Unlocking Our Quantum Future

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Hello, this is Malcolm Glauble, a new listing to smart talks with IBM. Every year Techweek brings thousands of people together to network and learn about what's emerging across the technology ecosystem. And at this year's conference in San Francisco, I had an amazing opportunity to sit down in front of a live audience with J.G. and Bada.

J. has been with IBM for years, and was recently promoted to Director of Research. In this job, J. has an important mission, helping the company build the future of computing. In the last episode of Smart Talks, I began to learn about quantum computing from IBM Chairman and CEO, Arvind Krishna. But this conversation I had with J, went even deeper. And convinced me that the development of quantum isn't just a fun, exciting new paradigm of computing.

It may be one of the most important scientific achievements of my lifetime.

J. Good morning. Morning. Welcome to Smart Talks with IBM. Thank you, special live recording here for Techweek and congratulations. How long have you been had a research at IBM? It's since October 1, October 10th today, so nine days, nine days. Do you just talk a little about the position? This is one of the most important positions in research in the world. IBM research has been around for 80 years, and it's done some tremendous

“technology. A lot of inventions in fundamentals for semiconductors, algorithms, AI, yeah, I think if we”

look back to where a lot of the innovation in the technology of the world comes from, I think you can find IBM's footprint on it, and you can find IBM research. So yeah, I'm very excited for the opportunity, but I'm also aware that there's big shoes to feel, and I'm looking forward to how we how we take IBM research forward. Obviously, I'm going to be bringing a lot of the quantum side, which we're going to talk about later. Beyond quantum, there's important work that needs to happen in the AI,

hybrid cloud, and I think we're going to also enter into this new period of mathematics, where we get to use quantum machines, and also AI machines, and there's some really good hard mathematical questions to answer. How many people do you have working for you? I mean, researchers in the 3000 researchers across many different labs around the world, our main lab is in Yorktown, but then we have the lab actually out on the west coast in Armiton,

or SVL now, and then we have one in Zurich, Japan, and a few others around the world. Tell me a little bit before we get into quantum. I'm just curious about your path, so you're Australian. Yep. You would talk about earlier backstage. Your accent has become muted.

“You should crank it up, because it's, yeah, I'm slowly losing my Australian accent. I've been”

in the US since 2004, so I asked that, you know, to sound very Australian. Yeah, but how do you practice it? Maybe I've got to go back to Australia here, more Australians, say, "Gaday, how's it going, things like that?" And you didn't grow up thinking you were going to be assigned to this one day. No, I grew up in a pretty normal life. My dreams as a kid was building things, so I was either going to be a carpenter or a mechanic, but I had some great teachers that inspired

me to go to university, and I didn't even know honestly what a scientist was, and then I found myself at university doing science, particular physics, and I ended up loving it. So you go from there to, "Where do you do your PhD?" So I did my undergrad in Australia. I did it actually in laser science. So I, I think I watched a, some TV show in lasers seemed interesting, so I wanted to learn about lasers. And then I realized in trying to understand lasers, there was this quantum mechanics,

and so I was like, "All right, I want to actually understand this quantum mechanics." So I did my equivalent of what you in the US school masters, we call it honours in Australia, but we do a research project. I said, "I wanted to shoot lasers into atoms and measure cross sections, and I got really into quantum physics." So then I decided, "All right, I don't understand this quantum physics.

what is this quantum mechanics? Why is everyone arguing on these different interpretations?"

Then I finished my PhD in Australia doing that. Then I moved over, at the end of my PhD interpretations. It's more, I'm people arguing about the equations. Whilst I think it's really important, I decided if it's going to be like a collapse equation, there's many worlds, or a hidden variable model, or that just quantum mechanics deco here is because we don't see

“superstitions in the everyday world because it interacts with some environment. The only way to answer”

that question was to build a quantum computer. And so then I decided at the end of my PhD, I wanted to work out how to build a quantum computer. And then I left there and I went to Yale, and then at Yale, that's where I got into superconducting cubits, which just a few days ago, one of the professors there, just won the Nobel Prize this year. Wow. I'm very interested in tracing, because your career follows the arc of quantum computing in a certain way. Right? At the time

when you asked the question, what I really want to do is to figure out how to build a quantum computer, where are we in quantum computing at that point? Yeah, so that would have been 1990. So there was shores algorithm, came out, let's say 95, there was a lot of theory. And then the reason I went to Yale is because people had started to show that they could see quantum effects in electrical circuits. So these macroscopic objects, they were starting to behave quantum

mechanical. There was a really significant breakthrough in 1999, where Yazon Nakamura, in Japan,

showed that a cubic could exist in these electrical circuits. And then I found out the group at Yale were really trying to take these electrical circuits and couple of them together. And so it was like,

“if I can build something using electrical circuits, and they're big, that's the best way that you”

can sort of test and understand whether quantum mechanics breaks down at a macroscopic scale or not, can we actually make them behave as qubits? And I agree when I came to Yale, the qubits were not very good. They were actually a couple of nanoseconds. They were unstable. Electron would jump onto the chip, and then they would change all their configurations, so you have to restart your experiment.

And so for the first time at Yale, it's kind of what the challenge there was, how do we make a

cubic? Not we make a stable qubit. And that took about five years, and that took us up to 2007. And I think the rest of the world looks and says quantum's just blowing up, but it's actually being like almost phases. Theory showing that we got the algorithms. How do we make a qubit? How do we couple of the qubits together, and now we're in the scaling phase? Describe for us, because many people

“in this room may include, have only a kind of surface level understanding of what we mean when we”

use that phrase. What is the difference between classical computing and quantum computing? What does that word mean? Yeah, so you can go down the physics way and talk about superposition and entanglement, which we can go in later, but I actually feel it's a bit of a distraction. So when you think of classical computers, what they were is they were machines that were very good at adding numbers together, like simple addition. And they really showed that they could add these numbers together

really, really fast. And now with GPUs and other AI accelerators, we can add those numbers together in parallel. And so the whole classical computing can come down to just arithmetic just adding numbers together. It turns out that there's a math that is the quantum mechanics shown to be true. It's more like a group theory type structure. And the way quantum works is it has a different math that is primitive. And if we can exploit that new math and build a machine that does it,

it allows us to answer different questions. And so think of it as a branching from classical compute that is very good at adding just numbers together to something that allows us to work with an algebra that is much, much harder to represent with addition. And that algebra happens to be the same algebra that defines the fundamental equations of nature, shorting this equation. So this is why you say it computes the same way nature does, but there are many other interesting

problems. So the way I explained it to people is think of it as bringing a new primitive to computer science and allowing us to work how to go with it. And I like the analogy, well actually maybe go back. So if you went back in time, so we're 100 years of quantum. And you went back in time and you asked, "What is the foundation? Is it chemistry or physics?" What would have probably

about the small physics is about planets and things like this." And 100 years ago when a

“Heisenberg bore Einstein all the grades, shredding him himself, invented quantum mechanics,”

it was this concept that nature is discrete, not continuous. It actually brought all the physical sciences together. And now quantum mechanics is like it is the foundation of the science. And so now what quantum computing is, by that analogy is the computer science, the foundation of the math is coming together with the physical science to allow us to compute using math that if you were to try to represent it with classical computers, it takes exponential time.

Yeah. And it was a classical computer and it explained us in a way that someone is learning for him as I am, can understand it. A classical computer works primarily on problems that can be easily represented in numerical form in numbers. Yes, quantum allows you to step outside to a class of problems that don't necessarily have a simple numerical representation. Yeah. And so imagine I got some medicine or some set of operation,

look call it A. And I then followed by different operation B. If A followed by B gave a

different answer than B first followed by A. So in mathematics we call that commuting. But like

you can think of a correlation there. One, one gives you a different outcome to the other. That means there's an algebra behind it that representing that algebra traditionally on classical computers is really, really hot. Whereas that algebra, if we can get creative, we can come up with ways of representing that math. So we step as you say, we step out a side of the the simple math to a new math that to allow us to calculate interesting problems.

So quite an endisense, it doesn't replace traditional classical computing. I think this is one of this is your exactly honest. People think quantum is going to be replacing classical.

“If your problem is good at adding numbers together, you should just keep using classical computers.”

I think the future is going to be heterogeneous accelerators. And it will definitely have quantum as one. But in some sense, the next generation of superstars are going to be those applied mathematicians that know how do I write a problem using the simple math of classical computers or the more complicated math for quantum computers. And how do I actually iterate between them and things like this? This is where I think the next generation of students are going to

come up with much more novel ideas. I can give you examples of what we want to do in quantum. But like you're giving them a fundamental foundational new thing. And so I'm optimistic that we'll do much better jobs than my generation will. Yeah. We're going to get to things together in a moment. But I wanted you to, the most kind of down to you said, as a kid, you thought you might want to be a mechanic as you'd like to build things. Describe to me,

what it takes to build a quantum computer? What are you doing at different from building a classical computer? Yeah. So maybe I'll give you a analogy and then I'll go in. So the way classical computers, we've got them to get to smaller and smaller sizes, like seven enemies, five nanometers and things. It's actually inventing material to kill quantum effects. So you actually put dialectrics and other things in there to kill the quantum tunneling effects

and you want them to behave more classically. In the quantum world, you want to get rid of all the classical effects. So you want to get rid of the ability of the cubits to interact with the environment. And in the sort of technical world, we call it this quantum conflict. The more ways you want to control a quantum computer, you're open it up to interacting with everything else, like interacting

with its environment. So the biggest challenge has always been, how do we give more control

but don't bring in other sources of noise? So I want to be able to do gates on the cubit, but I don't want it to deco here. I want to couple the cubits, but I don't want them to couple to other things. So the hardest challenge is the energy inside the cubits is a nine gigahertz and if you times I play H bar or 10 to the negative 34 with 9, you're at 10 to the negative 20, like three or something in energy, that's a tiny amount of energy. So you're trying to have a tiny,

“tiny amount of energy to control and you don't want that to interact with anything. So you have to”

cool them down, you have to isolate them, and you have to make the quantum effects dominate over the classical effects. But precisely, if I'm trying to do that, right now, how big are these machines? So the cubits themselves are not that big. So the cubits themselves are like a few microns,

to one of the machines in Yorktown. You saw that they're like 24x20 foot in size. Most of that is all that equipment to isolate the cubitship, which is only a few millimeters when you put it together from the rest of the environment. We will, as we get better at that, miniaturized all the isolation, but that's cooling it down to a few milli Kelvin. So about a thousand times colder than outer space, it's isolating the noise on any electrical signal so that

no noise from the outside world gets into the system. And so that's a lot of isolators, filters, and things like that that we've had to invent to allow us to make the quantum properties of this trip go. Yeah, it's like the princess in the pea. Mounds and mounds and mounds of mattresses.

“Yep, and isolate the impact of this little thing in there. Maybe that's the best way to”

describe it. Yeah, and you've got to keep it really, really prestige. But that, when you show me so in the lobby of the Watson Research Center in Yorktown, which by the way is just a cool spill. It's like a modernist fast or piece. Anyway, in the lobby, there are two machines. It's inside a container that has three machines. So what would one of those machines cost to build right now? So typically we put them together in a way where we upgrade them because we want to,

as I was talking about before one of the things we want to do is always get algorithms done on our

machines. And I've got a roadmap of building bigger and bigger machines. So usually one of those quantum processes today is out of date in six months. So we want to build this future of computing that leverages quantum computing where every six months we've outdated a quantum processor. Eventually, hopefully we get to a point where it's like stable and it can be many years operating. But we want to get as large a quantum computer in the hands of people to explore the math as

possible to come up with those new algorithms. So we've had a philosophy of having them open, working with universities and things like that. So it answered question of course. Yes, there's cost in building the system, but we are operating in the much more in a service model where people pay to use the machine because we have to continuously calibrate it and operate it. And so depending on various different things professors, we have a credits program where they get

free access, some universities and enterprises they can buy premium access and get more access. So think of not like a cost of it because it's almost like a continuum. I want to make sure that the best quantum processes that I can build get in the hands of students and professors and interested enterprises that want to explore these machines as fast as possible. And typically every six months we upgrade. Yeah, you don't start over, you upgrade. We upgrade various

different pieces. The processor, the electronics, some upgrades are just simply replaced the

“processor. But as an example, I think many people have probably seen photos of quantum computers and”

you see this scary thing with all these wires hanging down as a product referred to as the chandelier and it's got all these wires with loops and things like that. They're called our coax

cables. When we first put the quantum computer on the cloud in 2016, you could probably only

fit about 50 qubits inside one cryostat. We've had to upgrade all those cables so that we can fit around a thousand. I want to get to 3000 and that's about minimizing. So dance your question and upgrade it depends. It can be the just the processor or it can be the complete insights. And we're actually in our third generation of our electronics to control the systems to make them faster, less noise. Internally we've got exciting results of going to something like cold cryo CMOS. So

you can bring down the cost in terms of energy of running these quantum computers almost to negligible. And you could imagine future quantum computers are not going to require much energy to run. So unlike classical compute that requires lots of energy, the biggest machines that we envision is only in the few megawatts. But we have to upgrade to future controls that use less energy. So it depends, it's my long-ense short-answer to how that upgrades and it depends on what it is.

The only observation that I felt I could was capable of making when you showed me the quantum

“machine is it's gorgeous. I look about. I've always believed that and I think there's an IBM”

saying good designers, good business, but we've always taken pride in making sure what we build.

to make sure it looks and feels good. Will you donate it to Mama when you're

“through with that particular? Actually, I think we just put a old version of one of our insights”

with the United Airlines and the APS, which is the American Physical Society and the University of Chicago. There's a replica right now if you fly into one of the terminals in Chicago, you can walk and see one. Oh, really? Yeah. Well, the most advanced thing it'll air. I'm sure. Probably. But yeah, hopefully I think yeah, we're open to that, but I appreciate that you love the design. I was beautiful. So last week I interviewed for another episode of Smart Talks,

your CEO, Ivan Krishnan. And when we got to the quantum question, I mean, he's always

brilliant and brilliant and brilliant. But quantum, he's like lit up. I mean, right in thinking that IBM is much more invested in quantum than anybody else. Is that a fair statement? Oh, yeah, most definitely. Why? Why did IBM choose to go to make this such a priority? So when I talk to the history of the physics side, there's this interesting thing in the history of computing. So we build computer, like classical computers today, using bits and CMOS and they consume energy. Do you know

that there is a way in classical where you can actually compute without using energy? It's called reversal computing. Turns out to be a terrible idea. It's not practical to build. But IBM investigated that with Ralph Laura and Charlie Bennett early on and they proved the concept that reversible computing. The first use of quantum information theory, one of the first actually was from IBM. When I did my PhD, I remember actually picking up this paper on quantum

teleportation and seeing IBM written there. And at the time, I remember thinking that they make PCs. Well, what the hell are they doing this foundational paper on quantum teleportation? Why are they doing it? So to answer your question, actually IBM was the first in quantum information science because it's the fundamental of computation. Can we actually come up with compute that we can go beyond the classical? So way before anyone was talking about it, they were doing fundamental theory.

And then as we've built it, we've always when I first came there, the experimental team was small

“in 2011. We've had a small team that we're focusing on single qubits, coupling in them. I think”

in 2012 was the first time we showed really good two cubic gates. And no one was talking about quantum computing then. And then I remember in about 2016, I said to actually open was the director of research then. Can we actually put our quantum computer on the cloud? Well, that's probably 2015. And it was always supporting that. So as we've done more and more, we've been able to do it, it's had this program going. Now I agree is very visible because we're in the scaling phase.

And so we're invested to keep scaling it. And to get why is at IBM research, what we always do

is answer what is the future of computing, whether it's coming up with new algorithms, coming up with better AI coming up with quantum, or coming up with just how do different accelerators go together? It's our DNA to answer the question of what is the future? We need a perfect problem for IBM because you kind of need to have a legacy of building stuff, just building actual physical machines. Yeah, it's why I came to IBM. I wanted the experience, the culture of

building hard things that others have not done before. Where do you imagine we are in the timeline of this technology that will come up point when it will mature? Yeah, like cell phone is a mature

“technology at this point. How far are we from that point with quantum? So I think there's various”

aspects of it. So we sat in 2007, we sent our goal that in 2023, we would be able to build a machine that was beyond classical computers to simulate it. And we achieved that in 2023. So to run, we call it a quantum circuit, the details of a dome out of it, but to run a quantum workload, that if you were to simulate that workload, how a quantum computer operates on a classical computer, you couldn't do it. So we sent that as our first. And now I've made it publicly that by

2022, we'll build the first fault-tower in quantum computer. That is one that can completely handle the noise to the level to allow you to run a very, very large, large problem. So

cubits and hundred million operations, you're talking still interesting science problems like

simulating a molecule or calculating a small optimization problem or calculating, say, some pot of a matrix update in some type of differential. So it's still be scientific, but it'll be at the point where it's beyond well beyond any classical approximate method. And then I think that's 20, 20 now. That's 20, 29. So we're four years away from something that can start to handle scientists. We'll find interesting, cauristic problems before that. And so over the next four

years, you're going to continue to see more and more, let's call him heuristic, not provable quantum problems that run on quantum computers that come out. We've seen more and more come from many of our partners in ourselves. Heuristic problems have value, but they have to be tested. They have

“to stand up over time. You have to run them many, many times you have to try different ones.”

And many times heuristic can lead to formal problems. So you're going to see, because of we're beyond now the point that you can simulate these quantum computers with any classical computer, they're kind of like a scientific tool. So they're exploring the heuristic. What do you have to get done between now and 2020 now and to get there? So we had to reinvent how we wanted to do error correction. So we have to demonstrate modules. And if we can demonstrate

these error corrected module, and our goal is actually it's called cook-a-bar. I name all our chips after birds. So it's called cook-a-bar. It's named after an Australian bird. I think I still say cook-a-bar the way Australian stew. We need to then show that we can make a single module. And then we want to connect two of those modules together. And I call that one cock-a-two, which is another Australian bird. And then if we can do that, so that's 26 and 27.

And then we want to scale them. Scale those modules. And that we call styling. And we want to scale that in 2020-9. So get a module, join two modules together, and scale. And so each module is going to be around 1,000 qubits. The challenge to getting there is that finding the right material. What, how would you describe what, that's the beauty of it. If we were to be here two years ago,

I couldn't tell you how it would be done. So we had a huge breakthrough. We came up with a new

code, a new quantum error correction code. And the biggest part of that code that is the most important is its modular nature. So previous codes, without getting too technical, they were very monolithic. And you had to build a very big device. And I wouldn't know. And we would have to invent tools like new CMOS tools to do that. So we came up with this new code. We started on 2019,

“we published in 2024. We kind of had most of things worked out in 2023. That's why we got confident”

to release the thing. So the biggest breakthrough we had is coming up with a code that's modular in nature. And think of that as a like a blueprint. And so now we have the blueprint. And now we're doing

engineering tasks to implement every part of that blueprint. And so the minute you had that breakthrough,

then you began to have confidence at some exactly these goals could be met. And then you can't. And then anyone that's done engineering will know what I'm talking about when I say this is. Cycles are learning. It takes so long from test idea to build to test. In hardware, the cycles are learning a much much lower than software. Like you can be really, really fast in the software. So then we've planned out our iterations over the next few years. And so we have to successfully

demonstrate them. I may slip because sometimes you may estimate your time wrong. But we now have

“exactly what we want to do for the next four years. What about that breakthrough from oven?”

What is the word breaks for me to that context? Like it's not that you get a call in the morning from somebody who says I did it. Do you see it coming or is it a surprise when they get there? So the way this one worked is Sergei Bravi, who's algorithm person at IBM, one of the smartest in quantum information. Go, don't mention his name. Everyone still can be able to come for him. Everyone in quantum already knows his name. I don't think there's an idea that has not

originated from him in quantum. So we're looking at other codes and we're going, all right, we've got to get serious about these codes. And others were starting to propose to bring these. And we call them LDPC codes from the classical space into the quantum. And I asked him, "We need to get ahead of this and understand what they're doing at." He's like the most modest

And then I said, "Great." Then I don't know, six months later he comes back with a hundred

page report on everyone. You want to do PC codes on like awesome. So I started then to read from them.

“And then we said, "All right, how do we under the assumptions of the hardware we can build?”

Can we get an LDPC code knowing what we can build?" And that's a great question. And so we put a small team together to investigate. And it honestly took two to three years. And we iterated and we used the constraints. So we had the sort of theory and then we had the constraints of what we could build. And we iterated for a few years. And then at the end of that we came out with a solution that, yes, it is possible to meet all the constraints of the hardware and build a code that will work.

I'm just curious about, so you have this task, this problem you want to solve. And when you set out on the task of trying to solve the problem, what's your certainly level that you'll get a solution? Well, that's the beauty of science. Four of things we kind of have a few ideas. My philosophy is prior few. For the ones that need to be in that

“black wow moment, it's honestly, you got to set the ambition really, really high. But no”

when to stop. It was a great team that went together together, that breakthrough. And we knew that we

needed to come up with a code that met the requirements of the experiment. And I think what was different before then is the theorist that we're doing error correction codes didn't necessarily know the constraints of experiments. So it was like really more pen and paper. So this became one, all right, given these sets of constraints, is it possible? Well, that's question about this, sorry, and I love these kind of moments when things become clear. At the time the problem was solved,

were you aware of the implications of the solution or did, did that tips? You knew exactly what

we set out exactly. Either we were going to have to work out how to cool down a very large piece of

silicon, which would require a lot of engineering and building tools beyond what anyone has ever built in the silicon CMOS industry to implement the knowing codes, or we had to come up with the different one. And once I knew that we had one that I didn't need to reinvent any tools to build the implications are clear. I'm which time elapsed between the time you heard the problem was solved and the time you told Arvan Krishna the CEO the problem was solved. I'm sure the next time I spoke to him I

update, but I don't remember him. The beauty of Arvan is he trusts the scientists will do it, and so he doesn't really check on us. We update him when it is, and he empowers us to do really

“hard problems. Let's talk about uses. The really like cool big shiny machine. I think you'll get”

by 2021. But all kinds of really interesting problems you're already working on. Yes, this is like another interesting area is I can prove in pen and paper algorithms that we want to run that like it's not that we don't know what to do with a quantum compute. There are hundreds of algorithms you can go to think it's called quantum zoo.com and you can see many, many algorithms. People are coming up with more of them that they proved by pen and paper. Imagine now we have a machine that you

can't simulate. How do you actually discover algorithms in a scientific way? How do you look and discover algorithms using a quantum computer? We're in this exciting period right now. And so even now I can prove these ones that we can run in the future. There's a big white space between the machines we have and we're going to build and continue to do and those are those ones that won the provable ones. And I'm an optimistic person by nature. I think getting those machines

in the hands of students to explore and look at heuristic algorithms. So looking at the equivalent of doing numerical algorithms on computers, which there's many histories of numerical algorithms being discovered on classical computers before we had formal proofs that we rely on today. People would even argue the way I work was driven numerically even though we have input into it. There are ones in optimization driven numerically. We are entering that phase. So the computer

scientists now need to go play with these primitives. Our prediction is over the next couple of years we're going to see valuable numerical equivalent algorithms emerge. And with the scientists

chemistry-flight problems as an example with our partners in Japan, they took one of our quantum

“computers and Fogaku, a very large classical supercomputer. And they ran a problem where quantum”

was just a sub-routine of the problem that was running on all of Fogaku. And they were able to look at an interesting molecule. A molecule that if you were to go by pan and paper you were to say that's going to take me a very long time to run that. They were able to run that quite accurately heuristically and already get results that are comparable with the best classical methods. So they are extremely excited because they want to push that further. And they're sort of showing that

you can take a classical supercomputer with quantum as a sub-routine and start to push the level.

They were, this was, they're trying to solve a medical problem, is that it? This, this one is like,

most people don't realize like, I and Sulphur, just something as simple as I and Sulphur, we can't solve that exactly. Like, I and Sulphur molecules are too hard. So really small, small molecules are really, really hard, too hard for classical computers to solve. People think we can solve a lot of things. It actually turns out we can't solve very much. You say solve it as this is you. No precisely how that molecule works in this constructed.

No precisely what the energy levels of that molecule is and how they come together and then be able to do that on a classical computer and compare it to a quantum. It would be really, really useful to know that specifically because if you can have energy levels, then you can estimate reaction rates. If you can estimate reaction rates, you can see how different types of chemicals were react that can then lead to better informing eventually how to build materials

or even drug design. I just want to be careful and not say, "Oh, we're going to solve drug design or that because there's many scientific steps to make that so." And so what quantum gives you is a different tool to give you more accuracy and then lead to making the different methods work. You can subcontract out aspects of a problem, quantum right now and that how just gets you whether or long and you would have been. So at the moment, even this result still does not beat

the best approximate classical method. It's comparable. So the art of chemistry for the last hundred years has been about approximating. So what we've done is we have got very, very good are coming up with ways of approximating nature and a lot of the things that we do and we exploit and we use to estimate approximations. They don't simulate nature, the way nature is, they are approximated and there's I could list many different acronyms of different methods that go into

approximating nature. What quantum gives us is to eventually get beyond that approximation and do

“it the way nature works. And so we are beating those approximation methods and this is why I think”

this is why it's still in the science, but they're getting comparable. Getting comparable with a new tool where the previous tool is a dead end makes scientists very excited. That new one is where it is. And so that's in machine learning. I'm sorry, Hamiltonian. Then there's examples in differential equations. So can I actually come up with differential equations and solve them? And if I can solve them you could look at things like Navier Stokes, Quasian goes into weather. There's

financial differential equations that you can better predict. So differential equations, there's many different examples there. And then I would say that two others are optimization and then there's quantum versions of machine learning that are very exciting as well. Please include one of the organizations that you guys have worked with. Why would the Cleveland Clinic be calling you up? Because that problem that they want to look at. So they've also done similar problem to the

ReconLab. So they've taken that method now and they've looked at molecules that matter for drug design. So they're fundamentally looking at those molecules that matter for eventually replacing some of the steps. So they're investing to see how reliable it can be done. And so there's a scientist there that's done many iterations now using the techniques that were done first with

the team in Japan. They've now replicated that for new molecules that are essential primitives

for eventually designing drugs and things that may matter for medical. Yeah. And also there's some finance firms. Yeah. Thanks. It just PC, the anger. And their interest is what? So that was the differential equation and optimization. So if you are doing very large calculations like risk,

“portfolio, or if you want to model the black holes equation or things like this that are”

fundamental for them to make better predictions come up with better trades and things like this, that is a very hard computational task. And so rather than quantum replacing that whole problem,

data. They could take their real classical method and they just replaced a tiny part of it.

They replaced a tiny part of it with a quantum subroutine that allowed them to come up with better predictions of the weights. That then when they were to compare trial A versus trial B, it was 34% better at predicting algorithmic turn. And that's a big deal for that.

“Huge. Yes. Yeah. Now, do they need to do more trials? Do they need to see? Is this a heuristic algorithm?”

Do we need to be careful? Is there other classical algorithms that go into these? These are great questions that are now being investigated. So think of this period of heuristic algorithms is really a period of scientific discovery using these machines. Yeah. Knowing that we want to

continue and build the ones which have deterministic algorithms that can run. Do the people

who would profit the most by starting to run quantum experiments realize to take with profit so much from running quantum experiments. And there was does the world know this that you given as a couple of specific examples. But generally speaking, there must be a very large university people who could gain from at least starting to play in a space. So the enterprises that use computation as key for their survival understand the limits of classical computation and

they're very interested to get started. The university's very interested could we get more students doing more algorithms 100%. Some of the limitations on the rate of algorithm discovery is because people are thinking through the classical way of writing algorithms, my belief is yes. So this is the way we want to get more and more students and things because it's just starting. But I would say in general most people are aware of it. Could we get more? Could we accelerate it? Yes. Do we

need to make better hardware? Do we need to come up with better libraries? Yes. Do we need better software? Yes. But it's all happening over the next few years. Is it hard to get someone who spent their entire life thinking in terms of solving problems? New classical means to make the transition to this new paradigm? There's a lot of examples when you approach something with the classical intuition. It's not the right way to do it when you approach through the quantum. But if people are

being taught to understand the fundamentals of the math, then a lot of the techniques carry across. I don't recommend people need to learn about in Tangaman or suposition because whilst the physicists will argue like spooky action at distance and all these type of things

“in Tangaman is the power. Yes. That's how physicists are labeled, how quantum is different.”

But I would say, do we need some physicists really worrying thinking about that? Yes. But we need more applied mathematicians that are realizing they can use this as a different way of looking at the problems. Yes. When I see one of those question, I was describing a more than a new technology. We're talking about a new paradigm, just a way of thinking about problems. Can you compare this to kind of previous technological paradigms? If I'm looking at the last couple of

hundred years, what is this rank in terms of a new field that we've opened up? It's a hard

question to answer, but I often say the history of computing, this will be the first time

that computation has branched between classical and quantum. I like thinking, reading a lot in the past. One of the things that I think was a way we changed as a society was the invention of zero. Before zero, math was limited, realizing that numbers have a number as zero allowed us to develop a whole set of new mathematics that then went on and defined everything from waves to calculus to all of that. Yes, we can describe it with that same math, but when we describe it with

that math, it gets exponentially big and gets impractical to do. Now we can actually work on it. I would say, if I had to give you a quick answer, maybe going all the way back to when we were well, except the zero. I thought you were going to say like the airplane, but in fact,

“you went several orders of magnitude beyond that. Yes. But I think it's so fundamental. This is absolutely”

fascinating. Thank you so much for chatting with me about it. Thank you for your time. Hey, listeners. So, normally we'd end this episode here. But the tech week attendees asked you some really great questions. Questions I wish I'd asked. So, we wanted to include those here in joy. Hi, Jay. Thank you so much for the great presentation. My name is Strixie Apiado. I work for a list I was Watson and Shrin's broker. I helped CISOs identify and quantify

up and night. You mentioned so many good problems that quantum can solve. It can also break

“encryptions in our classical computer systems. So, what safeguards or policies do you implement”

in your teams to build quantum capabilities responsibly? And what can we do for people in this room as builders and users to secure our data in systems before quantum computers become more energy efficient cheaper and more available? So, it's a great question. So, yes, one of the algorithms for quantum computing is to break out traditional encryption. So, at IBM Research, we were aware of this from day one. We've come up with algorithms that we believe and have very strong evidence

will not be broken by a quantum or classical computer and this has selected them. So, first, the scientific technical question, security is saved. There are algorithms that exist that we can implement that neither a quantum or classical computer can break. So, the technical answer is we're all okay. The more complicated answer is a social and society answer. Encryption was built in

classical computing in a way that was never thought of being upgraded. It's mixed everywhere.

Some of it is downstream. Some of it is software that you may use. Some of it is software that you've developed. And I get that if you've got a product and you want to have it secure for the next 10 years, you probably want to think about how you're going to upgrade it or if you have data that needs to be secure for the next 10 years, it needs to upgrade to new encryption. So, the real challenge is more of a social business problem of how do we actually transition from old encryption

to new encryption? Knowing this is going to happen. So, at IBM, I've been very proactive on this. We've developed tools where we can determine where encryption is used. We've developed tools which can show you how to replace it. And we early on have made sure the mainframe when we made these

algorithms. So, I think it was z16. That was the first version of the mainframe to have these quantum

safe algorithms implemented. So, my answer to your question is, yes, there's a real problem, but it's not a technical problem. It's a social and business problem. And I'm not minimizing that.

“I understand that that is a lot of work. You need to start now. You need to come up and do a,”

you need to make it part of your IT transformation. You need to get onto it and I realize, I realize it's not going to take zero time because it's not an easy problem to do. So, the short answer is, one, we developed algorithms that we can't and we're developing tools to help you in that transformation. Thank you so much. Thank you. My name is Emma. I'm a product manager at Expedia, working on software side of things. My question is around a non-technical roles outside of

the researchers, the mathematicians, the builders. How can the rest of us, whether it be policy makers, those in the legal fields, those thinking about what use cases quantum can solve for in a future? What should we be thinking about? And how can we prepare for that? It's a good question. I think this is part of the requirement of the scientists to be able to articulate where they are. We need

“a forum for those type of discussions. I think a lot of this can fit within the forums that we already”

have for classical NAI and I think we need to just be asking how do we actually bring them into them, because I don't think of quantum as a replacement of compute. I think of it as an accelerated that expands what is possible, and I think if we can ask those questions in those forums, are we doing enough now? I think I agree with you. No. I don't know the answer to it. I mean, it's a really interesting perspective, because those existing forums do start to bring

in those other fields as well, so it could warrant the same sort of discussion and the factors. I understand those forums right now. AI is probably dominating and it should be. Like, we are going through a period of time where AI is impacting society. Technology is impacting society in big ways, so I totally understand that most of their focus should be on AI, but we should start to ask where is quantum in that as well. I'm Gobi, and I'm a graduating PhD student at

Northwestern and also a member of South Park Commons, which is a font here. You mentioned earlier that some problems are best solved by classical versus some problems are best solved by quantum.

but we're thinking about this from an AI perspective, could you just clarify when we think about

“quantum what is deterministic and what is not deterministic? I think the future of computing”

we've got to get our heads around, is that not everything is deterministic, and it's much more going to be probabilistic. How do you handle error, buzz, how do you put confidence? I think a lot of those questions, which you're referring to in AI are going to completely apply in quantum.

I actually think it's a mistake to compare AI versus quantum. I actually think of quantum

as much its quantum versus classical compute in AI is going to come across on top. So as we go forward and we get a better understanding, I'm not going to say quantum

“is going to replace the classical compute that enables AI, but I think some of the math you do in”

AI will be able to go to both. So what can we formally prove? I can come up with a problem,

where I take a circle and I call a half of it red and half of it blue, and then I say,

I'm going to apply an operation that takes those dots, make it, say, let's say 10 dots over here, red 10 dots over here blue, and I'm going to wind them around many, many times. I can then show you that if you feed that into a classical computer, it's a classical random

“number generator. You can give yourself as much data as you want. You will never be able to say,”

did the red come from the left side or the right side? You'll take infinite data. You would have to break a classical random number generator. I can show you a quantum algorithm that can do that deterministically. So where we're thinking is when the data appears to be completely unstructured or you look essentially like a complete random number to the classical methods, there are quantum methods that can actually potentially find that structure.

That's it for this episode of Smart Talks with IBM. If you haven't already, be sure to check out my conversation with IBM Chairman and CEO Arvind Krishna and stay tuned. Another episode is coming soon. Smart Talks with IBM is produced by Matt Romano, Amy Gaines McQuade, Trina Manino, and Jake Harper. Engineering by Nina Bird Lawrence, mastering by Sarah Bruger, music by Grammyscope, strategy by Tatyana Libraman,

Cassidy Meyer, and Sophia Durlan. Smart Talks with IBM is a production of pushkin industries and Ruby Studio at Ihart Media. To find more pushkin podcasts, listen on the Ihart radio app, Apple podcasts, or wherever you listen to podcasts, I'm Malcolm Godwell. This is a paid advertisement from IBM. The conversations on this podcast don't necessarily represent IBM's positions, strategies, or opinions.