Suter (Bob Sutor) at 00:30:02) Absolutely. And it's been there in various forms since May 4th. May the fourth be with you? 2016. Yeah. I know. It's the only way I remember it. It's 2016. Yeah. And the software stack in the latest version, you don't have to download anything. So, previously, if you were coding using Qiskit platform, which is written Python 3, you would download it, you would do Jupyter Notebooks on your laptop, you can do all that in the cloud now. You don't have to download anything. With Qiskit, though, just like another stat, 210,000 downloads. So maybe this is a little further along than some people have expected. Right?

(Joel Beasley at 00:30:41) I'm loving it.

(Robert Suter (Bob Sutor) at 00:30:43) You know? So, what's stopping people? We're trying to, you know, it's and it's no charge, by the way. You know? So it's no charge to use the IBM Q Experience and for the public offering, and the software is open source. So use it, learn about it, be that smart person who finds quantum advantage in whatever field, you know, you're interested in.

(Joel Beasley at 00:31:08) Have you seen any, like, widely applicable business use cases that we could throw out there?

(Robert Suter (Bob Sutor) at 00:31:16) So the way we've tried to stimulate that is through something called the IBM Q Network. Okay. Now so these are partners like JPMorgan Chase, ExxonMobil, Daimler, Mercedes. Altogether, we have over 70 different organizations. So we have Fortune 50s, several of those in different areas. We have many startups that are part of this. We have an academic program for really universities around the world. I mean, we cover, we have universities in the United States, Canada, Japan, many countries in Europe, South Africa, several in Asia, I said. And it's, for universities, whether to do research or for education, startups, obviously, you know, to whatever they feel they can do, and many other companies that just wanna get it up and running with this. The Q Network people, the premium clients have access to our latest and greatest machines.

(Robert Suter (Bob Sutor) at 00:32:21) So that is a commercial program, but as we come up with the newer quantum computers, they have first access to those. So those are the ones. So if you think about, you know, why would an ExxonMobil care? Why would a JPMorgan care about these? They're in their different areas.

(Robert Suter (Bob Sutor) at 00:32:41) We have started to do research papers. So we published a chemistry paper with Mitsubishi Chemical a few weeks ago, and so that's generally applicable. Right? And we have several other papers coming out with these partners as well.

(Joel Beasley at 00:32:56) That's awesome. So they'll have to check out the IBM Q Experience, play around with it a little bit, figure out what the good excuse is to keep working on it at work. Right? Because everyone wants to work on something cool at work. Right?

(Robert Suter (Bob Sutor) at 00:33:10) Yeah. Yeah. Well, you know, maybe it's just part of continued education there you go in the short term. But, you know, it's the software stack is evolving. Right? Many people, if you were to go to college right now, if you weren't majoring in computer science, you would just learn, let's say, a high level language. You'd learn Python, maybe R for data science, and things like this. Even in computer science, you know, the difference between now and twenty or thirty years ago, I learned assembly language. Because, of course, you can't go to computer unless you know at the very lowest level how to code.

(Robert Suter (Bob Sutor) at 00:33:49) We've decided maybe not everyone needs to learn. Right? But with quantum, we're sort of at that level, and people do look at and use the whole stack. So we're rapidly trying to make the top level, the user friendly libraries more useful. That's where we interface with chemistry. But at the lowest level, you're really using gates. And it's not ANDs, NORs, and NOTs in the logical gates, but you're using quantum gates.

(Joel Beasley at 00:34:16) That sounds exciting.

(Robert Suter (Bob Sutor) at 00:34:18) It's kind of cool. That's what you can use in the Q Experience. You can drag and drop and create these things and see how the classic algorithms work for quantum.

(Joel Beasley at 00:34:26) Yeah. I'm gonna play with it for sure because this is just—I don't, if you have to choose between like, you personally, I'm just gonna make you a choice. Like, you never want to choose between two kids, but here we go. Okay. Quantum computing and AI, which one is, let's say you're sixteen years old again, and you have to invest time into one of these two things. Which one do you pick?

(Robert Suter (Bob Sutor) at 00:34:53) It's different because there are different reasons to do it. Right? And you can do quantum for AI as well. So I think, so I personally have always been interested in programming languages, for example. And so for that reason, I would have tended to go toward quantum computing.

(Joel Beasley at 00:35:16) Just be a person. Yeah.

(Robert Suter (Bob Sutor) at 00:35:19) If you are somebody who is far more data driven, right, thinking about making sense of all this information, then you would probably do the AI approach. Right? So quantum would be my personal choice, but I certainly wouldn't slide anyone, you know, who called AI. And they should start thinking about how eventually quantum may help AI. And there's some interesting cases there.

(Joel Beasley at 00:35:45) So let's say you work with the team on these research projects. Correct?

(Robert Suter (Bob Sutor) at 00:35:49) Yes. Yeah. That's right.

(Joel Beasley at 00:35:50) And you're obviously a leader in the field, and you're very knowledgeable when you speak. And so let's say you've got one of your team members, and they're stuck on a problem, a really hard one, and you're gonna talk with them about how to get over that hump, maybe how to ask better questions or how to just get through the current struggle that they're facing. What sort of advice, high level advice do you give them?

(Robert Suter (Bob Sutor) at 00:36:15) When we started, so we've moved very quickly in three years. So if you had looked at what IBM Q was in terms of people three years ago, we felt much more like a startup. Now, albeit, we were backed by IBM, which is a rather large company, and we've had decades of experience working in the field. And so what that meant was there wasn't especially a whole lot of hierarchy in the team at that point. But as we've gotten bigger and as, you know, our plans have expanded and we're not just doing the hardware and software, we're doing everything I described before, like education, what's online, the business programs, and things like this.

(Robert Suter (Bob Sutor) at 00:37:03) We've had to have a lot of different people join. And so the reason why I'm going into this is we have all sorts of people. Right? We are not just filled with quantum scientists. Right? We have people who very much are in tune with how to present this material using the latest tools. Right? We have James Wootton, who's a scientist on our team out of Zurich. He's like the foremost expert on quantum games. Right?

(Robert Suter (Bob Sutor) at 00:37:36) He has, there's an app for Android and iPhone called Hello Quantum. And it's people love it. It's quantum puzzles, and it teaches you, it's a beautiful little game. Right? The biggest complaint we get is that there should be more of it. Right? But for some people, that's how you do that. So, therefore, asking your question is a fairly general question because we have lots of different people. But what we come back to over and over again, really, is whatever you're doing, who's your target audience? What's your segment here?

(Robert Suter (Bob Sutor) at 00:38:11) Right? So are you going after hardcore developers? Right? Before you asked me about speaking to a board of directors, very different group.

(Joel Beasley at 00:38:20) Very different group.

(Robert Suter (Bob Sutor) at 00:38:22) What is your problem about, you know, quantum? We had a board of directors in last week for a particular company, and about a third of them were really interested in quantum. The other ones were like, yeah. Okay. You know, they had other things, and that's fine. But the other ones were really fascinated by the potential of this in their fields, right, and what they're trying to do. So know thy audience, or in this case, you know, you're working on a problem, but who is the problem for? Right? And how should we address this? Is it a technical problem?

(Robert Suter (Bob Sutor) at 00:38:58) Is it a social understanding problem? Is it an educational problem?

(Joel Beasley at 00:39:04) I found a trick with the board, by the way, recently.

(Robert Suter (Bob Sutor) at 00:39:07) Okay.

(Joel Beasley at 00:39:08) Here. I'll show it to you.

(Robert Suter (Bob Sutor) at 00:39:09) Okay.

(Joel Beasley at 00:39:09) Actually, so I do two or three meetings. They go, okay. Right? And then I thought, how am I, I need to communicate better to them. I need to show them the value of what we're actually doing. So I printed out our standard operating procedures, which goes into detail about how every section of our project works, and it's just like chopping down trees, essentially. And I brought the beast of a binder with me. And the meeting where I did that, woah, did they love it. Okay. For some reason, that just translated way better to them. So for my specific audience, not saying it's applicable to everyone's.

(Robert Suter (Bob Sutor) at 00:39:49) Yeah. It depends, of course, whether we're speaking to our board. Right. Which I will not discuss our conversations with them, but or other people's boards. Right? But, you know, to whomever we're speaking, we're very lucky that quantum is this topic that just sells itself. The phrase has been used in so much science fiction through the years. I mean, there were shows like Quantum Leap. There was even the movie last year, Ant-Man and the Wasp. In the middle, they did this whole riff because the scientists are calling it, you know, quantum this and quantum that.

(Robert Suter (Bob Sutor) at 00:40:29) And then Paul Rudd, who plays Ant-Man, said, do you people just put quantum in front of everything? And they looked at each other because, yes, that's exactly what they were doing. But, you know, people say, oh, you have a quantum computer? Can I see it? What does it do? I never thought we'd really have these. So it's a conversation starter all by itself, which is lovely. Because sometimes, as we know in tech, we may love something, and we really want to tell somebody, and they said, well, maybe not today. Yeah. Yeah.

(Robert Suter (Bob Sutor) at 00:41:03) So there's a lot of—

(Joel Beasley at 00:41:06) By the way, thank you for spending your professional career, like, a large chunk of it helping us get to this thing that we as society have wanted for so long.

(Robert Suter (Bob Sutor) at 00:41:14) Well, I mean, I'm just involved now. Before this, I ran the math department, but the quantum guys were having so much fun. I felt I had to get involved.

(Joel Beasley at 00:41:22) Yeah. You gotta go to that party.

(Robert Suter (Bob Sutor) at 00:41:24) But, you know, IBM Research, this stuff, we had IBM Fellows in the nineteen sixties who were the, you know, the fathers and, I guess, I'd like, I guess, great grandparents or whatever you want to call them, of quantum information theory. Right? There was this huge activity in the fifties and the sixties and the seventies. And because we had built computers, but there wasn't the theory behind them. And in the fifties and sixties in particular, that's when they developed information theory.

(Robert Suter (Bob Sutor) at 00:41:54) So this quantum stuff goes back decades before we could even build one.

(Joel Beasley at 00:41:59) So I've got a question about your professional development. You've been at the company for so many years.

(Robert Suter (Bob Sutor) at 00:42:05) Mhmm.

(Joel Beasley at 00:42:05) And you started out, and then you've grown with the organization for, you know, over two decades. What sort of advice do you have for this next generation entering the workforce about, you know, what made you successful in doing that?

(Robert Suter (Bob Sutor) at 00:42:21) So, to be exact, I've been with IBM for over thirty-six years. And—

(Joel Beasley at 00:42:27) It's like as long as you've been married then. Are you, would that be longer?

(Robert Suter (Bob Sutor) at 00:42:31) Not a coincidence. Yes. Yes. And in fact, I was a summer student even before then.

(Joel Beasley at 00:42:36) Are you referring to IBM as your wife?

(Robert Suter (Bob Sutor) at 00:42:39) No. No. No. She's Judith. She's Judith.

(Robert Suter (Bob Sutor) at 00:42:43) And so, you know, for many people, it's like, how could you possibly be with the same company? Well, you know, when you are in a company that at different times is, like, you know, between three hundred and fifty and four hundred thousand people, there are a lot of different jobs. Right? And it is also a global company. I mean, I travel, you know, I was in South Korea a couple of months ago. You go, I go, I just walk in, I show my badge, I sit down, I have access to the IBM network. You know, it's like the IBM embassy, if you will. But we do business everywhere, and so there is this sense when you're in a company like this, right, which is very different from being in a startup where I might jump from company to company to build the different types of experience that a career would normally have. So I have to acknowledge that first and foremost, so it's different. Now my advice would be is really understand what you yourself like to do.

(Robert Suter (Bob Sutor) at 00:43:48) Right? And so you can find yourself going toward jobs. You know, certainly the financial aspect is important. I'm not belittling that at all. But understand at your core who you are, what sort of things you like to do.

(Robert Suter (Bob Sutor) at 00:44:03) So I mentioned earlier, I like to build things. Right? You know, so either coding or, you know, where I'm sitting now, little projects, right, around the house or whatever. I need that part of me. I was least happy in my career where all I was building, it seemed, was like a calendar to have more meetings about meetings. That didn't do it for me. Right? And so identify what are these characteristics in yourself that will give you enough satisfaction in the work you do. Yes. I still do meetings. That's fine. I don't mind that. But what I find myself now having come around starting out in math and science, I was in research for fifteen years. I was on the business side for thirteen, and now I've been back for seven in research. And that's kind of a nice cycle.

(Robert Suter (Bob Sutor) at 00:44:57) Right? I was realizing toward the end of the business period, you know, minimally, I wanted to become a CTO or something. I needed much more tech in my life, right, than I was getting from those jobs I had. And here in IBM Research, you know, we're 3,200 people around the world in over a dozen labs. It's like a candy store of amazing technology, and it's why people love to come to work because, you know, we've had six Nobel Prize winners associated with us.

(Robert Suter (Bob Sutor) at 00:45:28) Woah. Ten national medals of technology. Right? Six national medals of science, Turing awards. So you got these brilliant people all around, and it's just fun. Yeah? So if you're a tech person, do tech.

(Joel Beasley at 00:45:45) How do I learn more? Let's say we've got some people listening, and they were just blown away by that, and they want to learn more about working with you or in that part of the organization. Where would they go?

(Robert Suter (Bob Sutor) at 00:45:55) So IBM Research itself has a website. And there you'll see the normal big picture view of what we've done and the history, and there are also profiles of researchers. Many of the people, about two thirds, number varies, have doctorates in IBM Research, but there are many people who do not. We have technicians. We have software engineers. We have quantum software engineers, a new title. And you can see where it fits. Right? It's interesting to consider what you might do as a software engineer in a, let's say, startup. Right?

(Robert Suter (Bob Sutor) at 00:46:39) So you're very focused versus a research software engineer. Different roles. Right? Different immediate goals and things like this. So there's a tremendous amount of room, I think, is what I'm saying here. So don't feel that because you're involved in, you know, I'm speaking to any of your listeners, that you're involved in one section of tech, you know, you have to stay there. Right? Or you just have to follow the money. You know?

(Robert Suter (Bob Sutor) at 00:47:03) Again, I'm not belittling the money. But what do you love? What part of it? Is it programming languages? Is it Kubernetes? Is it the cloud? You know, there's gotta be something of all the things you do that, you know, given the choice. You asked me before if I had to choose between two things. Given the choice, what would you work on? Get that job.

(Joel Beasley at 00:47:26) Quantum Kubernetes.

(Robert Suter (Bob Sutor) at 00:47:28) Quantum Kubernetes. Yeah. Well, we're on the cloud, you know, and there you go.

(Joel Beasley at 00:47:34) Well, thank you so much. This has been an absolutely fantastic conversation. I definitely learned a ton. I would absolutely check out what they're doing with the DNA advancements because I think you will find that it's almost, the reason why I'm so attracted to bring this up again to you is because as you were describing it, and I did a poor job of articulating it, but it seems like they were on opposite ends of the spectrum, like what you're experiencing with the quantum computing and the issues there, and then what the strengths are of the DNA storage. They seem like just complete opposites, which always kind of, you know, gets your spidey senses tingling.

(Robert Suter (Bob Sutor) at 00:48:12) Yeah. I think it's, we have these stickers that Jacob Betta, who's the head of our quantum program, it's his quote, which is you're thinking too classically. And this very much applies to quantum because time and time again, we see people using the intuition they get from classical computing, and quantum does not work that way very often. Same way, thinking what storage is. Like, everybody knows what storage is.

(Robert Suter (Bob Sutor) at 00:48:40) You know, no. So as you've described, the DNA, there are other techniques as well. Maybe that's the future of what computing will be. We'll be using all of these in different combinations, but the world will be really different in fifty or a hundred years.

(Robert Suter (Bob Sutor) at 00:48:56) So for all of you, particularly people earlier in their career, help make that weird, strange, wonderful new world.

(Joel Beasley at 00:49:03) Oh, I love it. We're making the weird, strange, wonderful new world. Sure. Thank you so much for coming on and hanging out and talking about all of this great, nerdy, geeky stuff that makes me so excited.

(Robert Suter (Bob Sutor) at 00:49:16) My pleasure. Great to talk to you.

(Joel Beasley at 00:49:17) Is there anything else that we didn't cover that you want to get out there into the world?

(Robert Suter (Bob Sutor) at 00:49:22) Just saying there are lots of places to learn about this. I have a playlist on YouTube of over 50 different videos that approach quantum from lots of different directions. Check those out, and the IBM Q experience is always evolving. We're about to release new tutorials, new videos, and things like that. So keep your eye on that to learn more about quantum.

(Joel Beasley at 00:49:46) Excellent. What would I search on YouTube to find that so we can put in the show notes?

(Robert Suter (Bob Sutor) at 00:49:50) If you just go to me, to Bob Sutor.

(Joel Beasley at 00:49:54) Perfect. And then we'll put it in the show notes because people will definitely be interested in checking that out.

(Robert Suter (Bob Sutor) at 00:49:59) Very good. Alright.

(Joel Beasley at 00:50:00) Thank you so much. Have a great day.

(Robert Suter (Bob Sutor) at 00:50:01) Thank you. You too.

(Joel Beasley at 00:50:02) Bye bye.