Speaker 1: Welcome to tech Stuff, a production from iHeartRadio. This season,
Speaker 1: non Smart Talks with IBM, Malcolm Glabwell is back, and
Speaker 1: this time he's taking the show on the road. Malcolm
Speaker 1: is stepping outside the studio to explore how IBM clients
Speaker 1: are using artificial intelligence to solve real world challenges and
Speaker 1: transform the way they do business. From accelerating scientific breakthroughs
Speaker 1: to reimagining education. It's a fresh look at innovation in action,
Speaker 1: where big ideas meet cutting edge solutions. You'll hear from
Speaker 1: industry leaders, creative thinkers, and of course Malcolm Glabwell himself
Speaker 1: as he guides you through each story. New episodes of
Speaker 1: Smart Talks with IBM drop every month on the iHeartRadio app,
Speaker 1: Apple Podcasts, or wherever you get your podcasts. Learn more
Speaker 1: at IBM dot com slash smart Talks.
Speaker 2: Hello, this is Malcolm Gladwell and you're listening to Smart
Speaker 2: Talks with IBM. Every year, Tech Week brings thousands of
Speaker 2: people together to network and learn about what's emerging across
Speaker 2: the technology ecosystem, and at this year's conference in San Francisco,
Speaker 2: I had an amazing opportunity to sit down in front
Speaker 2: of a live audience with Jay Gambetta. Jay has been
Speaker 2: with IBM for years and was recently promoted to Director
Speaker 2: of Research. In this job, Jay has an important mission
Speaker 2: helping the company build the future of computing. In the
Speaker 2: last episode of Smart Talks, I began to learn about
Speaker 2: quantum computing from IBM Chairman and CEO Arvind Krishna. But
Speaker 2: this conversation I had with Jay went even deeper and
Speaker 2: convinced me that the development of quantum isn't just a fun,
Speaker 2: exciting new paradigm of computing. It may be one of
Speaker 2: the most important scientific achievements of my lifetime. Jay, Good morning, morning,
Speaker 2: Welcome to Smart Talks with IBM. Thank you special live
Speaker 2: recording here for tech Week and congratulations. How long have
Speaker 2: you been Head of Research at IBM Since October one?
Speaker 2: It's October tenth today, so nine days, nine days. Can
Speaker 2: you just talk a little about the position. This is
Speaker 2: one of the most important positions in research in the world.
Speaker 3: IBM Research has been around for eighty years and it's
Speaker 3: done some tremendous technology, a lot of inventions and fundamentals
Speaker 3: for semiconductors, algorithms.
Speaker 4: AI.
Speaker 3: Yeah, I think if we look back to where a
Speaker 3: lot of the innovation and the technology of the world
Speaker 3: comes from. I think you can find Ibram's footprints on it,
Speaker 3: and you can find IBM research. So yeah, I'm very
Speaker 3: excited for the opportunity, but I'm also aware that there's
Speaker 3: big shoes to fill, and I'm looking forward to how
Speaker 3: we take IBM research forward. Obviously, I'm going to be
Speaker 3: bringing a lot of the quantum side, which we're going
Speaker 3: to talk about later. Beyond quantum, there's important work that
Speaker 3: needs to happen in AI hybrid cloud, and I think
Speaker 3: we're going to also enter to this new period of
Speaker 3: mathematics where we get to use quantum machines and also
Speaker 3: AI machines, and there's some really good, hard mathematical questions
Speaker 3: to answer.
Speaker 2: How many people do you have working for you?
Speaker 3: I mean researchers in the three thousand researchers across many
Speaker 3: different labs around the world. Our main lab is in Yorktown,
Speaker 3: but then we have the lab actually out on the
Speaker 3: West coast in Armadan or sbl now, and then we
Speaker 3: have one in Zurich, Japan, and a few others around
Speaker 3: the world.
Speaker 2: Tell me a little bit before we get into quantum.
Speaker 2: I'm just curious about your path. So you're Australian. Yep,
Speaker 2: we were talking about earlier backstage. Your accent has become muted.
Speaker 2: You should crank it up because it's.
Speaker 3: Yeah, I'm slowly losing my Australian accent. I've been in
Speaker 3: the US since two thousand and four, so accent, you know,
Speaker 3: to sound very Australian. Yeah, but how do you practice it?
Speaker 3: Maybe I got to go back to Australia. Here a
Speaker 3: more Australians say gooday, how's it going? Things like that.
Speaker 2: And you didn't grow up thinking you're going to be
Speaker 2: a scientist one day. Now.
Speaker 3: I grew up in a pretty normal life. My dreams
Speaker 3: as a kid was building things, so I was either
Speaker 3: going to be a carpenter or a mechanic. But I
Speaker 3: had some great teachers that inspired me to go to university.
Speaker 3: And I didn't even know, honestly what a scientist was.
Speaker 3: And then I found myself at university doing science, particular physics,
Speaker 3: and I ended up loving it. So you go from
Speaker 3: there to what do you do your PhD? So I
Speaker 3: did my undergrad in Australia. I did it actually in
Speaker 3: laser science, so I think I watched some TV show
Speaker 3: in lasers seemed interesting, so I wanted to learn about lasers,
Speaker 3: and then I realized in trying to understand lasers, there
Speaker 3: was this quantum mechanics, and so I was like, all right,
Speaker 3: I want to actually understand this quantum mechanics. So I
Speaker 3: did my equivalent of what you and the US school masters.
Speaker 3: We call it honors in Australia, but we do a
Speaker 3: research project. I said, I wanted to shoot lasers into
Speaker 3: Adam and measure cross sections and I got really into
Speaker 3: quantum physics. So then I decided, all right, I don't
Speaker 3: understand this quantum physics. I want to do my PhD
Speaker 3: in Interpretations of quantum mechanics. So I jumped in and said,
Speaker 3: all right, what is this quantum mechanics? Why is everyone
Speaker 3: arguing on these different interpretations. Then I finished my PhD
Speaker 3: in Australia doing that. Then I moved over. At the
Speaker 3: end of my PhD interpretations, it's more people arguing about
Speaker 3: the equations whilst I think it's really important. I decided
Speaker 3: if it's going to be like a collapse equation versus
Speaker 3: many worlds, or a hidden variable model, or that just
Speaker 3: quantum mechanics decoheres because we don't see supersitions in the
Speaker 3: everyday world because it interacts with environment. The only way
Speaker 3: to answer that question was to build a quantum computer.
Speaker 3: And so then I decided at the end of my PhD,
Speaker 3: I wanted to work out how to build a quantum computer.
Speaker 3: And then I left there and I went to Yale.
Speaker 3: And then at Yale, that's where I got into superconducting cubits,
Speaker 3: which just a few days ago, one of the professors
Speaker 3: there just won the Nobel Prize this year.
Speaker 2: Oh wow, I'm very interested in tracing because your career
Speaker 2: follows the arc of quantum computing in a certain way.
Speaker 2: Right at the time when you asked the question, what
Speaker 2: I really want to do is to figure out how
Speaker 2: to build a quantum computer. Where are we in quantum
Speaker 2: computing at that point?
Speaker 3: Yeah, So that would have been nineteen ninety So there
Speaker 3: was Shaw's algorithm came out, let's say ninety five. There
Speaker 3: was a lot of theory. And then the reason I
Speaker 3: went to Yale is because people had started to show
Speaker 3: that they could see quantum effects in electrical circuits. So
Speaker 3: these macroscopic objects they were starting to behave quantum mechanical
Speaker 3: There was a really significant breakthrough in nineteen ninety nine
Speaker 3: where Yazoo Nakamura in Japan showed that a qubit could
Speaker 3: exist in these electrical circuits. I found out the group
Speaker 3: at Yale were really trying to take these electrical circuits
Speaker 3: and couple them together. And so it was like, if
Speaker 3: I can build something using electrical circuits and they're big,
Speaker 3: that that's the best way that you can decide to
Speaker 3: test and understand whether quantum mechanics breaks down at a
Speaker 3: macroscopic scale or not. Can we actually make them behave
Speaker 3: as cubits? And I agree When I came to Yale,
Speaker 3: the cubits were not very good. They were actually a
Speaker 3: couple of nanoseconds. They were unstable. Electron would jump onto
Speaker 3: the chip and then they would change all their configurations,
Speaker 3: so you have to restart your experiment. And so for
Speaker 3: the first time at Yale, it's kind of what the
Speaker 3: challenge there was, how do we make a cubit? How
Speaker 3: do we make a stable cubit? And that took about
Speaker 3: five years, and that took us up to two thousand
Speaker 3: and seven. And I think the rest of the world
Speaker 3: looks and says quantums like just blowing up, but it's
Speaker 3: actually been like almost phases theory showing that wet the algorithms,
Speaker 3: how do we make a cubit? How do we couple
Speaker 3: of the cubits together? And now we're in the scaling phase.
Speaker 2: Describe for us because many people in this room, me included,
Speaker 2: have only a kind of surface level understanding of what
Speaker 2: we mean when we use that phrase. What is the
Speaker 2: difference between classical computing and quantum computing? What does that
Speaker 2: word mean?
Speaker 3: Yeah, so you can go down the physics way and
Speaker 3: talk about supersition and entanglement, which we can go in later,
Speaker 3: but actually feel it's a bit of a distraction. So
Speaker 3: when you think of classical computers, what they were is
Speaker 3: there were machines that were very good at adding numbers together,
Speaker 3: like simple addition, and they really showed that they could
Speaker 3: add these numbers together really really fast. And now with
Speaker 3: GPUs and other AI accelerators, we can add those numbers
Speaker 3: together in parallel, and so the whole classical computing can
Speaker 3: come down to just arithmetic, just adding numbers together. It
Speaker 3: turns out that there's a math that is the quantum
Speaker 3: mechanics shown to be true. It's more like a group
Speaker 3: theory type structure, and the way quantum works is it
Speaker 3: has a different math as are primitive, and if we
Speaker 3: can exploit that new math and build a machine that
Speaker 3: does it, it allows us to answer different questions. And
Speaker 3: so think of it as a branching from classical compute
Speaker 3: that is very good at adding just numbers together to
Speaker 3: something that allows us to work with an algebra that
Speaker 3: is much much harder to represent with addition. And that
Speaker 3: algebra happens to be the same algebra that defines the
Speaker 3: fundamental equations of nature, shirting this equation. So this is
Speaker 3: why you say it computes the same way nature does,
Speaker 3: but there are many other interesting problems. So the way
Speaker 3: I explain it to people is think of it as
Speaker 3: bringing a new primitive to computer science and allowing us
Speaker 3: to work how to go with it. And I like
Speaker 3: the analogy. Well, actually, maybe go back. So if you
Speaker 3: went back in time, so we're one hundred years of quantum,
Speaker 3: and you went back in time and you asked, what
Speaker 3: is the foundation is a chemistry or physics? What would
Speaker 3: have probably the scientists of one hundred years ago would
Speaker 3: have said is they would have said, you know, chemistry
Speaker 3: is about the small, physics is about planets and things
Speaker 3: like this, and one hundred years ago when Heisenberg or Einstein,
Speaker 3: all the greats, Schroding her himself invented quantum mechanics. It
Speaker 3: was this concept that nature is discrete, not continuous. It
Speaker 3: actually brought all the physical sciences together. And now quantum
Speaker 3: mechanics is like it is the foundation of the science.
Speaker 3: And so now what quantum computing is by that analogy
Speaker 3: is computer science. The foundation of the math is coming
Speaker 3: together with the physical science to allow us to compute
Speaker 3: using math that if you were to try to represent
Speaker 3: it with classical computers, it takes exponential time.
Speaker 2: Yeah, and it was a classical computer an expence in
Speaker 2: a way that someone is well informed as I am
Speaker 2: can understand it. A customer computer. It works primarily on
Speaker 2: problems that can be easily represented in numerical form in numbers. Yes,
Speaker 2: quantum allows you to step outside to a class of
Speaker 2: problems that don't necessarily have a simple numerical representation. Yeah.
Speaker 3: And so imagine I got some medicine or or some
Speaker 3: set of operation, but call it A, and I then
Speaker 3: follow it by a different operation B. If A followed
Speaker 3: by B gave a different answer than B first followed
Speaker 3: by A. So in mathematics we call that commuting. But
Speaker 3: like you can think of a correlation there one one
Speaker 3: gives you a different outcome to the other. That means
Speaker 3: there's an algebra behind it that Representing that algebra traditionally
Speaker 3: on classical computers is really really hard, whereas that algebra,
Speaker 3: if we can get creative, we can come up with
Speaker 3: ways of representing that math. So we step as you say,
Speaker 3: we step out aside of the simple math to a
Speaker 3: new to allow us to calculate interesting problems.
Speaker 2: So quite in a sense, compliments, it doesn't replace judicial.
Speaker 3: I think this is one of the this is you're
Speaker 3: exactly on is people think quantum is going to be
Speaker 3: replacing classical If your problem is good at adding numbers together,
Speaker 3: you should just keep using classical computers. I think the
Speaker 3: future is going to be heterogeneous accelerators, and it will
Speaker 3: definitely have quantum as one. But in some sense, the
Speaker 3: next generation of superstars are going to be those applied
Speaker 3: mathematicians that know, how do I write a problem using
Speaker 3: the simple math of classical computers or the more complicated
Speaker 3: math for quantum computers, and how do I actually iterate
Speaker 3: between them? And things like this. This is where I
Speaker 3: think the next generation of students are going to come
Speaker 3: up with much more novel ideas. I can give you
Speaker 3: examples of what we want to do on quantum, but
Speaker 3: like you're giving them a fundamental, foundational new thing, and
Speaker 3: so I'm optimistic they will do much better jobs than
Speaker 3: my generation.
Speaker 2: Well, yeah, we're to get to some of the albums
Speaker 2: in a moment. But I wanted you to the most
Speaker 2: kind of down that you said as a kid, you
Speaker 2: thought you might want to be a mechanic because you'd
Speaker 2: like to build things. Describe to me what it takes
Speaker 2: to build a quantum computer, Like, what are you doing
Speaker 2: that's different from building a classical computer.
Speaker 3: Yeah, so maybe I'll give you analogy and then I'll
Speaker 3: go in. So the way classical computers, we've got them
Speaker 3: to get to smaller and smaller sizes like five seven animeters,
Speaker 3: five animeters and things, is actually inventing material to kill
Speaker 3: quantum effects. So you actually put dielectrics and other things
Speaker 3: in there to kill the quantum tunneling effects, and you
Speaker 3: want them to behave more classically in the quantum world,
Speaker 3: you want to get rid of all the classical effects.
Speaker 3: So you want to get rid of the ability of
Speaker 3: the cubits to interact with the environment, and in the
Speaker 3: in the sort of technical world, we call it this
Speaker 3: quantum com The more ways you want to control the
Speaker 3: quantum computer, you open it up to interacting with everything else,
Speaker 3: like interacting with its environment. So the biggest challenge has
Speaker 3: always been how do we give more control but don't
Speaker 3: bring in other sources of noise. So I want to
Speaker 3: be able to do gates on the cubit, but I
Speaker 3: don't want it to decohere. I want to couple the cubits,
Speaker 3: but I don't want them to couple to other things.
Speaker 3: So the hardest challenge is the energy inside the cubits
Speaker 3: is a nine gigahertz, and if your tames that by
Speaker 3: HBO ten to the niggave thirty four with nine, you're
Speaker 3: at a tender the negative twenty like three or something
Speaker 3: in energy. That's a tiny amount of energy. So you're
Speaker 3: trying to have a tiny, tiny amount of energy to control,
Speaker 3: and you don't want that to interact with anything. So
Speaker 3: you have to cool them down, you have to isolate them,
Speaker 3: and you have to make the quantum effects dominate over
Speaker 3: the classical effects.
Speaker 2: So practically, if I'm trying to do that. Right now,
Speaker 2: how big are these machines?
Speaker 3: So the cubits themselves are not that big, So the
Speaker 3: cubits themselves are like a few microns. But yeah, most
Speaker 3: of the size so you can see some of our
Speaker 3: I got a pleasure of showing you around to one
Speaker 3: of the machines in Yorktown. You saw that they're like
Speaker 3: twenty foot by twenty foot in size. Most of that
Speaker 3: is all that equipment to isolate the cubit chip, which
Speaker 3: is only a few millimeters when you put it together
Speaker 3: from the rest of the environment. We will, as we
Speaker 3: get better at that, miniaturize all the isolation. But that's
Speaker 3: cooling it down to a few milli calvin, so about
Speaker 3: a thousand times colder than outer space. It's isolating the
Speaker 3: noise on any electrical signal so that no noise from
Speaker 3: the outside world gets into the system. And so that's
Speaker 3: a lot of isolators, filters, and things like that that
Speaker 3: we've had to invent to allow us to make the
Speaker 3: quantum properties of the chip go.
Speaker 2: It's like the Princess and the peak, mounds and mounds
Speaker 2: and mounds of mattresses trying to isolate the impact of
Speaker 2: this little thing and that maybe that's the best way
Speaker 2: to describe it.
Speaker 3: Yeah, and you've got to keep it really really prestige.
Speaker 2: But that when you show me. So in the in
Speaker 2: the lobby of the Watson Research Center in New Yorktown,
Speaker 2: which by the way, is just the coolest building. It's
Speaker 2: like a it's like a modernist it's awesome master piece. Anyway,
Speaker 2: in the lobby there is there are these is it
Speaker 2: two machines.
Speaker 3: It's it's inside a container that has three machines.
Speaker 2: Three machines, So what can you can you tell me
Speaker 2: what would one of those machines cost to build right now?
Speaker 3: So typically we put them together in a way where
Speaker 3: we upgrade them because we want to as I as
Speaker 3: I was talking about before, one of the things we
Speaker 3: want to do is always get algorithms done on our machines.
Speaker 3: And I've got a roadmap of building bigger and bigger machines.
Speaker 3: So usually one of those quantum processors today is out
Speaker 3: of date in six months. So we want to build
Speaker 3: this future of computing that leverages quantum computing where every
Speaker 3: six months we've outdated a quantum processor. Eventually, hopefully we
Speaker 3: get to a point where it's like stable and it
Speaker 3: can be many years operating. But we want to get
Speaker 3: as large a quantum computer in the hands of people
Speaker 3: to explore the math as possible to come up with
Speaker 3: those new algorithms. So we've had a philosophy of having
Speaker 3: them open, working with universities and things like that. So
Speaker 3: to answer a question of costs, yes, there's cost in
Speaker 3: building the system, but we are operating in them much
Speaker 3: more in a service model where people pay to use
Speaker 3: the machine because we have to continuously calibrate it and
Speaker 3: operate it and so depending on various different things. Professors,
Speaker 3: we have a credits program where they get free access.
Speaker 3: Some universities and enterprises they can buy premium access and
Speaker 3: get more access. So think of not like a cost
Speaker 3: of it, because it's almost like a continuum. I want
Speaker 3: to make sure that the best quantum processors that I
Speaker 3: can build get in the hands of students and professors
Speaker 3: and interested enterprises that want to explore these machines as
Speaker 3: fast as possible. And typically every six months we upgrade it. Yeah,
Speaker 3: you don't start over, you upgrade. We upgrade various different pieces,
Speaker 3: the processor, the electronics. Some upgrades are just simply replaced
Speaker 3: the processor. But as an example, I think many people
Speaker 3: have probably seen photos of quantum computers and you see
Speaker 3: this scary thing with all these wires hanging down, as
Speaker 3: I've referred to as the chandelier, and it's got all
Speaker 3: these wires with loops and things like that. They're called
Speaker 3: co x cables. When we first put the quantum computer
Speaker 3: on the cloud in twenty sixteen, you could probably only
Speaker 3: fit about fifty cubits inside one cryostat. We've had to
Speaker 3: upgrade all those cables so that we can fit around
Speaker 3: one thousand to get to three thousand, and that's about minaturizing.
Speaker 3: So to answer your question, an upgrade, it depends. It
Speaker 3: can be just the processor or it can be the
Speaker 3: complete insides. And we're actually in our third generation of
Speaker 3: our electronics to control the systems to make them faster,
Speaker 3: less noise. Internally. We've got exciting results of going to
Speaker 3: something like cold cryocemos. So you can bring down the
Speaker 3: cost in terms of energy of running these quantum computers
Speaker 3: almost to negligible, and you could imagine future quantum computers.
Speaker 3: I'm not going to require much energy to run, so
Speaker 3: unlike classical compute that requires lots of energy. The biggest
Speaker 3: machines that we envision is only in the few megawatts,
Speaker 3: but we have to upgrade to future controls that use
Speaker 3: less energy. So it depends it's my long answer, short
Speaker 3: answer to how it upgrades, and it depends on what
Speaker 3: it is.
Speaker 2: The only observation that I felt I was capable of
Speaker 2: making when you showed me the quantum machine is it's gorgeous.
Speaker 2: I look a art.
Speaker 3: I've always believed that, and I think that there's an
Speaker 3: IBM saying good design is good business. But we've always
Speaker 3: taken pride in making sure what we build. I don't know,
Speaker 3: I feel if you're going to build something that is new,
Speaker 3: that can change, you should take the time to make
Speaker 3: sure it looks and feels good.
Speaker 2: Will you donated to MoMA when you're through with that particular?
Speaker 3: Actually, I think we just put an old version of
Speaker 3: one of our insights with the United Airlines and the AAPS,
Speaker 3: which is the American Physical Society and the University of Chicago.
Speaker 3: There's a replica right now. If you fly into one
Speaker 3: of the terminals in Chicago, you can walk and see one.
Speaker 2: Oh really, yeah, well the most advanced thing at a
Speaker 2: air I'm sure.
Speaker 3: Probably, but yeah, I hopefully. I think, yeah, we're open
Speaker 3: to that. But yeah, I appreciate that you love the design.
Speaker 2: It was beautiful. So I last week I interviewed for
Speaker 2: another episode Smotox, your CEO, Ivin Krishne, and when we
Speaker 2: got to the quantum question. I mean, he's always alliant
Speaker 2: and brilliant, and but quantum, he's like lit up. I
Speaker 2: mean right in thinking that IBM is much more invested
Speaker 2: in quantum than anybody else. Is that a fair statement? Oh?
Speaker 3: Yeah, most definitely.
Speaker 2: Why Why did IBM choose to kind of make this
Speaker 2: such a priority.
Speaker 3: So when I took to the history of the physics side,
Speaker 3: there's this interesting thing in the history of computing. So
Speaker 3: we build computer like classical computers today using bits and
Speaker 3: ce moss, and they consume energy. Do you know that
Speaker 3: there is a way in classical where you can actually
Speaker 3: compute without using energy. It's called reversal computing. Turns out
Speaker 3: to be a terrible idea, it's not practical to build.
Speaker 3: But IBM investigated that with Ralph Laura and Charlie Bennett
Speaker 3: early on, and they proved the concept that we're versable computing.
Speaker 3: The first use of quantum information theory. One of the
Speaker 3: first actually was from IBM. When I did my PhD,
Speaker 3: I remember actually picking up this paper on quantum teleportation
Speaker 3: and seeing IBM written there, and at the time I
Speaker 3: remember thinking that they make PCs. Well, what the hell
Speaker 3: are they doing this foundational paper on quantum teleportation? Why
Speaker 3: are they doing it? So to answer your question, actually,
Speaker 3: IBM was the first in quantum information science because it's
Speaker 3: the fundamental of computation. Can we actually come up with
Speaker 3: compute that we can go beyond the classical So way
Speaker 3: before anyone was talking about it, they were doing fundamental theory.
Speaker 3: And then as we've built it, we've always When I
Speaker 3: first came there, the experimental team was small. In twenty eleven,
Speaker 3: we've had a small team that we're focusing on single
Speaker 3: cubits coupling in them. I think in twenty twelve was
Speaker 3: the first time we showed really good two Cuba gates
Speaker 3: and no one was talking about quantum computing that And
Speaker 3: then I remember in about twenty sixteen I said to
Speaker 3: actually Arvin was the director of research, then can we
Speaker 3: actually put our quantum computer on the cloud? Well that's
Speaker 3: probably twenty fifteen, and it was always supporting that. So
Speaker 3: as we've done more and more we've been able to
Speaker 3: do it. It's had this program going now, I agree
Speaker 3: is very visible, like because we're in this scaling phase
Speaker 3: and so we're invested to keep scaling it and to
Speaker 3: get why is At IBM research, what we always do
Speaker 3: is answer what is the future of computing? Whether it's
Speaker 3: coming up with new algorithms, coming up with better AI,
Speaker 3: coming up with quantum, or coming up with just how
Speaker 3: do different accelerators go together. It's our DNA to answer
Speaker 3: the question of what is the future.
Speaker 2: Need a perfect problem for IBM because you kind of
Speaker 2: need to have a legacy of buildings, building actual physical machines.
Speaker 3: Yeah, it's why I came to IBM. I wanted the experience,
Speaker 3: the culture of building hard things that others have not
Speaker 3: done before.
Speaker 2: Where do you imagine we are in the timeline of
Speaker 2: this technology? It will come a point when it will mature.
Speaker 2: My cell phone is a mature technology at this point.
Speaker 2: How far are we from that point with condom?
Speaker 3: So I think there's various aspects of it. So we
Speaker 3: set in twenty and seventy we set our goal that
Speaker 3: in twenty twenty three we would be able to build
Speaker 3: a machine that was beyond classical computers to simulate it,
Speaker 3: and we achieved that in twenty twenty three. So to
Speaker 3: run a bigger we call it a quantum circuit. The
Speaker 3: details of it din't matter, but to run a quantum
Speaker 3: workload that if you were to simulate that workload how
Speaker 3: a quantum computer operates on a classical computer, you couldn't
Speaker 3: do it. So we set that does our first and
Speaker 3: now I've made it publicly that by twenty twenty nine
Speaker 3: we'll build the first fault tolerant corantum computer. That is,
Speaker 3: one that can completely handle the noise to the level
Speaker 3: to allow you to run a very very large, large problem.
Speaker 2: So an example of a large problem.
Speaker 3: Yeah, a large quantum problem. So for around a couple
Speaker 3: of one hundred cubits and one hundred million operations, you're
Speaker 3: talking still interesting science problems like simulating a molecule, or
Speaker 3: calculating a small optimization problem, or calculating say some part
Speaker 3: of a matrix update in some type of differential. So
Speaker 3: it'll still be scientific, but it'll be at the point
Speaker 3: where it's beyond, well beyond any classical approximate method. And
Speaker 3: then I think that's twenty twenty nine that's twenty twenty nine,
Speaker 3: so we're four.
Speaker 2: Years away from something that can start to handle.
Speaker 3: Interesting problem, serious problems. I do believe the scientists will
Speaker 3: find interesting heuristic problems before that, and so over the
Speaker 3: next four years, you're going to continue to see more
Speaker 3: and more let's call them heuristic not provable quantum problems
Speaker 3: that run on quantum computers that come out. We're seeing
Speaker 3: more and more come from many of our partners and ourselves.
Speaker 3: Heuristic problems have value, but they have to be tested,
Speaker 3: they have to stand up over time. You have to
Speaker 3: run them many, many times, you have to try different ones,
Speaker 3: and many times heuristic can lead to formal problems. So
Speaker 3: you're going to see because we're beyond now the point
Speaker 3: that you can simulate these quantum computers with any classical computer.
Speaker 3: They're kind of like a scientific tool. So they're exploring
Speaker 3: the heuristic.
Speaker 2: What do you have to get done between now and
Speaker 2: twenty twenty nine to get there?
Speaker 3: So we had to reinvent how we wanted to do
Speaker 3: error correction. So we have to demonstrate modules and if
Speaker 3: we can demonstrate these error corrected module and our goal
Speaker 3: is actually it's called Crookobarro I name all our chips
Speaker 3: after birds, so it's called Kookobara. It is named after
Speaker 3: an Australian verte. I think I still say kooka burrow
Speaker 3: the way Australians do. We need to then show that
Speaker 3: we can make a single module and then we want
Speaker 3: to connect two of those modules together, and I call
Speaker 3: that one cockatoo, which is another Australian vert. And then
Speaker 3: if we can do that, so that's twenty six and
Speaker 3: twenty seven, and then we want to scale the scale
Speaker 3: those modules, and that we call starling and we want
Speaker 3: to scale that in twenty twenty nine. So get a module,
Speaker 3: join two modules together and scale and so each module
Speaker 3: is going to be around one thousand cubits.
Speaker 2: The challenge to getting there is it finding the right
Speaker 2: material or how would you describe what? That's the beauty
Speaker 2: to be done.
Speaker 3: That's the beauty of it is if we would have
Speaker 3: been here two years ago, I couldn't tell you how
Speaker 3: it would be done. So we had a huge breakthrough.
Speaker 3: We came up with a new code, a new quantumeric
Speaker 3: Russian code, and that code. The biggest im part of
Speaker 3: that code that is the most important is it's modular
Speaker 3: in nature. So previous codes without getting too technical, they
Speaker 3: were very monolithic and you had to build a very
Speaker 3: big device, and I wouldn't have known we would have
Speaker 3: to invent tools like new Simos tools to do that.
Speaker 3: So we came up with this new code. We started
Speaker 3: on twenty nineteen, we published in twenty twenty four. We
Speaker 3: kind of had most of things worked out in twenty
Speaker 3: twenty three. That's why we got confident to release the thing.
Speaker 3: So the biggest breakthrough we had is coming up with
Speaker 3: a code that's modular in nature, and think of that
Speaker 3: as a like a blueprint. And so now we have
Speaker 3: the blueprint, and now we're doing engineering tasks to implement
Speaker 3: every part of that blueprint.
Speaker 2: And so the minute you had that breakthrough, then you
Speaker 2: began to have confidence at something exactly these goals could
Speaker 2: be met.
Speaker 3: And then you can't. And then anyone that's done engineering
Speaker 3: will know what I'm talking about when I say this
Speaker 3: is cycles are learning. It takes so long from test
Speaker 3: idea to build two tests. In hardware, the cycles of
Speaker 3: learning are much much lower than software, Like you can
Speaker 3: be really really faster in the software. So then we've
Speaker 3: planned out our iterations over the next few years, and
Speaker 3: so we have to successfully demonstrate them. I may slip,
Speaker 3: because sometimes you may estimate your time wrong, but we
Speaker 3: now have exactly what we want to do for the
Speaker 3: next four years.
Speaker 2: I want to go back to that breakthrough for a moment.
Speaker 2: What does the word breaks we mean in that context, Like,
Speaker 2: it's not that you get a call in the morning
Speaker 2: from somebody who says, I did it. Do you see
Speaker 2: it coming? Or is it a surprise when they get there.
Speaker 3: So the way this one worked is Sogo Brave, who's
Speaker 3: an algorithm person at IBM, one of the smartest and
Speaker 3: quantum information.
Speaker 2: Don't mention his name to everyone value you'll come for him.
Speaker 3: Everyone in quantum already knows his name. I don't think
Speaker 3: there's an idea that has not originated from him in quantum.
Speaker 3: So we're looking at other codes and we'll go all right,
Speaker 3: we've got to get serious about these codes. And others
Speaker 3: were starting to propose to bring these and then we
Speaker 3: call them LDBC codes from the classical space into the quantum.
Speaker 3: And I asked him, we need to get ahead of
Speaker 3: this and understand what they're doing it. He's like the
Speaker 3: most modest perfuse late, Jay, let me learn about them
Speaker 3: and I'll generate a report for us and we'll read
Speaker 3: through it. And then I said, great, Then I don't know.
Speaker 3: Six months later, he comes back with one hundred page
Speaker 3: report on everyone. Everyone had done an LTPC codes. I'm like, awesome.
Speaker 3: So I started then to read from them. And then
Speaker 3: we said, all right, how do we under the assumptions
Speaker 3: of the hardware we can build? Can we get an
Speaker 3: LTPC code knowing what we can build? And that's a
Speaker 3: great question, and so we put a small team together
Speaker 3: to investigate and honestly took two to three years, and
Speaker 3: we iterated, and we used the constraints, so we had
Speaker 3: the sort of theory, and then we had the constraints
Speaker 3: of what we could build. And we iterated for a
Speaker 3: few years, and then at the end of that we
Speaker 3: came out with a solution that, yes, it is possible
Speaker 3: to meet all the constraints of the hardware and build
Speaker 3: a code that will work.
Speaker 2: I'm just curious about So you had this task, this
Speaker 2: problem you want to solve, and when you set out
Speaker 2: on the task of trying to solve the problem, what's
Speaker 2: your certainty level that you'll get a solution.
Speaker 3: Well, that's the beauty of science. For things where you
Speaker 3: kind of have a few ideas. My philosophy is try
Speaker 3: a few for the ones that need to be in
Speaker 3: that like wow moment. It's honestly, you've got to set
Speaker 3: the ambition really, really high, but know when to stop.
Speaker 3: It was a great team that went together to get
Speaker 3: that breakthrough, and we knew that we needed to come
Speaker 3: up with a code that met the requirements of the experiment.
Speaker 3: And I think what was different before then is the
Speaker 3: theorists that were doing error correction codes didn't necessarily know
Speaker 3: the constraints of experiments, so it was like really more
Speaker 3: pen and paper. So this became one all right, given
Speaker 3: these sets of constraints, is it possible?
Speaker 2: When that's questions about this? Sorry, And I love these
Speaker 2: kind of moments when things become clear. At the time
Speaker 2: the problem was solved, were you aware of the implications
Speaker 2: of the solution or did that takes you knew exactly.
Speaker 3: What we set out exactly like either we were going
Speaker 3: to have to work out how to cool down a
Speaker 3: very large piece of silicon, which would require a lot
Speaker 3: of engineering and building tools beyond what anyone has ever
Speaker 3: built in the silicon semoss industry. To implement the known
Speaker 3: codes or we had to come up with a different one,
Speaker 3: and once I knew that we had one that I
Speaker 3: didn't need to reinvent any tools to build. The implications
Speaker 3: are clear how.
Speaker 2: Much time elapsed between the time you heard the problem
Speaker 2: was solved and the time you told Arvin Krishna, the CEO,
Speaker 2: the problem was solved.
Speaker 3: I'm sure the next time I spoke to him, I update,
Speaker 3: but I don't remember. The beauty of Avin is he
Speaker 3: trusts the scientists will do it, and so he doesn't
Speaker 3: really check on us. We update him when when it
Speaker 3: is and he he empowers us to do really hard problems.
Speaker 2: Yeah, so let's talk about uses. I mean they're really
Speaker 2: like cool, big shiny machine. I think you'll get paid
Speaker 2: twenty twenty nine. But there's all kinds of really interesting
Speaker 2: problems you're already working on.
Speaker 3: Yes, this is like another interesting area is I can
Speaker 3: prove in pen and paper algorithms that we want to
Speaker 3: run that. Like, it's not that we don't know what
Speaker 3: to do with a quantum computer. There are hundreds of algorithms.
Speaker 3: So you can go to I think it's called quantumzoo
Speaker 3: dot com and you can see many many algorithms people
Speaker 3: are coming up with more of more of them that
Speaker 3: they prove by pen and paper. But imagine now we
Speaker 3: have a machine that you can't simulate. How do you
Speaker 3: actually discover algorithms in a scientific way? How do you
Speaker 3: look and discover algorithms using a quantum computer. We're in
Speaker 3: this exciting period right now, and so even though I
Speaker 3: can prove these ones that we can run in the future,
Speaker 3: there's a big white space between what the machines we
Speaker 3: have and we're going to build and continue to do
Speaker 3: and those ones that want the provable ones. And I'm
Speaker 3: an optimistic person by nature. I think getting those machines
Speaker 3: in the hands of students to explore and look at
Speaker 3: heuristic algorithms. So looking at the equivalent of doing numerical
Speaker 3: algorithms on computers, which there's many histories of numerical algorithms
Speaker 3: being discovered on classical computers before we had formal proofs
Speaker 3: that we rely on today people would even are you
Speaker 3: the way AI works was driven numerically, even though we
Speaker 3: have input into it. There are ones in optimization driven numerically.
Speaker 3: We are entering that phase. So the computer scientists now
Speaker 3: need to go play with these primitives. Our prediction is
Speaker 3: over the next couple of years, we're going to see
Speaker 3: valuable numerical equivalent algorithms emerge. And where the scientists are
Speaker 3: going is in four categories. One is simulating nature, so
Speaker 3: looking at either hay Enerji physics, chemistry, light problems. As
Speaker 3: an example, with our partners in Japan, they took one
Speaker 3: of our quantum computers and for Gackle, a very large
Speaker 3: classical supercomputer, and they ran a problem where quantum was
Speaker 3: just a sub routine of the problem that was running
Speaker 3: on all of for Gackle, and they were able to
Speaker 3: look at an interesting molecule, a molecule that if you
Speaker 3: would go by pan and paper you would have said,
Speaker 3: it's going to take me a very long time to
Speaker 3: run that. They were able to on that quite accurately,
Speaker 3: heuristically and already get results that are comparable with the
Speaker 3: best classical methods. So they are extremely excited because they
Speaker 3: want to push that further, and they're sort of showing
Speaker 3: that you can take a classical supercomputer with quantum as
Speaker 3: a subroutine and start to push the level.
Speaker 2: They were This was trying to solve a medical problem.
Speaker 3: Is this one is a like most people don't realize,
Speaker 3: like iron sulfur, just something as simple as iron and sulfur,
Speaker 3: we can't solve that exactly, Like iron sulfur, molecules are
Speaker 3: too hard. So really small small molecules are really really hard,
Speaker 3: too hard for classical computers to solve. People think we
Speaker 3: can solve a lot of things. It actually turns out
Speaker 3: we can't solve very much.
Speaker 2: You say solve ins instance, you know precisely how that
Speaker 2: molecule works and is constructed.
Speaker 3: No, precisely what the energy levels of that molecule is
Speaker 3: and how they come together, and then be able to
Speaker 3: do that on a classical computer and compare it to
Speaker 3: a quantum.
Speaker 2: It would be really really useful to know that specific.
Speaker 3: Because if you can have energy levels, then you can
Speaker 3: estimate reaction rates. If you can estimate reaction rates, you
Speaker 3: can see how different types of chemicals will react. That
Speaker 3: can then lead to better informing eventually how to build
Speaker 3: materials or even drug design. I just want to be
Speaker 3: careful and not say, oh, we're going to solve drug
Speaker 3: design or that, because there's many scientific steps to make
Speaker 3: that so and so what quantum gives you as a
Speaker 3: different tool to give you more accuracy and then lead
Speaker 3: to making the different methods work.
Speaker 2: You can subcontract out aspects of a problem quantum right now,
Speaker 2: and that just gets you further along and you would.
Speaker 3: Have been so at the moment. Even this result still
Speaker 3: does not beat the best approximate classical method. It's comparable.
Speaker 3: So the art of chemistry for the last hundred years
Speaker 3: has been about approximating. So what we've done is we
Speaker 3: have got very very good are coming up with ways
Speaker 3: of approximating nature. And a lot of the things that
Speaker 3: we do and we exploit and we use to estimate approximations.
Speaker 3: They don't a stimulate nature of the way nature is.
Speaker 3: They approximate it. And there's I could list many different
Speaker 3: acronyms of different methods that go into approximating nature. What
Speaker 3: quantum gives us is to eventually get beyond that approximation
Speaker 3: and do it the way nature works. And so we
Speaker 3: aren't beating those approximation methods. And this is why I think,
Speaker 3: this is why it's still in the science. But they're
Speaker 3: getting comparable. Getting comparable with a new tool where the
Speaker 3: previous tool is a dead end makes scientists very excited. Yeah,
Speaker 3: that nuance is where it is, and so that's in
Speaker 3: machine learning, sorry Hamiltonian. Then there's examples in differential equations,
Speaker 3: So can I actually come up with differential equations and
Speaker 3: solve them? And if I can solve them, you could
Speaker 3: look at things like an avious Stokes equation goes into weather.
Speaker 3: There's financial differential equations that you can better predict. So
Speaker 3: differential equations. There's many different examples there. And then I
Speaker 3: would say that two others are optimization, and then there's
Speaker 3: quantum versions of machine learning that are very exciting as well.
Speaker 2: Cleveland Clinic one of the organizations that you guys have
Speaker 2: worked with. Why would the Cleveland Clinic be calling you up?
Speaker 3: Because that problem that they want to look at. So
Speaker 3: they've also done similar problem to the recent lab. So
Speaker 3: they've taken that method now and they've looked at molecules
Speaker 3: that matter for drug design. So they're fundamentally looking at
Speaker 3: those molecules that matter for eventually replacing some of the steps.
Speaker 3: So they're investing to see how reliable it can be done.
Speaker 3: And so there's a scientist there that's done many iterations
Speaker 3: now using the techniques that were done first with the
Speaker 3: team in Japan. They've now replicated that for new molecules
Speaker 3: that are essential primitives for eventually designing drugs and things
Speaker 3: that may matter for medical.
Speaker 2: And also there's some finance firms yep, HBC, Vango, yep,
Speaker 2: and their interest is.
Speaker 3: What so that was the differential equation and optimization. So
Speaker 3: if you are doing very large calculations like risk portfolio,
Speaker 3: or if you want to model the Black Shaws equation
Speaker 3: or things like this that are fundamental for them to
Speaker 3: make better predictions, come up with better trades and things
Speaker 3: like this. That is a very hard computational task. And
Speaker 3: so rather than quantum replacing that whole problem, can quantum
Speaker 3: be a subroutine in there? And what HSBC showed is
Speaker 3: they showed they could take their real data, they could
Speaker 3: take their real classical method and they just replaced a
Speaker 3: tiny part of it. They replaced a tiny part of
Speaker 3: it with a quantum subroutine that allowed them to come
Speaker 3: up with better predictions of the weights that then when
Speaker 3: they were to compare TRIALA versus Trial B, it was
Speaker 3: thirty four percent better at predicting algorithmic tron And that's
Speaker 3: a big deal for them.
Speaker 2: It's huge.
Speaker 3: Yes, Now, do they need to do more trials? Do
Speaker 3: they need to see is this a heuristic algorithm? Do
Speaker 3: we need to be careful? Is there other classical algorithms
Speaker 3: that go into these are great questions that are now
Speaker 3: being investigated. So think of this period of heuristic algorithms
Speaker 3: is really a period of scientific discovery using these machines,
Speaker 3: knowing that we want to continue and build the ones
Speaker 3: which have determinist their algorithms that can run.
Speaker 2: Do the people who would profit the most by starting
Speaker 2: to run quantum experiments realize that they would profit so
Speaker 2: much from running quantum experience And does the world know this.
Speaker 2: You've given us a couple of specific examples, but generally speaking,
Speaker 2: there must be a very large universe of people who
Speaker 2: could gain from at least starting to play in the space.
Speaker 3: So the enterprises that use computation as key for their
Speaker 3: survival understand the limits of classical computation and they're very
Speaker 3: interested to get started. The universities are very interested. Could
Speaker 3: we get more students doing more algorithms? One hundred percent?
Speaker 3: Some of the limitations on the rate of algorithm discovery
Speaker 3: is because people are thinking through the classical way of
Speaker 3: writing algorithms. My belief is yes, So this is why
Speaker 3: we want to get more and more students and things,
Speaker 3: because it's just starting. But I would say in general,
Speaker 3: most people are aware of it. Could we get more,
Speaker 3: could we accelerate it?
Speaker 2: Yes?
Speaker 3: Do we need to make better hardware, do we need
Speaker 3: to come up with better libraries, yes? Do we need
Speaker 3: better software yes, But it's all happening over the next
Speaker 3: few years.
Speaker 2: Is it hard to get someone who's spent their entire
Speaker 2: life thinking in terms of solving problems to classical means
Speaker 2: to make the transition to this new paradigm.
Speaker 3: There's a lot of examples when you approach something with
Speaker 3: the classical intuition, it's not the right way to do
Speaker 3: it when you approach it through the quantum. But if
Speaker 3: people are being taught to understand the fundamentals of the math,
Speaker 3: then a lot of the techniques carry across. I don't
Speaker 3: recommend people need to learn about entanglement or supersition because
Speaker 3: whilst the physicists will argue like spooky action a distance
Speaker 3: and all these type of things, entanglement is the power. Yes,
Speaker 3: that's how physicists are labeled. How quantum is different. But
Speaker 3: I would say, do we need some physicists really worrying
Speaker 3: thinking about that?
Speaker 1: Yes?
Speaker 3: But We need more applied mathematicians that are realizing they
Speaker 3: can use this as a as a different way of
Speaker 3: looking at the problems.
Speaker 2: Yeah, I when I asked you one question, No, we're
Speaker 2: describing a a It's more than a new technology. We're
Speaker 2: talking about a new paradigm. It's a way of thinking
Speaker 2: about problems. Can you compare this to kind of previous
Speaker 2: technological paradigms. If I'm thinking at the last couple hundred years,
Speaker 2: what does this rank in terms of a new field
Speaker 2: that we've opened up.
Speaker 3: It's a hard question to answer, but I often say
Speaker 3: the history of computing, this will be the first time
Speaker 3: that computation has branched between classical and quantum. I like
Speaker 3: thinking reading a lot in the past. One of the
Speaker 3: things that I think was a way we changed as
Speaker 3: a society was the invention of zero. Before zero, math
Speaker 3: was limited. Realizing that numbers have a number as zero
Speaker 3: allowed us to develop a whole set of new mathematics
Speaker 3: that then went on and defined like everything from waves
Speaker 3: to calculus to all of that. Yes, we can describe
Speaker 3: it with that same math, but when we describe it
Speaker 3: with that math, it gets exponentially big and gets impractical
Speaker 3: to do. Now we can actually work on it. I
Speaker 3: would say, if I had to give you a quick answer,
Speaker 3: maybe going all the way back to when we were
Speaker 3: accepted zero.
Speaker 2: I thought you were going to say, like the airplane,
Speaker 2: but in fact, yeah, you went several orders of magnitude
Speaker 2: beyond that.
Speaker 3: Yes, but I think it's so fundamental.
Speaker 2: This is absolutely fascinating. Thank you so much for chatting
Speaker 2: with me about it.
Speaker 3: Thank you. Fret time.
Speaker 2: Hey listeners. So normally we end this episode here, but
Speaker 2: the Tech Week attendees asked Jay some really great questions,
Speaker 2: questions I wish I'd asked, so we wanted to include
Speaker 2: those here. Enjoy.
Speaker 5: Hi, J, thank you so much for the great presentation.
Speaker 5: My name is Trixie Apiado. I work for Willis Towers Watson,
Speaker 5: an insurance broker. I help seisos identify and quantify their
Speaker 5: cyber risk so they can prepare for threats before they happen.
Speaker 5: And so quantum threats keep me up at night. You
Speaker 5: mentioned so many good problems that quantum can solve. It
Speaker 5: can also break encryptions in our classical computer systems. So
Speaker 5: what safeguards or policies do you implement in your teams
Speaker 5: to build quantum capabilities responsibly and what can we do
Speaker 5: for people in this room, US builders and users to
Speaker 5: secure our data in systems before quantum computers become more
Speaker 5: energy efficient, cheaper, and more available.
Speaker 3: So it's a great question. So yes, one of the
Speaker 3: algorithms for quantum computing is to break our traditional encryption.
Speaker 3: So at IBM Research we were aware of this from
Speaker 3: day one. We've come up with algorithms that we believe
Speaker 3: and have very strong evidence will not be broken by
Speaker 3: a quantum or classical computer, and has selected them. So
Speaker 3: first the scientific technical question, security is saved. There are
Speaker 3: algorithms that exist that we can implement that neither a
Speaker 3: quantum or classical computer can break. So the technical answer
Speaker 3: is we're all okay. The more complicated answer is a
Speaker 3: social and society answer. Encryption was built in classical computing
Speaker 3: in a way that was never thought of being grade it.
Speaker 3: It's mixed everywhere. Some of it is downstream, some of
Speaker 3: it is like software that you may use, Some of
Speaker 3: it is software that you've developed. And I get that
Speaker 3: if you've got a product and you want to have
Speaker 3: it secure for the next ten years, you probably want
Speaker 3: to think about how you're going to upgrade it, or
Speaker 3: if you have data that needs to be secure for
Speaker 3: the next ten years, it needs to upgrade to new encryption.
Speaker 3: So the real challenge is more of a social business
Speaker 3: problem of how do we actually transition from old encryption
Speaker 3: to new encryption knowing this is going to happen. So
Speaker 3: we at IBM have been very proactive on this. We've
Speaker 3: developed tools where we can determine where encryption is used,
Speaker 3: We've developed tools which can show you how to replace it,
Speaker 3: and we early on have made sure the Mainframe when
Speaker 3: we made these algorithms. So I think it was Z
Speaker 3: sixteen that was the first version of the Mainframe to
Speaker 3: have these quantum safe algorithms implemented. So my answer to
Speaker 3: your question is, yes, there's a real problem, but it's
Speaker 3: not a technical problem. It's a social and business problem.
Speaker 3: And I'm not minimizing that. I understand that that is
Speaker 3: a lot of work you need to start now. You
Speaker 3: need to come up and do a you need to
Speaker 3: make it part of your IT transformation. You need to
Speaker 3: get onto it. And I realize, I realize it's not
Speaker 3: going to take zero time because it's not an easy
Speaker 3: problem to do. So the short answer is one we
Speaker 3: developed algorithms that we can't, and we're developing tools to
Speaker 3: help you in that transformation.
Speaker 5: Thank you so much.
Speaker 2: Thank you. My name is Emma.
Speaker 6: I'm a product manager at Expedia, working on software side
Speaker 6: of things. My question is around the non technical roles
Speaker 6: outside of the researchers, the mathematicians, the builders. How can
Speaker 6: the rest of us, whether it be policymakers, those in
Speaker 6: the legal fields, those thinking about what use cases quantum
Speaker 6: can solve for in a few what should we be
Speaker 6: thinking about and how can we prepare for that.
Speaker 3: It's a good question. I think this is part of
Speaker 3: the requirement of the scientists to being able to articulate
Speaker 3: where they are. We need a forum for those type
Speaker 3: of discussions. I think a lot of this can fit
Speaker 3: within the forums that we already have for classical and AI,
Speaker 3: and I think we need to just be asking how
Speaker 3: do we actually bring them into them Because I don't
Speaker 3: think of quantum as a replacement of compute. I think
Speaker 3: of it as an accelerator that expands what is possible,
Speaker 3: and I think we can ask those questions in those forums.
Speaker 3: Are we doing enough now? I think I agree with you. No,
Speaker 3: I don't know the answer to it.
Speaker 6: I think it's a really interesting perspective because those existing
Speaker 6: forums do start to bring in those other fields as well,
Speaker 6: so it could warrant the same sort of discussion.
Speaker 3: And yeah, acts, and I understand those forums. Right now,
Speaker 3: AI is probably dominating and it should be like we
Speaker 3: going through a period of time where AI is impacting society.
Speaker 3: The technology is impacting society in big ways. So I
Speaker 3: totally understand that most of their focus should be on AI,
Speaker 3: but we should start to ask where is quantum in
Speaker 3: that as well?
Speaker 4: Hi, I'm Gobi and I'm a graduating PhD student at
Speaker 4: Northwestern and also a member of south Park Commons, which
Speaker 4: is a fund here. You mentioned earlier that some problems
Speaker 4: are best solved by classical versus some problems are best
Speaker 4: solved by quantum. When we're thinking about this, if we're
Speaker 4: not experts in quantum, but we're thinking about this from
Speaker 4: an AI perspective, could you just clarify when we think
Speaker 4: about quantum, what is deterministic and what is not deterministic.
Speaker 3: I think the future of computing we've got to get
Speaker 3: our heads around is that not everything is deterministic, and
Speaker 3: it's much more going to be probilistic. How do you
Speaker 3: handle error bars? How do you put confidence? I think
Speaker 3: a lot of those questions which you're referring to INAI
Speaker 3: are going to completely imply and quantum. I actually think
Speaker 3: it's a mistake to compare AI verse quantum. I actually
Speaker 3: think of quantum as much. It's quantum verse classical compute,
Speaker 3: and AI is going to come across on top. So
Speaker 3: as we go forward and we get a better understanding thing,
Speaker 3: I'm not going to say quantum is going to replace
Speaker 3: the classical compute that enables AI, but I think some
Speaker 3: of the math you do in AI will be able
Speaker 3: to go to both. So what can we formally prove?
Speaker 3: I can come up with a problem where I take
Speaker 3: a circle and a color, half of it red and
Speaker 3: half oft of blue, and then I say, I'm going
Speaker 3: to apply an operation that takes those dots make it. Say,
Speaker 3: let's say ten dots over here red, ten dots over
Speaker 3: here blue, and I'm going to wind them around many,
Speaker 3: many times. I can then show you that if you
Speaker 3: feed that into a classical computer it's a classical random
Speaker 3: number generator. You can give yourself as much data as
Speaker 3: you want. You will never be able to say did
Speaker 3: the red come from the left side or the right side.
Speaker 3: You would take infinite data. It is like you would
Speaker 3: have to break a classical random number generator. I can
Speaker 3: show you a quantum algorithm that can do that deterministically.
Speaker 3: So where we're thinking is when the data appears to
Speaker 3: be completely unstructured or you looks essentially like a complete
Speaker 3: random number to the classical methods, there are quantum methods
Speaker 3: that can actually potentially find that structure.
Speaker 2: That's it for this episode of Smart Talks with IBM.
Speaker 2: If you haven't already, be sure to check out my
Speaker 2: conversation with IBM Chairman and CEO Arvind Krishna, and stay tuned.
Speaker 2: Another episode is coming soon. Smart Talks with IBM is
Speaker 2: produced by Matt Romano, Amy Gains, McQuaid, Trina Menino, and
Speaker 2: Jake Harper. Engineering by Nina Bird Lawrence, Mastering by Sarah Buguer,
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