El contenido de los recursos se muestra sólo en inglés.
Raymond Hawkins: Well, thank you again for joining us. Welcome to another edition of Not Your Father’s Data Center. I’m Raymond Hawkins, your host. Today we are joined by IBM Chief Quantum Computing Exponent, Dr. Bob Sutor. Dr. Bob, thank you for joining us again.
Robert Sutor: I’m happy to be here.
Raymond Hawkins: We’re so grateful to have you back. For those of you who don’t know, we track all the statistics on our podcast and Dr. Bob has been our most listened to guest in this year, year and a half that we’ve been recording the podcast. And so we’re super grateful to have him on our first ever video version of the podcast. So this will be fun, he and I will be learning live on screen together with you guys, but excited to have him back.
Raymond Hawkins: For those of you who don’t remember, Dr. Bob’s primary function is to promote, talk and understand how quantum computing is changing or going to change our world and leading that thought process and that thinking and talking about it inside IBM. To say that you are getting the opportunity to listen to the global expert on quantum computing, I don’t think is too strong. Dr. Bob joined us for the audio version only, and really gave us some great insight into how that quantum computing isn’t going to replace traditional computing, but it’s really going to partner up with it and be an extension of it. And that it gives us computing capabilities that are even hard for us to comprehend.
Raymond Hawkins: Dr. Bob, before we jump into what’s changed and where things are, what’s accelerating, can you give us your caffeine molecule analogy, I just think it’s a great way to understand the exponential difference between traditional computing and what we see in quantum computing, and then we’ll go from there. If you don’t mind that, that analogy, I think is super helpful.
Robert Sutor: Yeah. In fact, a lot of people wonder about quantum computing versus just classical computing and to remind your audience, so classical computing is really what you do all day long. So it’s the chip, the memory, the storage that might be in your phone, in your laptop, the servers in the data centers, and so forth. That’s classical computing. Those technology, the ideas really go back to the mid 1940s. And so I don’t want to date it, it is in some sense like going to classical, like Beethoven or Mozart or things like that. But in the sense, its traditional type of computing.
Robert Sutor: And basically, there are wonderful theoretical results saying that you can compute anything you want with classical computing. What they don’t tell you is it might take 10 million years, though, to compute it. Or it might require so much memory and so much storage, it’s completely impractical. So people are always looking for new types of computing, new techniques, things that are really fundamentally different from classical computing to say, well, all right, can this new method solve some of the problems that classical computing, high performance computing can’t solve? So inspiration comes from nature, is one way of thinking about it. Nature, the universe, the galaxy, everything like this is one massive computer.
Robert Sutor: I mean, the data here is representing every atom, every molecule, every proton, every electron, right? Everything that composes you, and everything around you, is the data and nature is a set of applications, programs, that makes it all work, that carries out processes, that produces results. So a good question to ask is to say, can we use nature the way nature works as a computer, and apply that to some of our hard problems? So the way nature works when we talk about the very small, so at the atomic level, things like this is what’s called quantum mechanics. It’s a very deep, very mysterious, very head scratching strange part of science, yet it seems to be the way that describes nature and the universe and things like this. So the caffeine example that you mentioned is this.
Robert Sutor: When we talk about data centers, and we talk about capacity, how many cycles you can run, how much storage you have, how much RAM and then we talk about supercomputers and things like this. You want to say, well, can these solve really the important problems? So I’m going to give you one very simple problem. And that is, it’s a chemistry problem, right? So we have these wonderful computers. I’m going to give you one molecule, I’m going to give you one molecule of caffeine. Now, I choose caffeine because of all the molecules with all the long names. Caffeine, people know what it does. It’s something specific, you know where you get it from coffee, tea-
Raymond Hawkins: Widely used molecule.
Robert Sutor: Widely used and pretty much globally, so it saves me a lot of trouble when I talk around the world. And caffeine, of course, is not just a molecule. It enters your system and it goes to your brain and it makes you alert, keeps you awake and things like this. So if all these classical computers are so good, why can’t we just take this molecule, right? And so instead of studying the biochemistry, the way it works in your brain or think of a test tube or a laboratory, why can’t we simulate exactly the way that molecule works in one of these great big computers or data centers or things like that, it seems like a modest proposal.
Robert Sutor: Well, here’s the problem. If I were to write down all the information I would need to work with, with a caffeine molecule. So we think of again, just getting slightly technical, the electrons or the positions, and the carbons and the nitrogens, and the oxygens that compose it, and how they fit together and what they’re doing and things like this. The number of bits, so the number of zeros and ones will be on the order of 10 to the 48th. So that’s a one with 48 zeros. All right?
Robert Sutor: So a byte is eight bits, right? And then you go from there, megabyte is a million bytes and so forth, like that. So it’s eight million bits, we get bigger and bigger and bigger. But 10 to the 48th is 1 with 48 zeros, and people estimate that, scientists estimate that’s between one and 10% of all the atoms in earth. So that is, you would have to take in the worst case 10% of all the atoms in the earth, and say, okay, for working purposes, I’m going to assign you a zero and I’m going to assign you a one. We don’t have storage like that. Nobody has data centers with 10% of the earth [inaudible 00:06:42].
Raymond Hawkins: There not data centers that big.
Robert Sutor: Not data centers that big. And moreover, if I give you 10 caffeine molecules, you’d use the whole earth, so.
Raymond Hawkins: We’re done after that. And that’s to map, one caffeine molecule.
Robert Sutor: One caffeine molecule, like one instant. Classical. Now, quantum computers, because they’re based on quantum mechanics and the way nature actually works, you could represent that same information in 160 quantum bits, or qubits, we call them. Now, they have to be very good qubits, and I’m fudging a little bit here by not defining exactly what very good means. But we are on the road now, we have a 65 qubit machine now. Later this year, we’ll have over 100. In two years, we’ll have, IBM Quantum will have over 1000 qubits.
Robert Sutor: Now, those by themselves will not quite be good enough, but we are on track to scale and scale and scale. And we certainly do see over the next few years, the next decade or so getting up certainly to 160 and beyond. So caffeine classically impossible forever, within our sights. And look, do we care that much about caffeine? No, but we do care about antibiotics. We do care about antivirals, we do care, more mundane new materials, things like this.
Raymond Hawkins: As I think about a practical application, I love the caffeine molecule, especially in your situation, Dr. Bob, because it’s applicable on whatever content you’re speaking on. But I think about the really complex problems of what do we understand about why a problem happens? What do we understand about why something interaction, and what I think I hear you saying is, problems that there’s just not enough brute force horsepower in traditional classic computing, we can solve that with quantum computing, because just the raw horsepower will be at such an exponentially different level. I think problems that today we look and go, can’t solve that one, we won’t be faced with that challenge in the future. Is that a simpler way to think about it?
Robert Sutor: That’s one of the classes of problems, that’s right. So the caffeine is kind of a, in quantum computing is … Let’s do apples to apples computing. Quantum computing for a quantum mechanical problem. But yes, quantum computing does have this exponential aspect to it. And frankly, the word exponential is used too much in terms of marketing people. People use it as oh, it’s just growing really fast. Or it must be really hard, it’s an exponential problem. Well, in math, an exponent is something. So two to the 10th, 10 is the exponent, right? And that’s a much smaller number than two to 1000, or things like that. But every time you add a qubit, you double the amount of sensitive working space you have.
Robert Sutor: So, one qubit has two pieces of information, 10 qubits has 1024 pieces of information. It just grows and grows and grows. And going back to caffeine, by the time we get to 275 qubits, when the computer is running, it can represent more information than there are atoms in the observable universe. That sounds impossible, but that’s why quantum computing is so strange.
Raymond Hawkins: Right. You started to allude to where we are and I liked your phrase, 160 really good qubits when we get there. Could you take a minute before we get down on what’s changing, could you take a minute and talk, because I come from the traditional and classic computing world where Moore’s law is a thing. And we’ve observed it for almost four decades, maybe five now. And I think my industry largely understands or my space, the data center space, largely understands how Moore has impacted the traditional class compute world.
Raymond Hawkins: Could you tell me what it looks like, and I know Moore’s law doesn’t apply in qubits, but can you tell me what you’re seeing in the early stages how far we’ve come? I think you mentioned 65 qubits today. Is there a rule of thumb? And what does it feel like? And I’m not asking for any proprietary IBM information, but just what do you guys thinking as far as how improvement is going to go?
Robert Sutor: So once we established this idea of qubits, right? So a qubit is the fundamental piece of information. And we represent that somehow in a quantum computer, a physical representation of a qubit. So we want two things, we want a lot of them and we want them to be extremely good. So we want quantity and quality. In fact, a lot of the games that people were playing four years ago, saying, look at me, I got all these qubits and they were the worst qubits you can imagine. It’s like, totally useless, but they had a lot of really useless things, right?
Robert Sutor: So it’s really these two dimensions of being able to increase the sizes of our machines, while having them able to perform calculations, more and more accurately. Right? So too many bad qubits, who cares? Really great, but fewer qubits and you need to do anything useful, who cares? So you’ve got to increase them both in tandem. And so anyone who’s following quantum computing, you really have to look at both of these dimensions as I would call them. So your question, I would translate as saying, how are things going in terms of quantity and quality?
Raymond Hawkins: Okay, that’s a better way to say it. Quantity and quality, how are we looking?
Robert Sutor: Quantum and quality, right. We want a lot of really good things, which why not? So we published toward the end of last year, what we call The Hardware Roadmap. And we showed how we were going to go from what was then the maximum of 65 qubits to 121 qubits by the end of this year, and we’re on track for that. Next year we’ll go over 400 qubits. And then in 2023, over 1100 qubits. And that’s a milestone because 1000 people like these round numbers, but the technology to get us to 1000 is the same technology that will allow us to get to many 1000s, that is, we will have solved a lot of the technical problems, just to go from where we are now to over 1002 years. That means that we can continue to scale.
Robert Sutor: Yes, we’ll keep coming up with innovations. It’s not just a question of engineering, they’re still hard problems. But it says that, at least in that direction over the next few years we have figured out how to get through the fundamental roadblocks. And that’s true of any technology, and that is not true of all the different technologies that people are doing. So that’s the good news that we will continue to increase to do that. The next thing, as I said, is quality. So quality has to do and it’s a strange concept, because people, they think of oh, I run an app, and the app just does what they tell it to do. And that’s it.
Robert Sutor: Well, if you’re a hardware guy, which I admit I’m not, but if you go way back, right? You go way down on the innards of things, bad things happen in hardware. I talked about those zeros and ones, well, occasionally a zero becomes a one and a one becomes a zero. Now, our hardware is very sophisticated, it does error corrections, it’s to say that wasn’t supposed to happen, I’m going to change that back. So we are in this brand new type of technology, quantum computing, figuring out first, how to decrease the errors down. And then the second half of this decade, actually implement error correction and making things foul tolerant, which means that the thing that you care about running, so your quantum application, whatever it may happen to be, will go from beginning to end without errors from the quantum computer.
Raymond Hawkins: So pause there for just one second, Dr. Bob. I think that you and I are both sufficiently seasoned to remember that when computers in the early days, crashing was a normal thing, having memory faults was a normal thing, and that all kinds of applications stuttered, stumbled, crashed on us and that was normal. That was, you accepted that as part of computing that hey, something went wrong in there at the hardware layer or at the hypervisor layer, at the operating system layer, at the application layer, and you just reboot it. And you accepted that as-
Robert Sutor: That’s right.
Raymond Hawkins: … for all of this additional capability, you are going to have some glitches, and that was just part of it. And I think in the last couple of decades, the robustness, I think about my phone, right? There’s more compute function in here than what we put a man on the moon in, right? It’s such a well orchestrated device, and we’ve hidden those errors so far below and allowed the computer then the device to handle them, that most of the younger generation doesn’t understand, there’s things that break in there. And not break because someone did something wrong, it’s just that this is very sensitive activity going on at a very rudimentary level and one little mistake messes it up.
Raymond Hawkins: And what I think I hear you saying is that we’re a little bit back to that stage in quantum, that we’re at the very beginning of figuring out well, when something goes wrong, what do we do? How do we handle it? How do we make sure that it doesn’t impact whatever the thing, three layers up my application that I want to work doesn’t impact? Is that a good way of understanding it? Because I think, for a lot of our listeners, they don’t remember the days when you just accepted your computer crash regularly. It was okay, because you were getting all of this unique functionality.
Robert Sutor: Well, I think you hit the nail on the head when you said, oh, just reboot. And if the problem goes away, don’t worry about it. Right? Well, why did you have to reboot? Right? What was the fundamental problem? So while there are differences, obviously, between classical and quantum computing, when you introduce computing technology, as you pointed out, there are certain standard problems you have to tackle, and so it proceeds a pace. You tackle this class of problem, or this standard type of thing.
Robert Sutor: Let me throw you something out. We’re eventually going to need something called quantum RAM, quantum memory. We don’t know exactly how to do that yet, that will be a way. So that’s down the road. What is quantum RAM? Why don’t we just use regular RAM? Well, it’s weird. It’s weird stuff. Turns out in the quantum world, you can’t copy data.
Raymond Hawkins: And think about RAM, when we first started computing, we didn’t have RAM either. I mean, that was something that we recognized we needed, that … Wow! Yeah, just to think of how many of the lessons are translating and then the challenge are going to be faced, again, as you guys solve for how quantum computing is going to help us. Fascinating, fascinating stuff.
Robert Sutor: And for younger people, where you might say, oh, look at these guys, they’re reminiscing about the 80s, 80s weren’t that long ago.
Raymond Hawkins: That’s right.
Robert Sutor: And things move faster, and faster, and that’s what represented with Moore’s Law, right? The idea that every couple of years things would get, roughly speaking, twice as good. And we do have something like that, it’s called quantum volume, it is a metric related to this quality. We said two years ago we’d be able to double it every couple of years, we doubled it twice last year, which kind of leads me into this other statement, which is not only are we making progress with quantum computing, we’re going faster than we thought we would.
Raymond Hawkins: Well, that leads us nicely into this acceleration conversation, the lessons you’re learning, what’s coming, how it’s getting faster, what’s changing, we’d love to hear from you on the things that you’re comfortable talking at this stage about what you’re seeing as quantum computing accelerates its development.
Robert Sutor: Right. So a few things. So let me just give you some sort of big statistics as we keep updating these. So talking about data centers, we now have 24 quantum computers on the cloud. They are in IBM data centers. We did announce, though, at the Cleveland that we will be putting a quantum computer in the Cleveland Clinic next year, they’re starting a brand new research institute there, and we will be installing the latest and greatest quantum computer there include one next year.
Robert Sutor: So the significance of that is, these aren’t just living inside IBM, and won’t just live inside of IBM. We have a machine in Germany that is now online, we will have one in Tokyo in a few months. Cleveland is the first announced on premise quantum computer. So everything has been cloud based. Because really the future is classical and quantum together, right? I mean, what IBM calls hybrid cloud, right? Computing is fundamentally hybrid, the best components to solve whatever problem you have to do. So that is news in a way that represents our confidence that we can support further develop to quantum computers, when they’re not just living down the hall. Right?
Robert Sutor: And that took a while, to get that level of confidence, right? And so as things evolve in this industry, it’s very much, there’s this phrase I just use, levels of confidence. So you have a data center, do you want to install something there that is going to crash and burn all the time, because the vendors are … No, so you have to have a level of confidence in them, but they have to have a level of confidence in their machine, because it doesn’t help them as well. So during the evolution of the technology, some of it is just continuing a pace, and then there’re jumps.
Robert Sutor: So you’ve been working on a problem, you’ve been trying to figure out how to do this. It’s static, static, static, and then you try something, you say, wow, this gets much faster. We showed, for example, a couple of weeks ago, we mentioned that experimentally, again, going back to these qubits, whatever they are, I mean, we could really go deeply on them. But these physical things, a qubit doesn’t last forever. You can compute with it for a little while, but then it becomes kind of chaotic. We showed experimentally that we could produce a qubit that last nine times longer than our previous generation, nine times, it’s almost order of magnitude.
Robert Sutor: What do quantum computations do? How do they work? Well, a lot of computations these days involve many calls to a quantum computer. So you’re sitting there at your laptop, or in a container in a data center, something like this, you’re calling across the cloud, you’re mixing, okay, now I need to do a quantum computation, I call across the cloud. We have this chemistry example, and I know chemistry is hardcore, but you got to go back to my caffeine example. This is why people are working on this. If we can tackle it for these types of problems, it will work for more mundane and perhaps geeky things than this.
Robert Sutor: In 2017 we estimated that it would take 45 days to do a particular calculation on the cloud. So if you’re sitting on the laptop, going back and forth, 45 days of full time use on the cloud. So you don’t mind putting your laptop on the side for 45 days to compute something. We’ve reduced that to nine hours. So we’ve shown a 120 times increase in this chemistry example, and we expect this to be useful for other things as well. So this is a huge jump rather. So 120, it’s about 100, that’s two orders of magnitude. So we’re not saying hey, this is 1.1 times faster. Yeah, here’s a little improvement. We’re saying this is a breakthrough.
Robert Sutor: When you can do something two orders of magnitude faster, when you can make qubits last 10 times longer, nine or 10 times longer, this means that you’re getting these jumps, which means the innovation, the research that you’ve been doing pays off. And this gets you that much closer to putting quantum computers in productive production use as well.
Raymond Hawkins: So growing them … Let me rephrase, not growing, extending their livelihood, extending their life span, that might be a better way to say it. Their shelf life, that’s a big one. And then their ability to, and I’m going to not do this justice, but I know that there’s an issue about how many qubits stay close to each other, and how they impact each others state and your ability to keep them in close confines and have them work in concert. Could you talk to us a little bit about that, as we think about, I know you said 65 to 122. Is that part of the challenge of how do we get them close to each other and still being able to have reliable, good information that comes out of them?
Robert Sutor: So let me give you a way of visualizing this, because for most people, this idea of qubits and quantum devices is pretty abstract. So think of a qubit as a little computational unit, whatever happens. And we need to lay them out somehow. So imagine you’re putting them on your desk or on your table, and this is going to reflect the way they actually sit in the device. So we’re going to start with a hexagon. And the hexagon has six sides. And we’re going to put a qubit on each of the vertices, the points of the hexagon, so that’s six. And then we’re going to put one in the middle. So along the lines that connect the vertices, so for a total of 12.
Robert Sutor: Now, when I look at this, I observe certain things. Some qubits are next to each other. And that’s really good for certain computations, because cubits that are right next to each other, can talk directly to each other. And that’s required for quantum computation. Ones that are further away, you got to jump through some hoops with some software and things like this, you can make it work a little bit. So let’s say I have qubits one, two and three. I got one and two, and they’re talking to each other, but I’m also sending some information down to qubit three, saying, qubit three, you need to be doing this. I want you to be computing this thing over here.
Robert Sutor: Turns out if you’re not careful, when you send that information down to qubit three, because we’re dealing in a situation of very, very low energy, incredibly low amounts of energy, that’s a little bit too much energy. Or if there are defects, it acts as a little antenna that’s reading out noise static. So qubit three may be busily doing what it’s supposed to be doing, but it’s spreading the static in its immediate area, and that is screwing up. What’s happening on qubit two right next to it, is it’s trying to do its work, and it’s getting … Imagine you’re trying to listen to something, and you’re getting this static.
Robert Sutor: And so if you and I are talking and I’m kind of missing the words, Raymond, because there’s this noise in the background and I completely misunderstand what you told me to do. Well, so this is kind of a normal type of thing and this is called noise mitigation. They’re called spectator errors, and it’s yet a way that quantum computing is different. So you learn how to control spectator errors, you do things in a different way. First, you control it. And then you say, can we do this in an even better, smarter way so it doesn’t happen as much in the first place? So this is what the evolution looks like.
Raymond Hawkins: Quality and quantity, it’s interesting to understand that they both are impacting where we can go. I’m still moved by your 10 qubits. You said, I think 1024 computations with 10 qubits, did I get that right?
Robert Sutor: Well, the number of pieces of information. So yeah, because it doubles. So when you go from one qubit, you have two pieces of information, two qubit, you have four. And then the magic happens, three, you get 8, 16, 32.
Raymond Hawkins: So all of our friends in the financial industry, the magic of compounding, right? I think that’s why you alluded earlier to Moore’s Law, as we’ve been able to double. When we doubled in the early days, it wasn’t a big deal, when you double in the later years, it’s a big deal. And that’s what I think we’re already starting to see early in the life of quantum computing, is we double the number of qubits, just the … I’m going to have to think of another word beside exponent, because I like your analogy that yes, two to the 10th times the exponent, there’s some other way. But yes, the incredible growth that you see as things doubling out years.
Robert Sutor: And a lot of times when people … So your compound interest is exactly right, I wish more people would realize that compound interest is exponential. As is half life decay [inaudible 00:27:52].
Raymond Hawkins: Right.
Robert Sutor: Right? Things like this. When applied in a negative way, you say oh, that problem is exponential. That means that to solve a problem of a certain size, it gets really huge as the size increases. Starts modestly. It’s the old hockey stick type of thing that people say. You reach that point, and suddenly the problem becomes intractable. It just gets harder and harder and harder. So quantum has good exponential behavior. Some problems have bad exponential behavior, including some problems in AI, machine learning. Can you use the good exponential of quantum to control the bad exponential of certain types of problems? And so that offers us some insight.
Raymond Hawkins: Dr. Bob, can I ask you to speculate for a little bit. When I think of the ability to unlock complex problems, and to be able to address them, I liked your 45 days down to nine hours analogy, right? There’s questions that we won’t even bother to tackle when there’s a 45 day problem, or they’re only going to be tackled in a university or an experimental environment, and it’s not practical for the rest of us to use. But when I think of, and I’m going to do some bad analogies here, but when I think of when we first map the human genome, and it took a couple of years, and I think a lot of people said, well, I don’t understand why that’s a big deal. And now you can 23andMe your DNA, and you can get it mapped and sent back to you in a few days. Right?
Raymond Hawkins: And I think of that one as a, not a doctor and not a terribly well educated person, and I can easily see why that’s important. Because the more times we’ve sequenced DNA is the more times we can go, okay, this group of sequenced DNA, these people all had lung cancer, and is there something that we can look in their DNA and see, that’s the same across everybody that has lung cancer? It could give us a fundamental way of understanding problems that because we didn’t have the data and we didn’t have a way to analyze it quick enough, that would ever reveal itself to us.
Raymond Hawkins: So I know that’s a crude analogy and sort of a simplistic person’s understanding, but I think of understanding disease is made possible because of our ability to map the human genome much more quickly and understand it. Could you take, speculatively, because you live in this world, what kinds of complex problems that today we just look and go, that’s too high amount and could this be applied to quantum computing?
Robert Sutor: So your problem as you pose is very interesting, because as stated, it’s an AI problem. It’s a machine learning problem. So you have a whole lot of data, you want to extract patterns, you want to learn things from those patterns. So if someone else comes along, maybe they have a certain propensity to get a certain type of cancer because of genetics, or lifestyle, or pre-existing conditions and things like this. So you can’t cheat and code directly to quantum for that, but you can say, well, can we have quantum do AI or machine learning better? And down deep, all of machine learning, all of AI is just math. It’s a collection of mathematical techniques, actual calculation.
Robert Sutor: So you can say, can quantum help us do the calculations faster? So that would therefore deliver the AI results faster and get you what you would want there. The second way of saying is, well, as I’ve been talking about quantum computing really is just this very different thing, it does not have counterparts in classical. Can I use this radically different type of computing to find patterns in ways I can’t classically? So in that same data, can I examine it and say, here’s a connection we never would have seen before.
Robert Sutor: And there are even other ways, there are ways of saying, well, keep doing what you’re doing with AI, keep doing the calculations, but there’s this one little tricky bit in the middle, which performs an essential function. Turns out we’re learning that quantum can compute much better little, if you will, tricky bits, much more efficient, which you then plug into the classical computing. And so quantum computing, we anticipate, can improve AI in a number of different ways, enhance it in a number of different ways. And I put that out because AI and machine learning is now becoming widely, widely used in many areas that people never saw. Right? And so that fundamentally is because quantum is just a very good computational machine.
Robert Sutor: There are other areas simulating for risk, risk assessments. So let’s say you’re buying and selling stocks, let’s say you’re building a factory, let’s say you’re building a new data center. Right? So clearly you don’t just sit around saying, hey, I’d like to build a new data center over there. Great. You do a risk analysis, right? You try to figure out how much traffic is it going to get? Is it well located with respect to where it has to go? What about the power considerations? What about the environmental considerations? And as much as you’d want to do a perfect risk assessment, it’s limited.
Raymond Hawkins: By the way, three for three doc, you could do my job. Those are the top three’s. Network power and environmental risks. Yeah.
Robert Sutor: Well, what if I gave you the opportunity to do even more fine grained risk analysis? Because the way this is typically done, you’d say, well yeah, there’s this likelihood of this happening and this likelihood of this. Or there’s a likelihood in this range over here and this. Moreover, these things are related in different ways. So we believe that quantum will be able to allow you to simulate these much, much more efficiently than you could before. So you may be able to do far more, what if analysis, what if analysis on risk assessments in seconds or minutes, what would have taken an hour.
Robert Sutor: We did some interesting work with ExxonMobil that was announced a month or two ago on paper. And obviously they’re an energy company, but they care about ships at sea, because they have oil tankers. At any time they’re roughly 50,000 ships at sea. Well, they’re at the mercy of weather. And the strange thing is, the week we announced this with them was the week that ship got stuck in the Suez Canal. So would you like to do an analysis of how to redirect all those ships that were supposed to go through there? To go different routes, right? I mean, some of them started going south, some of them, do I wait, do I go? What are the odds? How long will it take and things like this?
Robert Sutor: Would you like that calculation to take five days or would you rather have a good idea in 10 minutes? Things like this. So it’s these sorts of calculations. Now, no promises, I really want to be very careful. We’re learning a lot about this, and sometimes we learn really good things, as I pointed out, and sometimes we say, that sounded good, but it didn’t really work out so well. But there’s such active research in industry, in corporations around the world, in academic institutions that it’s really heated up and that’s why it’s important when I tell you we could do something 120 times faster. I mean, it’s all those people out there who don’t work for us, can use this systems, and they can accelerate their work.
Raymond Hawkins: think about, you said that we looked and decided it didn’t work that well. We only solve the problems as best we can solve them today. And I’m going to go back to my classical computing roots, because it’s the best analogy for me. We thought that the best way to record information was on disks, external disks, just that we slid in a slot in the beginning of computing. And then we decided, we could take that platter and we could put it inside the computer. Right? And that the platters didn’t have to be changed out, and the floppy disk disappeared, and we had hard drives.
Raymond Hawkins: And then we decided that same concept, although those were both the same concept, but one was internal, one was external, then we decided, hey, that’s not the most efficient use of storage, we could do it in flash, in the same types of devices that we did RAM on. And we did it with the best economically viable, technologically capable solution at the time. And I think that’s an easy one for someone with my age to remember. I remember when computers had two floppy drives, and no memory and that’s how you’d talk to the computer. And then we went to hard drives, and now we’re at flash memory.
Raymond Hawkins: And I think those same kinds of things are what I’m hearing you say, is today we’re good at quantum for what we understand. And we’re going to try to solve the problems, but we’re going to solve the problems with the best capabilities we have today. We can’t even see what capabilities we’re going to have in the future, what new problems and how we’re going to solve them around quantum computing, but just like the silly example of floppy drives, to hard drives, to flash storage, we’re going to learn over time, we’re going to get better at it and more efficient at it.
Robert Sutor: Well, going back to, so there’s evolution. And certain aspects are somewhat predictable, miniaturization. Miniaturization, always happens. So whatever we do, whatever you look at and say, oh, that’s nice, it’s big. Just wait, it’ll get smaller. Now, as a purchaser of these different technologies, you may not have been aware of that back in the labs of the people. And, we know the vendors historically, who have proved the hard drives, produced them, they didn’t always come out right the first time. They said, well, let’s make it this way with such and such oxide, and it didn’t work that well.
Robert Sutor: So it’s the scientific method, you start with hypothesis. And then you gather data, and then you achieve. So you had your choice of the best technology available at each point of the evolution, but behind the scenes, there are a lot of starts and non-starts. Pharmaceuticals. I mean, how many drugs do they set off to develop that actually end up helping people in different ways? I mean, there are lots of variables there, so there’s a lot of work going on with AI for example, to increase the likelihood that they will get on the right path quicker. So that they will produce medicines faster.
Robert Sutor: And it also goes back to quantum, how can quantum do? If we can going way back to caffeine, right? If we can do more of that biochemistry in the computer, instead of inside you, Raymond, you’ll probably be happier, but it’ll also happen a lot quicker.
Raymond Hawkins: Right. Right. Right. Well, awesome stuff. Well, Dr. Bob, we always try to sneak in some trivia questions, we usually make those an ode to our guests. So we’re going to sneak in three trivia questions for our listeners, there’s an opportunity to win some money from Compass and to get to hear from our listeners. So I’ve got three in honor of, I went to the Harvard of Lee County, for those of you who don’t know, that’s the Auburn University, not quite as prestigious as Dr. Bob, who actually went to the Harvard. But in honor of Dr. Bob’s education, we’ve got a couple of questions for you.
Raymond Hawkins: Number one, and I might have to look down and read these. Number one, what year was Harvard founded? Now you can email these answers. Dr. Bob doesn’t get to answer. Who is generally considered the father of quantum computing? Question number two, and what is the quantum computing equivalent of a bit? That one was answered by Dr. Bob in the show. So those are our three trivia questions. You can email me, rhawkins@compassdatacenters.com, you can tweet us your answers @CompassDCs or you can send them to, answers@compassdatacenters.com.
Raymond Hawkins: Also, I think there is a text one, so I better read that one to get it right. You can text your answers to 844-511-1545, code word, Dr. Bob. One more time, 844-511-1545, the code word, Dr. Bob. So IBM’s Chief Quantum Computing Exponent, we love talking to you. We’re always impressed with your understanding and your knowledge, and how you make it understandable for simple guys like me, really appreciate that. Dr. Bob, we’re excited to see where it goes. I think the future is exciting.
Raymond Hawkins: I think that the world is coming out of a pandemic, as we record here at the first week of June. There’s some unrest in the world as there always is, but I think the future is bright. That the changes that are coming in robotics and in AI, and machine learning, and in quantum computing, and in biomechanics, and chemistry, and I think this is an exciting time to be alive, and there’s a great future ahead of us. And hybrid computing that embraces the power of quantum computing, I think is going to be a part of it. And we’re grateful to have you talk to us about it and help us learn a little bit about it. Thank you for joining us. Thank you so much.
Robert Sutor: Glad to. And I hope you all understand now my title of Chief Quantum Exponent, it’s a bit of a play on words here because exponent can mean more than one thing. Raymond, I want to do a shameless plug just before we go here.
Raymond Hawkins: Please do, absolutely.
Robert Sutor: So last time I was here, I talked about my book about quantum computing, Dancing with Qubits. The idea is, is that look, if you’re going to do quantum computing, you’re going to have to learn some math. Sorry, but I’m going to take you from beginning to end and we’re going to go everything you need. Just came out yesterday in hardcover. It’s available on Amazon, the US and soon other geographies as well.
Robert Sutor: I have another book, which is at the moment called Dancing with Python. So it is a book about programming. It’s an introduction to coding using the Python programming language, but what’s different about it is I teach you not only all the classical stuff, but I also start teaching about quantum coding. So in one place, you can learn to code and also start learning in quantum. Because computing is computing, right? And so with luck, that’ll be out by the end of August.
Raymond Hawkins: So go check out Amazon, checkout Dancing with Qubits. I have the soft copy version, I will be buying the hardback version, and recommend it and looking forward to Dancing with Python coming later this year.
Robert Sutor: My daughter keeps asking me what’s all this dancing going on?
Raymond Hawkins: Good stuff. Well, Dr. Bob, it’s been awesome having you. We’re so grateful. Thank you for doing my first ever video podcast with me. We are excited to learn about this new way to deliver content to our audience, and we’re grateful that you’re with us for the very first one. Thank you, sir.
Robert Sutor: Thrilled to be here. Great to see you again, Raymond. Thank you.
Raymond Hawkins: Bye now.