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Speaker 1: Hello, This is Malcolm Gladwell and you're listening to Smart

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Speaker 1: Talks with IBM. Every year, tech Week brings thousands of

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Speaker 1: people together to network and learn about what's emerging across

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Speaker 1: the technology ecosystem, and at this year's conference in San Francisco,

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Speaker 1: I had an amazing opportunity to sit down in front

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Speaker 1: of a live audience with Jay Gambetta. Jay has been

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Speaker 1: with IBM for years and was recently promoted to Director

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Speaker 1: of Research. In this job, Jay has an important mission

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Speaker 1: helping the company build the future of computing. In the

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Speaker 1: last episode of Smart Talks, I began to learn about

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Speaker 1: quantum computing from IBM Chairman and CEO Arvind Krishna. But

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Speaker 1: this conversation I had with Jay went even deeper and

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Speaker 1: convinced me that the development of quantum isn't just a fun,

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Speaker 1: exciting new paradigm of computing. It may be one of

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Speaker 1: the most important scientific achievements of my lifetime. Jay, Morning, Morning,

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Speaker 1: Welcome to Smart Talks with IBM. Thank your special live

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Speaker 1: recording here for tech Week, and congratulations. How long have

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Speaker 1: you been Head of Research at IBM?

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Speaker 2: Since October one? It's October tenth today, since nine days,

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Speaker 2: nine days.

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Speaker 1: Can you just talk a little about the position. This

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Speaker 1: is one of the most important positions in research in

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Speaker 1: the world.

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Speaker 2: IBM research has been around for eighty years and it's

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Speaker 2: done some tremendous technology, a lot of inventions and fundamentals

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Speaker 2: for semiconductors, algorithms, AI. Yeah, I think if we look

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Speaker 2: back to where a lot of the innovation and the

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Speaker 2: technology of the world comes from, I think you can

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Speaker 2: find Ibram's footprints on it, and you can find IBM research.

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Speaker 2: So yeah, I'm very excited for the opportunity, but I'm

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Speaker 2: also aware that there's big shoes to fill, and I'm

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Speaker 2: looking forward to how we take IBM research forward. Obviously,

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Speaker 2: I'm going to be bringing a lot of the quantum side,

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Speaker 2: which we're going to talk about later. Beyond quantum, there's

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Speaker 2: important work that needs to happen in AI hybrid cloud,

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Speaker 2: and I think we're going to also enter into this

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Speaker 2: new period of mathematics where we get to use quantum

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Speaker 2: machines and also AI machines. And there's some really good

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Speaker 2: hard mathematical questions to answer.

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Speaker 1: How many people do you have working for you?

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Speaker 2: I've been researchers in the three thousand researchers across many

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Speaker 2: different labs around the world. Our main lab is in Yorktown,

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Speaker 2: but then we have the lab actually out on the

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Speaker 2: West coast in Armatan or sbl now, and then we

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Speaker 2: have one in Zurich, Japan, and a few others around

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Speaker 2: the world.

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Speaker 1: Tell me a little bit before we get into quantum.

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Speaker 1: I'm just curious about your path. So you're Australian. Yep,

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Speaker 1: we were talking about earlier. Backstage. Your accent has become muted.

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Speaker 1: You should crank it up because it's.

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Speaker 2: Yeah, I'm slowly losing my Australian accent. I've been in

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Speaker 2: the US since two thousand and four, so accent, you know,

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Speaker 2: to sound very Australian. Yeah, but how do you practice it?

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Speaker 2: Maybe I got to go back to Australia. Here more

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Speaker 2: Australians say gooday, how's it going? Things like that?

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Speaker 1: And you you didn't grow up thinking you're going to

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Speaker 1: be a scientist one day, now.

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Speaker 2: I grew up in a pretty normal life. My dreams

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Speaker 2: as a kid was building things, so I was either

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Speaker 2: going to be a carpenter or a mechanic. But I

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Speaker 2: had some great teachers that inspired me to go to

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Speaker 2: university and I didn't even know honestly what a scientist was.

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Speaker 2: And then I found myself at university doing science, particular physics,

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Speaker 2: and I ended up loving it. So you go from

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Speaker 2: there to what do you do your PhD? So I

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Speaker 2: did my undergrad in Australia. I did it actually in

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Speaker 2: laser science, so I think I watched some TV show

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Speaker 2: in lasers seemed interesting, so I wanted to learn about lasers,

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Speaker 2: and then I realized in trying to understand lasers there

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Speaker 2: was this quantum mechanics, and so I was like, all right,

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Speaker 2: I want to actually understand this mechanics. So I did

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Speaker 2: my equivalent of what you and the US gore masters.

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Speaker 2: We call it honors in Australia, but we do a

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Speaker 2: research project. I said, I wanted to shoot lasers into

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Speaker 2: atoms and measure cross sections and I got really into

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Speaker 2: quantum physics. So then I decided, all right, I don't

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Speaker 2: understand this quantum physics. I want to do my PhD

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Speaker 2: in interpretations of quantum mechanics. So I jumped in and said,

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Speaker 2: all right, what is this quantum mechanics? Why is everyone

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Speaker 2: arguing on these different interpretations? Then I finished my PhD

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Speaker 2: in Australia doing that. Then I moved over at the

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Speaker 2: end of my PhD interpretations, it's more people arguing about

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Speaker 2: the equations whilst I think it's really important. I decided

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Speaker 2: if it's going to be like a collapse equation versus

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Speaker 2: many worlds, or a hidden variable model, or that just

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Speaker 2: quantum mechanics decoheres because we don't see supersitions in the

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Speaker 2: everyday world because it interacts with environment. The only way

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Speaker 2: to answer that question was build a quantum computer. And

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Speaker 2: so then I decided at the end of my PhD,

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Speaker 2: I wanted to work out how to build a quantum computer.

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Speaker 2: And then I left there and I went to Yale,

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Speaker 2: and then at Yale, that's where I got into superconducting cubits,

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Speaker 2: which just a few days ago one of the professors

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Speaker 2: there just won the Nobel Price this year.

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Speaker 1: Oh wow, I'm very interested in tracing because your career

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Speaker 1: follows the arc of quantum computing in a certain way.

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Speaker 1: Right at the time when you asked the question, what

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Speaker 1: I really want to do is to figure out how

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Speaker 1: to build a quantum computer. Where are we in quantum

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Speaker 1: computing at that point?

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Speaker 2: Yeah, So that would have been nineteen ninety so there

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Speaker 2: was Shaw's algorithm came out let's say ninety five. There

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Speaker 2: was a lot of theory, and then the reason I

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Speaker 2: went to Yale is because people had started to show

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Speaker 2: that they could see quantum effects in electrical circuits. So

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Speaker 2: these macroscopic objects they were starting to behave quantum mechanical

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Speaker 2: There was a really significant breakthrough in nineteen ninety nine

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Speaker 2: where Yazoo Nakamura in Japan showed that a cubit could

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Speaker 2: exist in these electrical circuits. And then I found out

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Speaker 2: the group at Yale were really trying to take these

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Speaker 2: electrical circuits and couple them together. And so it was like,

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Speaker 2: if I can build something using electrical circuits and they're big,

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Speaker 2: that that's the best way that you cancide to test

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Speaker 2: and understand whether quantum mechanics breaks down at a macroscopic

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Speaker 2: scale or not. Can we actually make them behave as cubits?

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Speaker 2: And I agree. When I came to Yale, the qubits

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Speaker 2: were not very good. They were actually a couple of nanoseconds.

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Speaker 2: They were unstable. Electron would jump onto the chip and

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Speaker 2: then they would change all their configurations, so you have

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Speaker 2: to restart your experiment. And so for the first time

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Speaker 2: at Yale. It's kind of what the challenge there was,

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Speaker 2: how do we make a cubit? How do we make

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Speaker 2: a stable cubit? And that took about five years, and

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Speaker 2: that took us up to two thousand and seven. And

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Speaker 2: I think the rest of the world looks and says

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Speaker 2: quantums like just blowing up, But it's actually been like

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Speaker 2: almost phases theory, showing that we got the algorithms, how

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Speaker 2: do we make a cubit? How do we couple of

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Speaker 2: the cubits together? And now we're in the scaling phase.

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Speaker 1: Describe for us because many people in this room, me included,

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Speaker 1: have only a kind of surface level understanding of what

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Speaker 1: we mean when we use that phrase. What is the

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Speaker 1: difference between classical computing and quantum computing? What does that

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Speaker 1: word mean?

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Speaker 2: Yeah, so you can go down the physics way and

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Speaker 2: talk about supersition and entanglement, which we can go in later,

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Speaker 2: but I actually feel it's a bit of a distraction.

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Speaker 2: So when you think of classical computers, what they were

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Speaker 2: is there were machines that were very good at adding

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Speaker 2: numbers together, like simple addition, and they really showed that

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Speaker 2: they could add these numbers together really really fast, and

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Speaker 2: now with GPUs and other AI accelerators, we can add

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Speaker 2: those numbers together in parallel, and so the whole classical

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Speaker 2: computing can come down to just arithmetic, just adding numbers together.

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Speaker 2: It turns out that there's a math that is with

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Speaker 2: the quantum mechanics shown to be true. It's more like

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Speaker 2: a group theory type structure. And the way quantum works

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Speaker 2: is it has a different math as are primitive and

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Speaker 2: if we can exploit that new math and build a

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Speaker 2: machine that does it, it allows us to answer different questions.

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Speaker 2: And so think of it as a branching from classical

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Speaker 2: compute that is very good at adding just numbers together

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Speaker 2: to something that allows us to work with an algebra

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Speaker 2: that is much much harder to represent with addition. And

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Speaker 2: that algebra happens to be the same algebra that defines

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Speaker 2: the fundamental equations of nature shirting as equation. So this

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Speaker 2: is why you say it computes the same way nature does.

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Speaker 2: But there are many other interesting problems. So the way

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Speaker 2: I explain it to people is think of it as

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Speaker 2: bringing a new permiitive to computer science and allowing us

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Speaker 2: to work out to go with it. And I like

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Speaker 2: the analogy well, actually, maybe go back. So if you

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Speaker 2: went back in time, so we're one hundred years of quantum,

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Speaker 2: and you went back in time and you asked, what

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Speaker 2: is the foundation is a chemistry or physics? What would

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Speaker 2: have probably the scientists of one hundred years ago would

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Speaker 2: have said is they would have said, you know, chemistry

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Speaker 2: is about the small, physics is about planets and things

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Speaker 2: like this. And one hundred years ago when Heisenberg or Einstein,

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Speaker 2: all the greats shreading her himself invented quantum mechanics, it

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Speaker 2: was this concept that nature is discrete, not continuous. It

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Speaker 2: actually brought all the physical sciences together. And now quantum

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Speaker 2: mechanics is like it is the foundation of the science.

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Speaker 2: And so now what quantum computing is by that analogy

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Speaker 2: is computer science. The foundation of the math is coming

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Speaker 2: together with the physical science to allow us to compute

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Speaker 2: using math that if you were to try to represent

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Speaker 2: it with classical computers, it takes exponential time.

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Speaker 1: Yeah, and it was a classical computer an expense in

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Speaker 1: a way that someone is well informed as I am

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Speaker 1: can understand it. A customer computer works primarily on problems

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Speaker 1: that can be easily represented in numerical form in numbers. Yes,

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Speaker 1: Quantum allows you to step outside to a class of

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Speaker 1: problems that don't necessarily have a simple numerical representation.

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Speaker 2: Yeah. And so imagine I got some medicine or or

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Speaker 2: some set of operation, but call it A, and I

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Speaker 2: then follow it by a different operation B if A

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Speaker 2: followed by B gave a different answer than B first

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Speaker 2: followed by A. So in mathematics we call that commuting.

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Speaker 2: But like you can think of a correlation there one

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Speaker 2: one gives you a different outcome to the other. That

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Speaker 2: means there's an algebra behind it. That representing that algebra

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Speaker 2: traditionally on classical computers is really really hard, whereas that algebra,

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Speaker 2: if we can get creative, we can come up with

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Speaker 2: ways of representing that math. So we step as you say,

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Speaker 2: we step out aside of the simple math to a

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Speaker 2: new math to allow us to calculate interesting problems.

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Speaker 1: So quite in a sense, compliments it doesn't replace judicial.

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Speaker 1: That's good.

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Speaker 2: I think this is one of the this is you're

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Speaker 2: exactly on is people think quantum is going to be

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Speaker 2: replacing classical If your problem is good at adding numbers together,

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Speaker 2: you should just keep using classical computers. I think the

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Speaker 2: future is going to be heterogeneous accelerators, and it will

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Speaker 2: definitely have quantum as one. But in some sense, the

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Speaker 2: next generation of superstars are going to be those applied

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Speaker 2: mathematicians that know, how do I write a problem using

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Speaker 2: the simple math of classical computers or the more complicated

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Speaker 2: math for quantum computers, and how do I actually iterate

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Speaker 2: between them? And things like this. This is where I

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Speaker 2: think the next generation of students are going to come

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Speaker 2: up with much more novel ideas. I can give you

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Speaker 2: examples of what we want to do on quantum, but like,

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Speaker 2: you're giving them a fundamental, foundational new thing, and so

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Speaker 2: I'm optimistic that will do much better jobs than my generation.

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Speaker 1: Well, yeah, we're to get to some of the albums

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Speaker 1: in a moment. But I wanted you to the most

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Speaker 1: kind of down that you said as a kid, you

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Speaker 1: thought you might want to be a mechanic because you'd

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Speaker 1: like to build things. Describe to me what it takes

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Speaker 1: to build a quantum computer, Like, what are you doing

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Speaker 1: that's different from building a classical computer.

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Speaker 2: Yeah, so maybe I'll give you analogy and then I'll

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Speaker 2: go in so the way classical computers, we've got them

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Speaker 2: to get to smaller and smaller sizes like five seven animeters,

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Speaker 2: five animeters and things is actually inventing material to kill

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Speaker 2: quantum effects. So you actually put dielectrics and other things

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Speaker 2: in there to kill the quantum tunneling effects, and you

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Speaker 2: want them to behave more classically. In the quantum world.

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Speaker 2: You want to get rid of all the classical effects,

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Speaker 2: so you want to get rid of the ability of

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Speaker 2: the cubits to interact with the environment. And in the

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Speaker 2: sort of technical world we call it this quantum conflict.

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Speaker 2: The more ways you want to control the quantum computer,

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Speaker 2: you open it up to interacting with everything else, like

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Speaker 2: interacting with its environment. So the biggest challenge has always

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Speaker 2: been how do we give more control but don't bring

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Speaker 2: in other sources of noise. So I want to be

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Speaker 2: able to do gates on the cubit, but I don't

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Speaker 2: want it to decohere. I want to couple the cubits,

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Speaker 2: but I don't want them to couple to other things.

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Speaker 2: So the hardest challenge is the energy inside the cubits

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Speaker 2: is a nine gigahertz, and if your tames that by

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Speaker 2: HBO tend to the neggive thirty four with nine, you're

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Speaker 2: at a tender the negative twenty like three or something

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Speaker 2: in energy. That's a tiny amount of energy. So you're

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Speaker 2: trying to have a tiny, tiny amount of energy to control,

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Speaker 2: and you don't want that to interact with anything. So

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Speaker 2: you have to cool them down, you have to isolate them,

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Speaker 2: and you have to make the quantum effects dominate over

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Speaker 2: the classical effects.

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Speaker 1: So practically, if I'm trying to do that right now,

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Speaker 1: how big are these machines?

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Speaker 2: So the cubits themselves are not that big, So the

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Speaker 2: cubits themselves are like a few microns. But yeah, most

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Speaker 2: of the size so you can see some of our

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Speaker 2: I got the pleasure of showing you around to one

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Speaker 2: of the machines in Yorktown. You saw that they're like

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Speaker 2: twenty foot by twenty foot in size. Most of that

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Speaker 2: is all that equipment to isolate the cubit chip, which

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Speaker 2: is only a few millimeters when you put it together

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Speaker 2: from the rest of the environment. We will, as we

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Speaker 2: get better at that, miniaturize all the isolation. But that's

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Speaker 2: cooling it down to a few milli calvin, so about

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Speaker 2: a thousand times colder than outer space. It's isolating the

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Speaker 2: noise on any electrical signal so that no noise from

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Speaker 2: the outside world gets into the system. And so that's

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Speaker 2: a lot of isolators, filters and things like that that

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Speaker 2: we've had to invent to allow us to make the

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Speaker 2: quantum properties of this chip go.

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Speaker 1: It's like the Princess and the pea. Mounds and mounds

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Speaker 1: and mounds of mattresses trying to isolate the impact of

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Speaker 1: this little thing.

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Speaker 2: And that maybe that's the best way to describe it. Yeah,

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Speaker 2: and you've got to keep it really really prestige.

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Speaker 1: But that when you show me so in the in

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Speaker 1: the lobby of the Watson Research Center in New Yorktown,

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Speaker 1: which by the way, is just the coolest building. It's

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Speaker 1: like a it's like a modernist it's awesome master piece. Anyway,

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Speaker 1: in the lobby there's there are these is it two machines.

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Speaker 2: It's it's inside a container that has three machines.

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Speaker 1: Three machines. So what can you can you tell me

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Speaker 1: what would one of those machines cost to build? Right now?

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Speaker 2: So typically we put them together in a way where

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Speaker 2: we upgrade them because we want to as I as

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Speaker 2: I was talking about before, one the things we want

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Speaker 2: to do is always get algorithms done on our machines,

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Speaker 2: and I've got a roadmap of building bigger and bigger machines.

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Speaker 2: So usually one of those quantum processes today is out

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Speaker 2: of date in six months. So we want to build

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Speaker 2: this future of computing that leverages quantum computing where every

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Speaker 2: six months we've outdated a quantum processor. Eventually, hopefully we

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Speaker 2: get to a point where it's like stable and it

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Speaker 2: can be many years operating. But we want to get

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Speaker 2: as large a quantum computer in the hands of people

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Speaker 2: to explore the math as possible to come up with

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Speaker 2: those new algorithms. So we've had a philosophy of having

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Speaker 2: them open, working with universities and things like that. So

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Speaker 2: to answer a question of costs, yes, there's cost in

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Speaker 2: building the system, but we are operating in them much

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Speaker 2: more in a service model where people pay to use

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Speaker 2: the machine because we have to continuously calibrate it and

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Speaker 2: operate it and so depending on very different things. Professors,

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Speaker 2: we have a credits program where they get free access

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Speaker 2: some universities and enterprises, they can buy premium access and

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Speaker 2: get more access. So think of not like a cost

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Speaker 2: of it, because it's almost like a continuum. I want

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Speaker 2: to make sure that the best quantum processors that I

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Speaker 2: can build get in the hands of students and professors

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Speaker 2: and interested enterprises that want to explore these machines as

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Speaker 2: fast as possible. And typically every six months we upgrade it. Yeah,

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Speaker 2: you don't start over, you upgrade. We upgrade various different pieces,

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Speaker 2: the processor, the electronics. Some upgrades are just simply replaced

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Speaker 2: the processor. But as an example, I think many people

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Speaker 2: have probably seen photos of quantum computers and you see

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Speaker 2: this scary thing with all these wires hanging down, as

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Speaker 2: I've referred to as the chandelier, and it's got all

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Speaker 2: these wires with loops and things like that. They're called

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Speaker 2: co x cables. When we first put the quantum computer

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Speaker 2: on the cloud in twenty sixteen, you could probably only

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Speaker 2: fit about fifty cubits inside one cryostat. We've had to

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Speaker 2: upgrade all those cables so that we can fit around

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Speaker 2: one thousand. I want to get to three thousand, and

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Speaker 2: that's about miniaturizing. So to answer your question, an upgrade,

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Speaker 2: it depends. It can be just the processor or it

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Speaker 2: can be the complete insides. And we're actually in our

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Speaker 2: third generation of our electronics to control the systems, to

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Speaker 2: make them faster, less noise. Internally, We've got exciting results

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Speaker 2: of going to something like cold cryocemos. So you can

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Speaker 2: bring down the cost in terms of energy of running

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Speaker 2: these quantum computers almost to negligible, and you could imagine

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Speaker 2: future quantum computers. I'm not going to require much energy

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Speaker 2: to run, so unlike classical compute that requires lots of energy.

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Speaker 2: The biggest machines that we envision is only in the

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Speaker 2: few megawatts. But we have to upgrade to future controls

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Speaker 2: that use less energy. So it depends it's my long

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Speaker 2: end short answer to how it upgrades, and it depends

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Speaker 2: on what it is.

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Speaker 1: The only observation that I felt I was capable of

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Speaker 1: making when you showed me the quantum machine is it's gorgeous.

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Speaker 1: I look at art.

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Speaker 2: I've always believed that, and I think that there's an

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Speaker 2: IBM saying good design is good business. But we've always

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Speaker 2: taken pride in making sure what we build. I don't know,

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Speaker 2: I feel if you're going to build something that is

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Speaker 2: new that can change, you should take the time to

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Speaker 2: make sure it looks and feels good.

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Speaker 1: Will you donated to MoMA when you're through with that

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Speaker 1: particular actually I think we just put an old version

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Speaker 1: of one of our insights with the United Airlines and

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Speaker 1: the AAPS, which is the American Physical Society, and the

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Speaker 1: University of Chicago.

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Speaker 2: There's a replica right now. If you fly into one

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Speaker 2: of the terminals in Chicago, you can walk and see one.

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Speaker 1: Oh really, yeah, well the most advanced thing at oh air.

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Speaker 1: I'm sure probably, but yeah, hopefully. I think, yeah, we're

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Speaker 1: open to that. But I yeah, I appreciate that you

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Speaker 1: love the design. I was beautiful. So I last week

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Speaker 1: I interviewed for another episode of Smart Tox your CEO,

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Speaker 1: Irvin Kushner, And when we got to the quantum question.

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Speaker 1: I mean, he's always alliant and brilliant, and but quantum,

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Speaker 1: he's like lit up. I mean right in thinking that

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Speaker 1: IBM is much more invested in quantum than anybody else.

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Speaker 1: Is that a fair statement? Oh yeah, most definitely. Why

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Speaker 1: Why did IBM choose to kind of make this such

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Speaker 1: a priority.

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Speaker 2: So when I took to the history of the physics side,

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Speaker 2: there's this interesting thing in the history of computing. So

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Speaker 2: we build computer classical computers today using bits and see moss,

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Speaker 2: and they consume energy. Do you know that there is

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Speaker 2: a way in classical where you can actually compute without

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Speaker 2: using energy. It's called reversal computing. Turns out to be

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Speaker 2: a terrible idea, not practical to build, but IBM investigated

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Speaker 2: that with Ralph Laura and Charlie Bennett early on, and

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Speaker 2: they proved the concept that reversible computing. The first use

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Speaker 2: of quantum information theory. One of the first actually was

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Speaker 2: from IBM. When I did my PhD, I remember actually

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Speaker 2: picking up this paper on quantum teleportation and seeing IBM

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Speaker 2: written there, and at the time I remember thinking that

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Speaker 2: they make PCs. Well, what the hell are they doing

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Speaker 2: this foundational paper on quantum teleportation? Why are they doing it?

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Speaker 2: So to answer your question, actually, IBM was the first

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Speaker 2: in quantum information science because it's the fundamental of computation.

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Speaker 2: Can we actually come up with compute that we can

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Speaker 2: go beyond the classical So way before anyone was talking

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Speaker 2: about it, they were doing fundamental theory. And then as

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Speaker 2: we've built it, we've always When I first came there,

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Speaker 2: the experimental team was small. In twenty eleven, we've had

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Speaker 2: a small team that we're focusing on single cubitts coupling

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Speaker 2: in them. I think in twenty twelve was the first

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Speaker 2: time we showed really good two Cuba gates and no

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Speaker 2: one was talking about quantum computing then. And then I

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Speaker 2: remember in about twenty sixteen I said to actually Arvin

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Speaker 2: was the director of research, then can we actually put

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Speaker 2: our quantum computer on the cloud? Well that's probably twenty fifteen,

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Speaker 2: and it was always supporting that. So as we've done

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Speaker 2: more and more we've been able to do it. It's

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Speaker 2: had this program going now, I agree, is very visible,

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Speaker 2: like because we're in this scaling phase and so we're

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Speaker 2: invested to keep scaling it and to get why is

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Speaker 2: At IBM research, what we always do is answer what

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Speaker 2: is the future of computing? Whether it's coming up with

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Speaker 2: new algorithms, coming up with better AI, coming up with quantum,

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Speaker 2: or coming up with how do different accelerators go together.

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Speaker 2: It's our DNA to answer the question of what is

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Speaker 2: the future?

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Speaker 1: Need a perfect problem for IBM because you kind of

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Speaker 1: need to have a legacy of building stuff, building actual

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Speaker 1: physical machines.

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Speaker 2: Yeah, it's why I came to IBM. I wanted the experience,

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Speaker 2: the culture of building hard things that others have not

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Speaker 2: done before.

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Speaker 1: Where do you imagine we are in the timeline of

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Speaker 1: this technology, it will come a point when it will mature.

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Speaker 1: My cell phone is a mature technology this point. How

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Speaker 1: far are we from that point with condom?

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Speaker 2: So I think there's various aspects of it. So we

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Speaker 2: sat in twentsand and seventy and we set our goal

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Speaker 2: that in twenty twenty three we would be able to

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Speaker 2: build a machine that was beyond classical computers to simulate it,

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Speaker 2: and we achieved that in twenty twenty three. So to

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Speaker 2: run a biggo we call it a quantum circule, the

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Speaker 2: details of a dimatic but to run a quantum workload

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Speaker 2: that if you were to simulate that workload how a

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Speaker 2: quantum computer operates on a classical computer, you couldn't do it.

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Speaker 2: So we said that as our first and now I've

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Speaker 2: made it publicly that by twenty twenty nine we'll build

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Speaker 2: the first fault tolerant quantum computer. That is, one that

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Speaker 2: can completely handle the noise to the level to allow

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Speaker 2: you to run a very very large, large problem.

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Speaker 1: So an example of a large problem.

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Speaker 2: Yeah, a large quantum problem. So for around a couple

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Speaker 2: of one hundred cubits and one hundred million operations, you're

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Speaker 2: talking still interesting science problems like simulating a molecule, or

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Speaker 2: calculating a small optimization problem, or calculating, say some part

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Speaker 2: of a matrix update in some type of differential. So

437
00:24:52,119 --> 00:24:55,119
Speaker 2: it'll still be scientific, but it'll be at the point

438
00:24:55,200 --> 00:25:01,000
Speaker 2: where it's beyond, well beyond any classical approximate method. And

439
00:25:01,040 --> 00:25:02,000
Speaker 2: then I think.

440
00:25:01,880 --> 00:25:02,920
Speaker 1: That's twenty twenty nine.

441
00:25:03,000 --> 00:25:05,480
Speaker 2: That's twenty twenty nine, So we're four.

442
00:25:05,359 --> 00:25:08,120
Speaker 1: Years away from something that can start to handle.

443
00:25:08,440 --> 00:25:12,360
Speaker 2: Interesting problem, serious problems. I do believe the scientists will

444
00:25:12,400 --> 00:25:15,800
Speaker 2: find interesting heuristic problems before that, and so over the

445
00:25:15,880 --> 00:25:18,360
Speaker 2: next four years, you're going to continue to see more

446
00:25:18,400 --> 00:25:22,920
Speaker 2: and more let's call them heuristic not provable quantum problems

447
00:25:22,960 --> 00:25:26,000
Speaker 2: that run on quantum computers that come out. We're seeing

448
00:25:26,119 --> 00:25:28,879
Speaker 2: more and more come from many of our partners and ourselves.

449
00:25:29,160 --> 00:25:31,960
Speaker 2: Heuristic problems have value, but they have to be tested,

450
00:25:32,040 --> 00:25:33,760
Speaker 2: they have to stand up over time. You have to

451
00:25:33,840 --> 00:25:36,240
Speaker 2: run them many, many times, you have to try different ones,

452
00:25:36,720 --> 00:25:39,760
Speaker 2: and many times heuristic can lead to formal problems. So

453
00:25:39,840 --> 00:25:41,920
Speaker 2: you're going to see, because we're beyond now the point

454
00:25:41,960 --> 00:25:46,280
Speaker 2: that you can simulate these quantum computers with any classical computer.

455
00:25:46,400 --> 00:25:49,840
Speaker 2: They're kind of like a scientific tool. So they're exploring

456
00:25:49,960 --> 00:25:50,520
Speaker 2: the heuristic.

457
00:25:50,760 --> 00:25:52,359
Speaker 1: What do you have to get done between now and

458
00:25:52,400 --> 00:25:53,760
Speaker 1: twenty twenty nine to get there?

459
00:25:54,160 --> 00:25:56,280
Speaker 2: So we had to reinvent how we wanted to do

460
00:25:56,440 --> 00:26:00,399
Speaker 2: error correction. So we have to demonstrate modules and if

461
00:26:00,440 --> 00:26:04,119
Speaker 2: we can demonstrate these error corrected module and our goal

462
00:26:04,240 --> 00:26:06,560
Speaker 2: is actually it's called Crooker Borrough. I name all our

463
00:26:06,600 --> 00:26:09,320
Speaker 2: chips after birds, so it's called Crooker Borrow is named

464
00:26:09,359 --> 00:26:12,000
Speaker 2: after an Australian birt. I think I still say Crooker

465
00:26:12,040 --> 00:26:16,520
Speaker 2: Borrow the way Australians do. We need to then show

466
00:26:16,560 --> 00:26:18,679
Speaker 2: that we can make a single module and then we

467
00:26:18,720 --> 00:26:21,119
Speaker 2: want to connect two of those modules together and I

468
00:26:21,160 --> 00:26:24,919
Speaker 2: call that one Cockatoo, which is another Australian vert. And

469
00:26:24,960 --> 00:26:27,680
Speaker 2: then if we can do that, so that's twenty six

470
00:26:28,200 --> 00:26:30,800
Speaker 2: and twenty seven, and then we want to scale them

471
00:26:31,000 --> 00:26:34,000
Speaker 2: scale those modules and that we call Starling, and we

472
00:26:34,040 --> 00:26:36,760
Speaker 2: want to scale that in twenty twenty nine. So get

473
00:26:36,800 --> 00:26:40,479
Speaker 2: a module, join two modules together, and scale and so

474
00:26:40,600 --> 00:26:42,960
Speaker 2: each module is going to be around one thousand cubits.

475
00:26:44,000 --> 00:26:46,520
Speaker 1: The challenge to getting there is it finding the right

476
00:26:46,760 --> 00:26:50,800
Speaker 1: material or how would you describe what that's The beauty.

477
00:26:50,520 --> 00:26:52,800
Speaker 2: Needs to be done That's the beauty of it is

478
00:26:53,760 --> 00:26:56,840
Speaker 2: if we would have been here two years ago, I

479
00:26:57,000 --> 00:26:59,600
Speaker 2: couldn't tell you how it would be done. So we

480
00:26:59,640 --> 00:27:02,600
Speaker 2: had a huge breakthrough. We came up with a new code,

481
00:27:03,119 --> 00:27:07,440
Speaker 2: a new quantumeric Russian code, and that code. The biggest

482
00:27:07,560 --> 00:27:10,080
Speaker 2: in part of that code that is the most important

483
00:27:10,359 --> 00:27:14,399
Speaker 2: is its modular in nature. So previous codes, without getting

484
00:27:14,440 --> 00:27:18,040
Speaker 2: too technical, they were very monolithic and you had to

485
00:27:18,040 --> 00:27:20,160
Speaker 2: build a very big device, and I wouldn't have known

486
00:27:20,520 --> 00:27:24,320
Speaker 2: we would have to invent tools like new simos tools

487
00:27:24,359 --> 00:27:28,080
Speaker 2: to do that. So we came up with this new code.

488
00:27:28,119 --> 00:27:31,320
Speaker 2: We started on twenty nineteen, we published in twenty twenty four.

489
00:27:31,359 --> 00:27:33,119
Speaker 2: We kind of had most of things worked out in

490
00:27:33,160 --> 00:27:35,960
Speaker 2: twenty twenty three. That's why we got confident to release

491
00:27:36,160 --> 00:27:38,800
Speaker 2: the thing. So the biggest breakthrough we had is coming

492
00:27:38,880 --> 00:27:41,600
Speaker 2: up with a code that's modular in nature. And think

493
00:27:41,640 --> 00:27:44,400
Speaker 2: of that as a like a blueprint. And so now

494
00:27:44,440 --> 00:27:48,560
Speaker 2: we have the blueprint, and now we're doing engineering tasks

495
00:27:49,000 --> 00:27:51,520
Speaker 2: to implement every part of that blueprint.

496
00:27:51,720 --> 00:27:54,879
Speaker 1: And so the minute you had that breakthrough, then you

497
00:27:54,960 --> 00:27:57,760
Speaker 1: began to have confidence at something exactly these goals could

498
00:27:57,760 --> 00:27:58,200
Speaker 1: be met.

499
00:27:58,359 --> 00:28:02,080
Speaker 2: And then you can't and then Anyone that's done engineering

500
00:28:02,320 --> 00:28:04,120
Speaker 2: will know what I'm talking about when I say this

501
00:28:04,240 --> 00:28:08,440
Speaker 2: is cycles are learning. It takes so long from test

502
00:28:08,480 --> 00:28:12,600
Speaker 2: idea to build two tests. In hardware, the cycles are

503
00:28:12,680 --> 00:28:15,000
Speaker 2: learning are much much lower than software, Like you can

504
00:28:15,040 --> 00:28:18,280
Speaker 2: be really really faster in the software. So then we've

505
00:28:18,359 --> 00:28:22,040
Speaker 2: planned out our iterations over the next few years, and

506
00:28:22,119 --> 00:28:26,320
Speaker 2: so we have to successfully demonstrate them. I may slip,

507
00:28:26,400 --> 00:28:31,159
Speaker 2: because sometimes you may estimate your time wrong, but we

508
00:28:31,280 --> 00:28:33,720
Speaker 2: now have exactly what we want to do for the

509
00:28:33,760 --> 00:28:34,480
Speaker 2: next four years.

510
00:28:34,600 --> 00:28:36,160
Speaker 1: I want to go back to that breakthrough for a moment.

511
00:28:36,320 --> 00:28:39,000
Speaker 1: What does the word breaks we mean in that context, Like,

512
00:28:39,280 --> 00:28:41,360
Speaker 1: it's not that you get a call in the morning

513
00:28:41,800 --> 00:28:44,280
Speaker 1: from somebody who says, I did it? Do you see

514
00:28:44,320 --> 00:28:46,480
Speaker 1: it coming? Or is it a surprise when they get there.

515
00:28:46,680 --> 00:28:50,320
Speaker 2: So the way this one worked is Sergo Brave, who's

516
00:28:50,840 --> 00:28:53,800
Speaker 2: an algorithm person at IBM, one of the smartest and

517
00:28:53,920 --> 00:28:54,720
Speaker 2: quantum information.

518
00:28:55,080 --> 00:28:59,000
Speaker 1: Don't mention his name. Everyone valid, you'll come for him.

519
00:28:59,040 --> 00:29:01,920
Speaker 2: Everyone in quantum already knows his name. I don't think

520
00:29:01,920 --> 00:29:05,400
Speaker 2: there's an idea that has not originated from him in

521
00:29:05,480 --> 00:29:10,120
Speaker 2: quantit So we're looking at other codes and we'll go

522
00:29:10,280 --> 00:29:14,719
Speaker 2: all right, we've got to get serious about these codes.

523
00:29:14,760 --> 00:29:17,840
Speaker 2: And others were starting to propose to bring these and

524
00:29:18,040 --> 00:29:22,400
Speaker 2: we call them LDPC codes from the classical space into

525
00:29:22,400 --> 00:29:25,720
Speaker 2: the quantum. And I asked him, we need to get

526
00:29:25,720 --> 00:29:28,560
Speaker 2: ahead of this and understand what they're doing it. He's like,

527
00:29:28,760 --> 00:29:32,640
Speaker 2: the most modest perfuse late, Jay, let me learn about them,

528
00:29:32,720 --> 00:29:35,840
Speaker 2: and I'll generate a report for us and we'll read

529
00:29:35,840 --> 00:29:38,880
Speaker 2: through it. And then I said, great, Then I don't know.

530
00:29:38,960 --> 00:29:41,320
Speaker 2: Six months later, he comes back with one hundred page

531
00:29:41,360 --> 00:29:45,320
Speaker 2: report on everyone. Everyone had done an LTPC codes. I'm like, awesome.

532
00:29:45,360 --> 00:29:48,840
Speaker 2: So I started then to read from them. And then

533
00:29:48,960 --> 00:29:51,959
Speaker 2: we said, all right, how do we under the assumptions

534
00:29:52,000 --> 00:29:54,920
Speaker 2: of the hardware we can build? Can we get an

535
00:29:55,080 --> 00:30:00,479
Speaker 2: LTPC code knowing what we can build? And he and

536
00:30:00,520 --> 00:30:03,080
Speaker 2: that's a great question. And so we put a small

537
00:30:03,120 --> 00:30:06,720
Speaker 2: team together to investigate and honestly took two to three years,

538
00:30:07,600 --> 00:30:12,120
Speaker 2: and we iterated and we used the constraints, so we

539
00:30:12,200 --> 00:30:14,680
Speaker 2: had the sort of theory and then we had the

540
00:30:14,720 --> 00:30:17,880
Speaker 2: constraints of what we could build, and we iterated for

541
00:30:17,920 --> 00:30:20,320
Speaker 2: a few years, and then at the end of that

542
00:30:20,400 --> 00:30:22,880
Speaker 2: we came out with a solution that yes, it is

543
00:30:22,960 --> 00:30:26,280
Speaker 2: possible to meet all the constraints of the hardware and

544
00:30:26,360 --> 00:30:27,920
Speaker 2: build a code that will work.

545
00:30:28,520 --> 00:30:32,320
Speaker 1: I'm just curious about So you had this task, this

546
00:30:32,440 --> 00:30:35,880
Speaker 1: problem you want to solve, and when you set out

547
00:30:35,880 --> 00:30:38,080
Speaker 1: on the task of trying to solve the problem, what's

548
00:30:38,120 --> 00:30:40,960
Speaker 1: your certainty level that you'll get a solution?

549
00:30:41,400 --> 00:30:45,160
Speaker 2: Well, that's the beauty of science. Four things. We kind

550
00:30:45,160 --> 00:30:48,840
Speaker 2: of have a few ideas. My philosophy is try a

551
00:30:48,840 --> 00:30:51,600
Speaker 2: few for the ones that need to be in that

552
00:30:51,920 --> 00:30:56,360
Speaker 2: like wow moment. It's honestly, you've got to set the

553
00:30:56,400 --> 00:31:00,719
Speaker 2: ambition really, really high, but know when to stop. It

554
00:31:00,760 --> 00:31:03,320
Speaker 2: was a great team that went together to get that breakthrough,

555
00:31:03,920 --> 00:31:05,959
Speaker 2: and we knew that we needed to come up with

556
00:31:05,960 --> 00:31:10,520
Speaker 2: a code that met the requients of the experiment. And

557
00:31:10,760 --> 00:31:14,480
Speaker 2: I think what was different before then is the theorists

558
00:31:14,480 --> 00:31:19,120
Speaker 2: that we're doing error correction codes didn't necessarily know the

559
00:31:19,160 --> 00:31:22,720
Speaker 2: constraints of experiments. So it was like, really more pen

560
00:31:22,800 --> 00:31:25,200
Speaker 2: and paper. So this became one, all right, given these

561
00:31:25,240 --> 00:31:27,760
Speaker 2: sets of constraints, is it possible?

562
00:31:28,280 --> 00:31:31,160
Speaker 1: When Las questions about this? Sorry, and I love these

563
00:31:31,240 --> 00:31:34,320
Speaker 1: kind of moments when things become clear. At the time

564
00:31:34,360 --> 00:31:37,800
Speaker 1: the problem was solved, were you aware of the implications

565
00:31:37,800 --> 00:31:41,160
Speaker 1: of the solution, or did that takes you knew exactly

566
00:31:41,160 --> 00:31:45,520
Speaker 1: what we set out exactly like, Either we were going

567
00:31:45,600 --> 00:31:47,640
Speaker 1: to have to work out how to cool down a

568
00:31:47,720 --> 00:31:51,280
Speaker 1: very large piece of silicon, which would require a lot

569
00:31:51,320 --> 00:31:54,440
Speaker 1: of engineering and building tools beyond what anyone has ever

570
00:31:54,480 --> 00:31:59,640
Speaker 1: built in the silicon semoss industry to implement the known codes,

571
00:32:00,280 --> 00:32:02,120
Speaker 1: or we had to come up with a different one.

572
00:32:02,400 --> 00:32:04,680
Speaker 2: Yeah, and once I knew that we had one that

573
00:32:05,240 --> 00:32:09,640
Speaker 2: I didn't need to reinvent any tools to build. The

574
00:32:09,680 --> 00:32:10,720
Speaker 2: implications are clear.

575
00:32:11,000 --> 00:32:13,880
Speaker 1: How much time elapsed between the time you heard the

576
00:32:13,920 --> 00:32:17,080
Speaker 1: problem was solved and the time you told Arvin Krishna,

577
00:32:17,120 --> 00:32:18,880
Speaker 1: the CEO, the problem was solved.

578
00:32:19,880 --> 00:32:22,360
Speaker 2: I'm sure the next time I spoke to him, I update,

579
00:32:22,400 --> 00:32:25,320
Speaker 2: but I don't remember. The beauty of Avin is he

580
00:32:25,400 --> 00:32:27,960
Speaker 2: trusts the scientists will do it, and so he doesn't

581
00:32:27,960 --> 00:32:30,800
Speaker 2: really check on us. We update him when it is

582
00:32:30,840 --> 00:32:33,960
Speaker 2: and he empowers us to do really hard problems.

583
00:32:34,160 --> 00:32:37,920
Speaker 1: Yeah, so let's talk about uses. I mean, they're really

584
00:32:38,000 --> 00:32:41,800
Speaker 1: like cool, big shiny machine I think you'll get by

585
00:32:41,840 --> 00:32:45,080
Speaker 1: twenty twenty nine. But there's all kinds of really interesting

586
00:32:45,120 --> 00:32:46,560
Speaker 1: problems you're already working on.

587
00:32:46,960 --> 00:32:52,200
Speaker 2: Yes, this is like another interesting area is I can

588
00:32:52,360 --> 00:32:56,240
Speaker 2: prove in pen and paper algorithms that we want to run.

589
00:32:56,320 --> 00:32:58,360
Speaker 2: That like, it's not that we don't know what to

590
00:32:58,400 --> 00:33:01,720
Speaker 2: do with a quantum Computerhundreds of algorithms. You can go

591
00:33:01,800 --> 00:33:04,400
Speaker 2: to I think it's called quantumzoo dot com and you

592
00:33:04,440 --> 00:33:07,560
Speaker 2: can see many, many algorithms. People are coming up with

593
00:33:07,600 --> 00:33:09,280
Speaker 2: more of more of them that they prove by pen

594
00:33:09,360 --> 00:33:13,920
Speaker 2: and paper. Imagine, now we have a machine that you

595
00:33:14,080 --> 00:33:20,160
Speaker 2: can't simulate, how do you actually discover algorithms in a

596
00:33:20,240 --> 00:33:24,560
Speaker 2: scientific way? How do you look and discover algorithms using

597
00:33:24,560 --> 00:33:28,000
Speaker 2: a quantum computer. We're in this exciting period right now.

598
00:33:28,560 --> 00:33:32,000
Speaker 2: And so even though I can prove these ones that

599
00:33:32,040 --> 00:33:34,640
Speaker 2: we can run in the future, there's a big white

600
00:33:34,680 --> 00:33:38,160
Speaker 2: space between what the machines we have and we're going

601
00:33:38,200 --> 00:33:41,920
Speaker 2: to build and continue to do and those ones that

602
00:33:41,960 --> 00:33:46,440
Speaker 2: want the provable ones. And I'm an optimistic person by nature.

603
00:33:46,920 --> 00:33:51,200
Speaker 2: I think getting those machines in the hands of students

604
00:33:51,240 --> 00:33:54,720
Speaker 2: to explore and look at heuristic algorithms, So looking at

605
00:33:54,800 --> 00:33:59,600
Speaker 2: the equivalent of doing numerical algorithms on computers, which there's

606
00:33:59,720 --> 00:34:04,640
Speaker 2: many histories of numerical algorithms being discovered on classical computers.

607
00:34:05,160 --> 00:34:08,400
Speaker 2: Before we had formal proofs that we rely on today,

608
00:34:08,600 --> 00:34:12,839
Speaker 2: people would even argue the way AI works was driven numerically,

609
00:34:13,120 --> 00:34:16,080
Speaker 2: even though we have input into it. There are ones

610
00:34:16,120 --> 00:34:20,640
Speaker 2: in optimization driven numerically. We are entering that phase, so

611
00:34:20,920 --> 00:34:25,200
Speaker 2: the computer scientists now need to go play with these primitives.

612
00:34:25,760 --> 00:34:29,600
Speaker 2: Our prediction is over the next couple of years we're

613
00:34:29,640 --> 00:34:34,480
Speaker 2: going to see valuable numerical equivalent algorithms emerge. And where

614
00:34:34,560 --> 00:34:37,520
Speaker 2: the scientists are going is in four categories. One is

615
00:34:37,520 --> 00:34:43,080
Speaker 2: simulating nature, so looking at either Hayanerji physics, chemistry, light problems.

616
00:34:43,640 --> 00:34:47,040
Speaker 2: As an example, with our partners in Japan, they took

617
00:34:47,400 --> 00:34:50,960
Speaker 2: one of our quantum computers and for Gackle, a very

618
00:34:51,200 --> 00:34:55,840
Speaker 2: large classical supercomputer, and they ran a problem where quantum

619
00:34:56,440 --> 00:34:59,400
Speaker 2: was just a sub routine of the problem that was

620
00:34:59,480 --> 00:35:01,480
Speaker 2: running on all of for Garco, and they were able

621
00:35:01,560 --> 00:35:04,480
Speaker 2: to look at an interesting molecule, a molecule that if

622
00:35:04,480 --> 00:35:06,319
Speaker 2: you would go by pen and paper you would have said,

623
00:35:06,360 --> 00:35:08,000
Speaker 2: it's going to take me a very long time to

624
00:35:08,080 --> 00:35:10,719
Speaker 2: run that. They were able to run that quite accurately,

625
00:35:10,800 --> 00:35:14,360
Speaker 2: heuristically and already get results that are comparable with the

626
00:35:14,360 --> 00:35:17,600
Speaker 2: best classical methods. So they are extremely excited because they

627
00:35:17,600 --> 00:35:19,880
Speaker 2: want to push that further, and they're sort of showing

628
00:35:19,880 --> 00:35:23,040
Speaker 2: that you can take a classical supercomputer with quantum as

629
00:35:23,040 --> 00:35:25,520
Speaker 2: a subroutine and start to push the level.

630
00:35:26,440 --> 00:35:28,760
Speaker 1: This was trying to solve a medical problem?

631
00:35:28,800 --> 00:35:33,160
Speaker 2: Is this one is a like most people don't realize,

632
00:35:33,200 --> 00:35:36,080
Speaker 2: like iron sulfur, just something as simple as iron and sulfur,

633
00:35:36,440 --> 00:35:41,040
Speaker 2: we can't solve that exactly, Like iron sulfur, molecules are

634
00:35:41,080 --> 00:35:45,399
Speaker 2: too hard. So really small, small molecules are really really hard,

635
00:35:45,480 --> 00:35:48,319
Speaker 2: too hard for classical computers to solve. People think we

636
00:35:48,360 --> 00:35:50,600
Speaker 2: can solve a lot of things. It actually turns out

637
00:35:50,600 --> 00:35:51,760
Speaker 2: we can't solve very much.

638
00:35:52,000 --> 00:35:55,400
Speaker 1: You say solved instance, you know precisely how that molecule

639
00:35:55,440 --> 00:35:57,040
Speaker 1: works and is constructed.

640
00:35:57,040 --> 00:36:01,120
Speaker 2: No precisely what the energy levels of that molecule is

641
00:36:01,200 --> 00:36:03,960
Speaker 2: and how they come together, and then be able to

642
00:36:03,960 --> 00:36:06,239
Speaker 2: do that on a classical computer and compare it to

643
00:36:06,280 --> 00:36:07,200
Speaker 2: a quantum.

644
00:36:06,880 --> 00:36:10,760
Speaker 1: It would be really really useful to know that specifically, because.

645
00:36:10,800 --> 00:36:13,840
Speaker 2: If you can have energy levels, then you can estimate

646
00:36:14,120 --> 00:36:17,359
Speaker 2: reaction rates. If you can estimate reaction rates, you can

647
00:36:17,360 --> 00:36:21,200
Speaker 2: see how different types of chemicals will react. That can

648
00:36:21,239 --> 00:36:24,920
Speaker 2: then lead to better informing eventually how to build materials

649
00:36:25,280 --> 00:36:27,600
Speaker 2: or even drug design. I just want to be careful

650
00:36:27,600 --> 00:36:29,680
Speaker 2: and not say, oh, we're going to solve drug design

651
00:36:29,719 --> 00:36:34,160
Speaker 2: or that, because there's many scientific steps to make that

652
00:36:34,760 --> 00:36:37,480
Speaker 2: so and so what quantum gives you as a different

653
00:36:37,520 --> 00:36:40,600
Speaker 2: tool to give you more accuracy and then lead to

654
00:36:40,680 --> 00:36:42,279
Speaker 2: making the different methods work.

655
00:36:42,880 --> 00:36:47,719
Speaker 1: You can subcontract out aspects of a problem quantum right now,

656
00:36:47,760 --> 00:36:50,719
Speaker 1: and that just gets you further along than you would

657
00:36:50,760 --> 00:36:50,960
Speaker 1: have been.

658
00:36:51,239 --> 00:36:54,960
Speaker 2: So at the moment, even this result still does not

659
00:36:55,080 --> 00:37:00,600
Speaker 2: beat the best approximate classical method. It's comparable. The art

660
00:37:00,840 --> 00:37:04,879
Speaker 2: of chemistry for the last hundred years has been about approximating.

661
00:37:05,480 --> 00:37:08,879
Speaker 2: So what we've done is we have got very very

662
00:37:08,920 --> 00:37:13,560
Speaker 2: good at coming up with ways of approximating nature. And

663
00:37:13,800 --> 00:37:15,640
Speaker 2: a lot of the things that we do and we

664
00:37:15,760 --> 00:37:19,120
Speaker 2: exploit and we use to estimate approximations. They don't a

665
00:37:19,160 --> 00:37:21,880
Speaker 2: stimulate nature of the way nature is. They approximate it.

666
00:37:22,280 --> 00:37:26,480
Speaker 2: And there's I could list many different acronyms of different

667
00:37:26,520 --> 00:37:31,080
Speaker 2: methods that go into approximating nature. What quantum gives us

668
00:37:31,200 --> 00:37:35,200
Speaker 2: is to eventually get beyond that approximation and do it

669
00:37:35,280 --> 00:37:38,600
Speaker 2: the way nature works and so we aren't beating those

670
00:37:38,640 --> 00:37:41,520
Speaker 2: approximation methods. And this is why I think, this is

671
00:37:41,520 --> 00:37:43,640
Speaker 2: why it's still in the science. But they're getting comparable.

672
00:37:44,000 --> 00:37:47,320
Speaker 2: Getting comparable with a new tool where the previous tool

673
00:37:47,480 --> 00:37:51,480
Speaker 2: is a dead end makes scientists very excited. Yeah, that

674
00:37:51,560 --> 00:37:54,400
Speaker 2: nuance is where it is, and so that's in machine learning.

675
00:37:54,520 --> 00:37:59,200
Speaker 2: Sorry Hamiltonian. Then there's examples in differential equations. So can

676
00:37:59,239 --> 00:38:02,200
Speaker 2: I actually come up with differential equations and solve them?

677
00:38:02,600 --> 00:38:04,400
Speaker 2: And if I can solve them, you could look at

678
00:38:04,760 --> 00:38:08,239
Speaker 2: things like an obvious Stokes equation goes into weather. There's

679
00:38:08,280 --> 00:38:12,680
Speaker 2: financial differential equations that you can better predict. So differential equations.

680
00:38:12,760 --> 00:38:15,600
Speaker 2: There's many different examples there. And then I would say

681
00:38:15,600 --> 00:38:19,200
Speaker 2: that two others are optimization, and then there's quantum versions

682
00:38:19,239 --> 00:38:21,919
Speaker 2: of machine learning that are very exciting as well.

683
00:38:22,600 --> 00:38:25,680
Speaker 1: Cleveland Clinic one of the organizations that you guys have

684
00:38:25,760 --> 00:38:28,319
Speaker 1: worked with. Why would the Cleveland Clinic be calling you.

685
00:38:28,360 --> 00:38:31,160
Speaker 2: Up because that problem that they want to look at.

686
00:38:31,640 --> 00:38:35,719
Speaker 2: So they've also done similar problem to the reacin lab.

687
00:38:35,840 --> 00:38:38,440
Speaker 2: So they've taken that method now and they've looked at

688
00:38:38,640 --> 00:38:43,520
Speaker 2: molecules that matter for drug design. So they're fundamentally looking

689
00:38:43,560 --> 00:38:48,000
Speaker 2: at those molecules that matter for eventually replacing some of

690
00:38:48,040 --> 00:38:51,920
Speaker 2: the steps. So they're investing to see how reliable it

691
00:38:52,000 --> 00:38:54,080
Speaker 2: can be done. And so there's a scientist there that's

692
00:38:54,120 --> 00:38:57,919
Speaker 2: done many iterations now using the techniques that were done

693
00:38:57,920 --> 00:39:01,879
Speaker 2: first with the team in Japan, replicated that for new

694
00:39:01,920 --> 00:39:07,279
Speaker 2: molecules that are essential primitives for eventually designing drugs and

695
00:39:07,360 --> 00:39:09,680
Speaker 2: things that may matter for medical Yeah.

696
00:39:09,760 --> 00:39:14,879
Speaker 1: And also there's some finance firms yep, HBC, Van Good, yep,

697
00:39:15,360 --> 00:39:16,600
Speaker 1: and their interest is what.

698
00:39:17,120 --> 00:39:20,919
Speaker 2: So that was the differential equation and optimization. So if

699
00:39:20,960 --> 00:39:25,600
Speaker 2: you are doing very large calculations like risk portfolio or

700
00:39:25,640 --> 00:39:28,160
Speaker 2: if you want to model the Black Staws equation or

701
00:39:28,200 --> 00:39:30,640
Speaker 2: things like this that are fundamental for them to make

702
00:39:30,719 --> 00:39:33,839
Speaker 2: better predictions, come up with better trades and things like this,

703
00:39:34,360 --> 00:39:38,640
Speaker 2: that is a very hard computational task. And so rather

704
00:39:38,680 --> 00:39:42,000
Speaker 2: than quantum replacing that whole problem, can quantum be a

705
00:39:42,040 --> 00:39:46,240
Speaker 2: subroutine in there? And what HSBC showed is they showed

706
00:39:46,280 --> 00:39:48,600
Speaker 2: they could take their real data, they could take their

707
00:39:48,640 --> 00:39:52,399
Speaker 2: real classical method and they just replaced a tiny part

708
00:39:52,400 --> 00:39:55,319
Speaker 2: of it. They replaced a tiny part of it with

709
00:39:55,360 --> 00:39:58,160
Speaker 2: a quantum subroutine that allowed them to come up with

710
00:39:58,239 --> 00:40:01,640
Speaker 2: better predictions of the weights that then when they were

711
00:40:01,680 --> 00:40:04,720
Speaker 2: to compare trial A versus Trial B, it was thirty

712
00:40:04,760 --> 00:40:08,799
Speaker 2: four percent better at predicting algorithmic tren And that's a

713
00:40:08,800 --> 00:40:09,760
Speaker 2: big deal for them.

714
00:40:10,080 --> 00:40:10,640
Speaker 1: It's huge.

715
00:40:10,760 --> 00:40:14,040
Speaker 2: Yes, Now do they need to do more trials? Do

716
00:40:14,120 --> 00:40:16,680
Speaker 2: they need to see is this a heuristic algorithm? Do

717
00:40:16,760 --> 00:40:19,759
Speaker 2: we need to be careful? Is there other classical algorithms

718
00:40:19,760 --> 00:40:22,040
Speaker 2: that go into these are great questions that are now

719
00:40:22,560 --> 00:40:26,879
Speaker 2: being investigated. So think of this period of heuristic algorithms

720
00:40:27,440 --> 00:40:31,160
Speaker 2: is really a period of scientific discovery using these machines,

721
00:40:32,160 --> 00:40:35,080
Speaker 2: knowing that we want to continue and build the ones

722
00:40:35,160 --> 00:40:38,680
Speaker 2: which have determinist their algorithms that can run.

723
00:40:39,520 --> 00:40:42,800
Speaker 1: Do the people who would profit the most by starting

724
00:40:42,840 --> 00:40:48,160
Speaker 1: to run quantum experiments realize that they would profit so

725
00:40:48,239 --> 00:40:51,600
Speaker 1: much from running quantum experience And does the world know this.

726
00:40:52,239 --> 00:40:56,040
Speaker 1: You've given us a couple of specific examples, but generally speaking,

727
00:40:56,080 --> 00:40:58,200
Speaker 1: there must be a very large universe of people who

728
00:40:58,239 --> 00:41:01,280
Speaker 1: could gain from at least starting to play in the space.

729
00:41:01,880 --> 00:41:06,319
Speaker 2: So the enterprises that use computation as key for their

730
00:41:06,360 --> 00:41:10,839
Speaker 2: survival understand the limits of classical computation and they're very

731
00:41:10,880 --> 00:41:15,719
Speaker 2: interested to get started. The universities are very interested. Could

732
00:41:15,800 --> 00:41:19,400
Speaker 2: we get more students doing more algorithms? One hundred percent?

733
00:41:20,360 --> 00:41:23,560
Speaker 2: Some of the limitations on the rate of algorithm discovery

734
00:41:23,600 --> 00:41:26,600
Speaker 2: is because people are thinking through the classical way of

735
00:41:26,680 --> 00:41:29,760
Speaker 2: writing algorithms. My belief is yes, so this is why

736
00:41:29,840 --> 00:41:32,000
Speaker 2: we want to get more and more students and things,

737
00:41:32,000 --> 00:41:35,040
Speaker 2: because it's just starting. But I would say in general,

738
00:41:35,120 --> 00:41:38,439
Speaker 2: most people are aware of it. Could we get more,

739
00:41:38,560 --> 00:41:39,759
Speaker 2: could we accelerate it?

740
00:41:40,000 --> 00:41:40,239
Speaker 1: Yes?

741
00:41:40,440 --> 00:41:42,560
Speaker 2: Do we need to make better hardware, Do we need

742
00:41:42,600 --> 00:41:45,120
Speaker 2: to come up with better libraries, yes, Do we need

743
00:41:45,160 --> 00:41:48,600
Speaker 2: better software yes, But it's all happening over the next

744
00:41:48,600 --> 00:41:49,240
Speaker 2: few years.

745
00:41:49,600 --> 00:41:51,799
Speaker 1: Is it hard to get someone who's spent their entire

746
00:41:51,840 --> 00:41:55,080
Speaker 1: life thinking in terms of solving problems to classical means

747
00:41:55,280 --> 00:41:57,600
Speaker 1: to make the transition to this new paradigm.

748
00:41:57,880 --> 00:42:00,680
Speaker 2: There's a lot of examples when you approach something with

749
00:42:00,800 --> 00:42:04,200
Speaker 2: the classical intuition, it's not the right way to do

750
00:42:04,239 --> 00:42:07,359
Speaker 2: it when you approach it through the quantum. But if

751
00:42:07,440 --> 00:42:11,719
Speaker 2: people are being taught to understand the fundamentals of the math,

752
00:42:12,239 --> 00:42:15,840
Speaker 2: then a lot of the techniques carry across. I don't

753
00:42:15,880 --> 00:42:19,720
Speaker 2: recommend people need to learn about entanglement or supersition, because

754
00:42:20,560 --> 00:42:24,800
Speaker 2: whilst the physicists will argue like spooky action a distance

755
00:42:24,840 --> 00:42:28,000
Speaker 2: and all these type of things, entanglement is the power. Yes,

756
00:42:28,120 --> 00:42:31,319
Speaker 2: that's how physicists are labeled. How quantum is different. But

757
00:42:31,400 --> 00:42:34,800
Speaker 2: I would say, do we need some physicists really worrying

758
00:42:34,960 --> 00:42:38,200
Speaker 2: thinking about that? Yes, but we need more applied mathematicians

759
00:42:38,200 --> 00:42:41,480
Speaker 2: that are realizing they can use this as a as

760
00:42:41,520 --> 00:42:43,200
Speaker 2: a different way of looking at the problems.

761
00:42:43,360 --> 00:42:46,120
Speaker 1: Yeah. When I asked you one question, know is we're

762
00:42:46,120 --> 00:42:49,480
Speaker 1: describing a a It's more than a new technology. We're

763
00:42:49,520 --> 00:42:52,680
Speaker 1: talking about a new paradigm. It's a way of thinking

764
00:42:52,719 --> 00:42:56,960
Speaker 1: about problems. Can you compare this to kind of previous

765
00:42:57,400 --> 00:43:00,759
Speaker 1: technological paradigms? If I'm looking at the last a couple

766
00:43:00,880 --> 00:43:03,319
Speaker 1: hundred years, what does this rank in terms of a

767
00:43:03,400 --> 00:43:06,000
Speaker 1: new field that we've opened up.

768
00:43:06,600 --> 00:43:08,719
Speaker 2: It's a hard question to answer, but I often say

769
00:43:08,880 --> 00:43:12,120
Speaker 2: the history of computing, this will be the first time

770
00:43:12,719 --> 00:43:17,400
Speaker 2: that computation has branched between classical and quantum. I like

771
00:43:17,760 --> 00:43:21,319
Speaker 2: thinking reading a lot in the past. One of the

772
00:43:21,320 --> 00:43:24,799
Speaker 2: things that I think was a way we changed as

773
00:43:24,800 --> 00:43:29,080
Speaker 2: a society was the invention of zero. Before zero math

774
00:43:29,320 --> 00:43:33,880
Speaker 2: was limited. Realizing that numbers have a number A zero

775
00:43:34,400 --> 00:43:37,240
Speaker 2: allowed us to develop a whole set of new mathematics

776
00:43:37,640 --> 00:43:41,840
Speaker 2: that then went on and defined like everything from waves

777
00:43:41,880 --> 00:43:45,680
Speaker 2: to calculus to all of that. Yes, we can describe

778
00:43:45,719 --> 00:43:48,120
Speaker 2: it with that same math, but when we describe it

779
00:43:48,120 --> 00:43:51,359
Speaker 2: with that math, it gets exponentially big and gets impractical

780
00:43:51,440 --> 00:43:54,480
Speaker 2: to do. Now we can actually work on it. I

781
00:43:54,480 --> 00:43:57,040
Speaker 2: would say, if I had to give you a quick answer,

782
00:43:57,160 --> 00:43:59,960
Speaker 2: maybe going all the way back to when we were

783
00:44:00,120 --> 00:44:02,000
Speaker 2: well accepted zero, I.

784
00:44:01,920 --> 00:44:03,600
Speaker 1: Thought you were going to say, like the airplane, but

785
00:44:03,719 --> 00:44:06,799
Speaker 1: in fact you went several orders of magnitude beyond that.

786
00:44:07,080 --> 00:44:10,319
Speaker 2: Yes, but I think it's sort of fundamental.

787
00:44:10,640 --> 00:44:14,040
Speaker 1: This is absolutely fascinating. Thank you so much for chatting

788
00:44:14,120 --> 00:44:14,600
Speaker 1: with me about it.

789
00:44:14,680 --> 00:44:15,319
Speaker 2: Thank you for your time.

790
00:44:17,800 --> 00:44:20,960
Speaker 1: Hey, listeners. So normally we'd end this episode here, but

791
00:44:21,000 --> 00:44:24,320
Speaker 1: the Tech Week attendees asked Jay some really great questions,

792
00:44:24,760 --> 00:44:27,560
Speaker 1: questions I wish I'd asked, so he wanted to include

793
00:44:27,560 --> 00:44:29,200
Speaker 1: those here. Enjoy.

794
00:44:31,000 --> 00:44:33,279
Speaker 3: Hi, J, thank you so much for the great presentation.

795
00:44:33,760 --> 00:44:37,360
Speaker 3: My name is Trixie Apiado. I work for Willis Towers Watson,

796
00:44:37,400 --> 00:44:41,359
Speaker 3: an insurance broker. I help seisos identify and quantify their

797
00:44:41,400 --> 00:44:45,400
Speaker 3: cyber risk so they can prepare for threats before they happen,

798
00:44:45,840 --> 00:44:48,520
Speaker 3: and so quantum threats keep me up at night. You

799
00:44:48,640 --> 00:44:52,640
Speaker 3: mentioned so many good problems that quantum can solve. It

800
00:44:52,680 --> 00:44:57,359
Speaker 3: can also break encryptions in our classical computer systems. So

801
00:44:58,480 --> 00:45:02,600
Speaker 3: what safeguards or policies do you implement in your teams

802
00:45:02,960 --> 00:45:07,440
Speaker 3: to build quantum capabilities responsibly and what can we do

803
00:45:08,200 --> 00:45:11,560
Speaker 3: for people in this room as builders and users to

804
00:45:11,680 --> 00:45:16,120
Speaker 3: secure our data in systems before quantum computers become more

805
00:45:16,200 --> 00:45:18,480
Speaker 3: energy efficient, cheaper, and more available.

806
00:45:19,360 --> 00:45:22,640
Speaker 2: So it's a great question. So yes, one of the

807
00:45:22,680 --> 00:45:27,040
Speaker 2: algorithms for quantum computing is to break out traditional encryption.

808
00:45:27,760 --> 00:45:31,600
Speaker 2: So at IBM Research, we were aware of this from

809
00:45:31,719 --> 00:45:36,160
Speaker 2: day one. We've come up with algorithms that we believe

810
00:45:36,360 --> 00:45:39,239
Speaker 2: and have very strong evidence will not be broken by

811
00:45:39,320 --> 00:45:43,200
Speaker 2: a quantum or classical computer, and has selected them. So

812
00:45:44,320 --> 00:45:48,680
Speaker 2: first the scientific technical question, security is saved. There are

813
00:45:48,760 --> 00:45:53,600
Speaker 2: algorithms that exist that we can implement that neither a

814
00:45:53,719 --> 00:45:57,840
Speaker 2: quantum or classical computer can break. So the technical answer

815
00:45:57,960 --> 00:46:01,840
Speaker 2: is we're all okay. The more our complicated answer is

816
00:46:01,880 --> 00:46:06,480
Speaker 2: a social and society answer. Encryption was built in classical

817
00:46:06,520 --> 00:46:10,040
Speaker 2: computing in a way that was never thought of being upgraded.

818
00:46:10,880 --> 00:46:14,920
Speaker 2: It's mixed everywhere. Some of it is downstream, some of

819
00:46:14,960 --> 00:46:17,560
Speaker 2: it is like software that you may use, some of

820
00:46:17,600 --> 00:46:21,840
Speaker 2: it is software that you've developed. And I get that

821
00:46:21,960 --> 00:46:23,799
Speaker 2: if you've got a product and you want to have

822
00:46:23,920 --> 00:46:26,879
Speaker 2: it secure for the next ten years, you probably want

823
00:46:26,880 --> 00:46:29,359
Speaker 2: to think about how you're going to upgrade it. Or

824
00:46:29,520 --> 00:46:32,719
Speaker 2: if you have data that needs to be secure for

825
00:46:32,920 --> 00:46:36,200
Speaker 2: the next ten years, it needs to upgrade to new encryption.

826
00:46:36,840 --> 00:46:39,880
Speaker 2: So the real challenge is more of a social business

827
00:46:39,920 --> 00:46:44,400
Speaker 2: problem of how do we actually transition from old encryption

828
00:46:44,520 --> 00:46:47,640
Speaker 2: to new encryption knowing this is going to happen. So

829
00:46:47,760 --> 00:46:50,279
Speaker 2: we at IBM have been very proactive on this. We've

830
00:46:50,320 --> 00:46:53,720
Speaker 2: developed tools where we can determine where encryption is used,

831
00:46:54,120 --> 00:46:58,120
Speaker 2: We've developed tools which can show you how to replace it,

832
00:46:58,280 --> 00:47:03,200
Speaker 2: and we early on have the Mainframe when we made

833
00:47:03,239 --> 00:47:06,239
Speaker 2: these algorithms. So I think it was Z sixteen that

834
00:47:06,400 --> 00:47:09,200
Speaker 2: was the first version of the Mainframe to have these

835
00:47:09,280 --> 00:47:14,480
Speaker 2: quantum safe algorithms implemented. So my answer to your question is, yes,

836
00:47:14,640 --> 00:47:18,320
Speaker 2: there's a real problem, but it's not a technical problem.

837
00:47:18,600 --> 00:47:21,200
Speaker 2: It's a social and business problem, and I'm not minimizing that.

838
00:47:21,360 --> 00:47:25,080
Speaker 2: I understand that that is a lot of work. You

839
00:47:25,200 --> 00:47:27,640
Speaker 2: need to start now. You need to come up and

840
00:47:27,680 --> 00:47:29,640
Speaker 2: do a you need to make it part of your

841
00:47:29,640 --> 00:47:34,440
Speaker 2: IT transformation. You need to get onto it. And I realize,

842
00:47:35,880 --> 00:47:38,640
Speaker 2: I realize it's not going to take zero time because

843
00:47:38,680 --> 00:47:41,640
Speaker 2: it's not an easy problem to do. So the short

844
00:47:41,680 --> 00:47:44,759
Speaker 2: answer is one we developed algorithms that we can't and

845
00:47:44,800 --> 00:47:47,160
Speaker 2: we're developing tools to help you in that transformation.

846
00:47:47,640 --> 00:47:51,080
Speaker 4: Thank you so much, Thank you. My name is Emma.

847
00:47:51,200 --> 00:47:54,799
Speaker 4: I'm a product manager at Expedia working on software side

848
00:47:54,800 --> 00:47:58,000
Speaker 4: of things. My question is around the non technical roles

849
00:47:58,120 --> 00:48:01,879
Speaker 4: outside of the researchers, the mathmatair, the builders, How can

850
00:48:01,960 --> 00:48:05,200
Speaker 4: the rest of us, whether it be policymakers, those in

851
00:48:05,200 --> 00:48:08,600
Speaker 4: the legal fields, those thinking about what use cases quantum

852
00:48:08,680 --> 00:48:10,960
Speaker 4: can solve for in the future, What should we be

853
00:48:11,040 --> 00:48:13,399
Speaker 4: thinking about and how can we prepare for that.

854
00:48:13,719 --> 00:48:15,839
Speaker 2: It's a good question. I think this is part of

855
00:48:15,920 --> 00:48:19,799
Speaker 2: the requirement of the scientists to being able to articulate

856
00:48:20,480 --> 00:48:23,080
Speaker 2: where they are. We need a forum for those type

857
00:48:23,120 --> 00:48:26,160
Speaker 2: of discussions. I think a lot of this can fit

858
00:48:26,280 --> 00:48:29,560
Speaker 2: within the forums that we already have for classical in AI,

859
00:48:30,160 --> 00:48:32,160
Speaker 2: and I think we need to just be asking how

860
00:48:32,200 --> 00:48:35,280
Speaker 2: do we actually bring them into them because I don't

861
00:48:35,480 --> 00:48:38,680
Speaker 2: think of quantum as a replacement of compute. I think

862
00:48:38,680 --> 00:48:41,680
Speaker 2: of it as an accelerator that expands what is possible.

863
00:48:42,360 --> 00:48:45,120
Speaker 2: And I think we can ask those questions in those forums.

864
00:48:45,520 --> 00:48:48,000
Speaker 2: Are we doing enough now? I think I agree with you. No,

865
00:48:48,280 --> 00:48:49,560
Speaker 2: I don't know the answer to it.

866
00:48:50,440 --> 00:48:53,720
Speaker 4: I think it's a really interesting perspective because those existing

867
00:48:53,760 --> 00:48:57,200
Speaker 4: forums do start to bring in those other fields as well,

868
00:48:57,480 --> 00:48:59,880
Speaker 4: so it could warrant the same sort of discussion.

869
00:49:00,080 --> 00:49:06,240
Speaker 2: And yeah, and I understand those forums right now, AI

870
00:49:06,360 --> 00:49:09,560
Speaker 2: is probably dominating and it should be like we are

871
00:49:09,600 --> 00:49:13,960
Speaker 2: going through a period of time where AI is impacting society.

872
00:49:14,520 --> 00:49:17,680
Speaker 2: The technology is impacting society in big ways. So I

873
00:49:17,760 --> 00:49:21,360
Speaker 2: totally understand that most of their focus should be on AI,

874
00:49:21,600 --> 00:49:24,200
Speaker 2: but we should start to ask where is quantum in

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00:49:24,640 --> 00:49:25,319
Speaker 2: that as well.

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Speaker 5: I'm Gobi and I'm a graduating PhD student at Northwestern

877
00:49:31,239 --> 00:49:34,279
Speaker 5: and also a member of south Park Commons, which is

878
00:49:34,320 --> 00:49:37,920
Speaker 5: a fund here. You mentioned earlier that some problems are

879
00:49:37,920 --> 00:49:41,080
Speaker 5: best solved by classical versus some problems are best solved

880
00:49:41,080 --> 00:49:43,440
Speaker 5: by quantum. When we're thinking about this, if we're not

881
00:49:43,520 --> 00:49:45,840
Speaker 5: experts in quantum, but we're thinking about this from an

882
00:49:45,880 --> 00:49:49,200
Speaker 5: AI perspective, Could you just clarify when we think about quantum,

883
00:49:49,560 --> 00:49:52,640
Speaker 5: what is deterministic and what is not deterministic.

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Speaker 2: I think the future of computing we've got to get

885
00:49:55,680 --> 00:49:59,040
Speaker 2: our heads around is that not everything is deterministic, and

886
00:49:59,080 --> 00:50:01,640
Speaker 2: it's much more going to be probilistic. How do you

887
00:50:01,760 --> 00:50:05,000
Speaker 2: handle error bars? How do you put confidence? I think

888
00:50:05,040 --> 00:50:07,960
Speaker 2: a lot of those questions which you're referring to in

889
00:50:08,040 --> 00:50:11,720
Speaker 2: AI are going to completely apply in quantum. I actually

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00:50:11,760 --> 00:50:17,319
Speaker 2: think it's a mistake to compare AI verse quantum. I

891
00:50:17,400 --> 00:50:22,000
Speaker 2: actually think of quantum as much. It's quantum verse classical compute,

892
00:50:22,040 --> 00:50:25,160
Speaker 2: and AI is going to come across on top. So

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00:50:25,360 --> 00:50:27,920
Speaker 2: as we go forward and we get a better understanding,

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00:50:28,239 --> 00:50:31,720
Speaker 2: I'm not going to say quantum is going to replace

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00:50:31,760 --> 00:50:34,759
Speaker 2: the classical compute that enables AI, but I think some

896
00:50:34,840 --> 00:50:37,080
Speaker 2: of the math you do in AI will be able

897
00:50:37,080 --> 00:50:39,839
Speaker 2: to go to both. So what can we formally prove?

898
00:50:40,640 --> 00:50:43,560
Speaker 2: I can come up with a problem where I take

899
00:50:43,600 --> 00:50:46,000
Speaker 2: a circle and I color half of it red and

900
00:50:46,080 --> 00:50:48,880
Speaker 2: half of it of blue, and then I say I'm going

901
00:50:48,960 --> 00:50:52,920
Speaker 2: to apply an operation that takes those dots make it. Say,

902
00:50:53,000 --> 00:50:55,359
Speaker 2: let's say ten dots over here red, ten dots over

903
00:50:55,400 --> 00:50:58,120
Speaker 2: here blue, and I'm going to wind them around many

904
00:50:58,120 --> 00:51:01,160
Speaker 2: many times. I can and then show you that if

905
00:51:01,160 --> 00:51:03,840
Speaker 2: you feed that into a classical computer, it's a classical

906
00:51:03,960 --> 00:51:07,720
Speaker 2: random number generator. You can give yourself as much data

907
00:51:07,760 --> 00:51:10,279
Speaker 2: as you want. You will never be able to say

908
00:51:10,320 --> 00:51:12,920
Speaker 2: did the red come from the left side or the

909
00:51:13,000 --> 00:51:16,120
Speaker 2: right side. You would take infinite data like it is

910
00:51:16,320 --> 00:51:19,120
Speaker 2: like you would have to break a classical random number generator.

911
00:51:19,880 --> 00:51:22,319
Speaker 2: I can show you a quantum algorithm that can do

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00:51:22,400 --> 00:51:26,760
Speaker 2: that deterministically. So where we're thinking is when the data

913
00:51:26,840 --> 00:51:32,000
Speaker 2: appears to be completely unstructured or you looks essentially like

914
00:51:32,080 --> 00:51:36,160
Speaker 2: a complete random number to the classical methods, there are

915
00:51:36,239 --> 00:51:39,800
Speaker 2: quantum methods that can actually potentially find that structure.

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Speaker 1: That's it for this episode of Smart Talks with IBM.

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Speaker 1: If you haven't already, be sure to check out my

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00:51:49,480 --> 00:51:54,880
Speaker 1: conversation with IBM Chairman and CEO Arvind Krishna, and stay tuned.

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Speaker 1: Another episode is coming soon. Smart Talks with ib is

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00:52:00,320 --> 00:52:04,480
Speaker 1: produced by Matt Ramano, Amy Gains McQuaid, Trina Menino, and

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00:52:04,560 --> 00:52:09,240
Speaker 1: Jake Harper. Engineering by Nina Bird Lawrence, Mastering by Sarah Buger.

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00:52:09,600 --> 00:52:14,719
Speaker 1: Music by Gramoscope Strategy by Tatiana Lieberman, Cassidy Meyer and

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00:52:14,840 --> 00:52:19,000
Speaker 1: Sofia Derlon. Smart Talks with IBM is a production of

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00:52:19,080 --> 00:52:24,319
Speaker 1: Pushkin Industries and Ruby Studio at iHeartMedia. To find more

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00:52:24,320 --> 00:52:29,080
Speaker 1: Pushkin podcasts, listen on the iHeartRadio app, Apple Podcasts, or

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00:52:29,120 --> 00:52:33,600
Speaker 1: wherever you listen to podcasts. I'm Malcolm Godwell. This is

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00:52:33,640 --> 00:52:38,120
Speaker 1: a paid advertisement from IBM. The conversations on this podcast

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00:52:38,400 --> 00:52:48,640
Speaker 1: don't necessarily represent IBM's positions, strategies, or opinions.