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Speaker 1: Hello, Hello. This is Smart Talks with IBM, a podcast

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Speaker 1: from Pushkin Industries, High Heart Media and IBM about what

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Speaker 1: it means to look at today's most challenging problems in

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Speaker 1: a new way. I'm Malcolm Gladwell. In this episode, I'll

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Speaker 1: be discussing the capabilities of quantum computing with Dr Dario

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Speaker 1: gil Dr Gill is the senior Vice president and director

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Speaker 1: of IBM Research. He's also recognized globally as one of

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Speaker 1: the brightest minds in the quantum computing industry. But what

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Speaker 1: we know is that is theoretically sound and possible, and

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Speaker 1: that we are making very significant progress towards achieving good goal.

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Speaker 1: And that's why this is a quest of doing something

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Speaker 1: that has never been done before, but it is definitely possible.

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Speaker 1: Earlier this year, at the Wall Street Journals Virtual CIO

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Speaker 1: Network Summit, Dr Gil pro claimed that the next ten

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Speaker 1: years will be the decade in which quantum really comes

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Speaker 1: of age. So what is quantum computing and how will

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Speaker 1: it transform over the next decade and in what ways

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Speaker 1: will quantum computers change the way we interact with technology.

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Speaker 1: Let's dive in. Thanks for joining us. Start to kill.

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Speaker 1: It's a real pleasure thank you for having me, Malcolm,

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Speaker 1: it's a pleasure to be with you. I wanted to

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Speaker 1: start my talent us a little bit about yourself. You

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Speaker 1: did your graduate work at m I T. What did

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Speaker 1: you study there? I studied nanotechnology at m i T.

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Speaker 1: I joined the Nanostructure's laboratory. It was in the Department

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Speaker 1: of Electrical Engineering and Computer Science and their professor Hank Smith,

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Speaker 1: who was one of the pioneers of the field of

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Speaker 1: nano fabrication. You know, how do you manipulate and build

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Speaker 1: incredibly small part of our world? And so that sets

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Speaker 1: you up for the world that you're in now. Right

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Speaker 1: this the quantum computing flows naturally out of the idea

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Speaker 1: that this plenty of room at the bottom. Yes, it

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Speaker 1: is because of the different theories of physics. One that

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Speaker 1: is of course extraordinarily irrelevant for the world of the

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Speaker 1: small is quantum physics. So if we are to understand

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Speaker 1: how matter behaves at the atomic scale and electronic structure

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Speaker 1: and the interactions and uh what occurs at the level

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Speaker 1: of materials, you have to understand what is happening in

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Speaker 1: the world of quantum physics. For those who are not

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Speaker 1: from a technical background, can you give us the give

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Speaker 1: me this the simplest definition of what quantum computing is.

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Speaker 1: You know, we're all accustomed to using computers today, and

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Speaker 1: at the foundation of the computers we use every day

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Speaker 1: and our smartphones is the idea of bits or binary digits.

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Speaker 1: And interestingly, this is an idea that the fascination that

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Speaker 1: all the complexity in the world we could reuse it

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Speaker 1: in a mode of communication, which is zeros and ones

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Speaker 1: dates back as far back as Lightnings, but it was

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Speaker 1: really in the twenty century. In the nineteen fourties and fifties,

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Speaker 1: a Cloud Shannon, which is one of the great leaders

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Speaker 1: in the world of computing, told us we could create

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Speaker 1: these incredibly sophisticated modes of communication and computation just by

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Speaker 1: being able to map all the complexity and information in

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Speaker 1: the world to strings of zeros and ones, and computers

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Speaker 1: are machines that manipulate zeros and ones very very efficient.

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Speaker 1: So in quantum computing it actually revisits that idea. It

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Speaker 1: turns out that the most fundamental building block of computation

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Speaker 1: is not the zero on one, not the bit. That's

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Speaker 1: something known as the cubit sure for quantum bit, and

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Speaker 1: at its heart it melts that idea ye of information

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Speaker 1: with the idea of physics. So what quantum computers do

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Speaker 1: is they manipulate information, exploiting the loss of quantum physics

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Speaker 1: to be able to do calculations that are simply impossible

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Speaker 1: to do if you just use the binary digit the

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Speaker 1: zeros and ones. It's a richer way to represent information

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Speaker 1: and manipulate information by exploiting the properties of quantum mechanics

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Speaker 1: to do things are impossible classically. Yeah, would you say

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Speaker 1: you can tackle problems now that would be impossible mechanically.

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Speaker 1: What is that? Could you represent the difference in capacity

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Speaker 1: of these two ways of computing, like how how much

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Speaker 1: of a gap is here between quantum and conventional computer?

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Speaker 1: You know, and it's full potential the gaps it's an

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Speaker 1: exponential So let me let me explain what I mean

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Speaker 1: by that. If you want to simulate nature, so let's

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Speaker 1: say very practical that you want to build a better

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Speaker 1: battery technology for electric cons right, so those are based

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Speaker 1: on lithium chemistry. And if you want to say, build

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Speaker 1: a battery that is longer lasting or safer, contract faster.

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Speaker 1: So now you have in front of your material science

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Speaker 1: problems and what you can do is go through the

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Speaker 1: periodic table and see all the different elements and figure

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Speaker 1: out how you are going to combine them to create

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Speaker 1: the material that has the properties you like. Okay, so

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Speaker 1: how can you go about doing that? Well? One approach

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Speaker 1: is to, uh, do it empirically, just try and humans

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Speaker 1: have been doing that since time me memorial right, combine

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Speaker 1: elements and see hy works. Another methodological approach is if

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Speaker 1: you have a theory of how things work, you could

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Speaker 1: try to solve the problem long form and see if

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Speaker 1: you can have a close form solution to the problem.

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Speaker 1: And a third angle that really came about with the

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Speaker 1: advent of computers is you could simulate it. Right. You

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Speaker 1: could use computers to mimic how atoms behave and use

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Speaker 1: those equation issues and try to do the calculations to

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Speaker 1: see what the properties would be. What's the problem. The

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Speaker 1: problem is that no matter how big computers we use today,

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Speaker 1: the number of variables that we have to compute over

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Speaker 1: is roughly correlated to the number of electrons and electron

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Speaker 1: orbitals present in those elements. So the more sophisticated and

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Speaker 1: material we've gotta make, the more interactions between these electrons

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Speaker 1: we gotta be able to calculate, and that number grows exponentially,

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Speaker 1: you know, pretty soon we need to have a computer,

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Speaker 1: you know, with more components than there are atoms in

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Speaker 1: the universe. Right, so it's impractical. So what do we do.

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Speaker 1: We approximate place, and when we approximate, we don't get

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Speaker 1: the right answer. So we are in this stock loop

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Speaker 1: of rate of progress. What is interesting on quantum is

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Speaker 1: that for modeling those kinds of problems, instead of having

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Speaker 1: an exponential meaning the more electrons we add, you know,

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Speaker 1: the number of calculations blowing up. Now it's a relation

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Speaker 1: that looks more like linear, meaning I only need one

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Speaker 1: more cubit roughly speaking, to model another electron. So even

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Speaker 1: even have a complex molecule where I need you know,

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Speaker 1: dozens or hundreds of of orbital calculations and I need

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Speaker 1: to do I would need a machine with dozens of

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Speaker 1: hundreds of cubits rather than a classical machine with ten

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Speaker 1: trillion transistors. Right that we don't know how to make.

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Speaker 1: It's not an extension or a derivative from the kind

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Speaker 1: of computers that we've been using. It's an entirely new

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Speaker 1: class of computers. That's exactly right, And that is what's

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Speaker 1: so interesting. So there will be classical computing and quantum computing.

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Speaker 1: That's how important this is, right, that you phrased it

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Speaker 1: very nicely, which is not just another evolution. Is we've

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Speaker 1: actually left no element of the assumption of the current

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Speaker 1: information on computational model as sacred. Right, not even the

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Speaker 1: bit has survived the quantum information view of the world. Right,

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Speaker 1: the very foundation had to be revisited. So where are

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Speaker 1: we How close are we having quantum computers? Actually that

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Speaker 1: you you describe that that task kept trying to figure

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Speaker 1: out how to make a better battery. When do you

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Speaker 1: think we'll be able to use quantum computers for a

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Speaker 1: task like that? We already have built quantum computers. Actually,

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Speaker 1: Iban was the first company in the world in two

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Speaker 1: thousand and sixteen to build a small quantum computer and

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Speaker 1: making universally available. So the first part of the answer

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Speaker 1: is like, we already have quantum computers, so you can

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Speaker 1: learn how to program them. You can start mapping problems

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Speaker 1: around how you do it. The challenge we have is

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Speaker 1: that very difficult to build these machines, so we have

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Speaker 1: not yet crossed the path where they can do things

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Speaker 1: that are of practical value that or classical machines cannot.

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Speaker 1: So we gotta keep an eye now of when that

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Speaker 1: crossover is going to happen, and that is something that

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Speaker 1: is this tier of information and computation that would likely

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Speaker 1: happen in the next few years, and then that begins,

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Speaker 1: you know, a whole a whole new space right of opportunity. Yeah,

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Speaker 1: you had said earlier with the stage now where the

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Speaker 1: machines make errors, what's the source of the difficulty at

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Speaker 1: the moment. Yeah, then you can build these machines that

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Speaker 1: have special properties to represent information in unique ways that

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Speaker 1: gives them exponential power compared to classical machines. The massines

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Speaker 1: are subject to errors, but there's both a theory and

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Speaker 1: a way to implement an error correction technique that would

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Speaker 1: allow us to compute in definitely and with like minimum

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Speaker 1: levels of errors. That's that it would be a practical value.

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Speaker 1: But realizing that large scale machine is still a significant

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Speaker 1: journey with a lot of scientific and engineering breakthroughs need

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Speaker 1: to occur. But what we know is that it's theoretically

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Speaker 1: sound and possible, and that we are making very significant

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Speaker 1: progress towards achieving the goal. And that's why this is

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Speaker 1: a quest of doing something that has never been done before,

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Speaker 1: but it is definitely possible. Yeah, you began with the

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Speaker 1: example of the electric battery. Give me another example of

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Speaker 1: a of an industry or a problem which would be

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Speaker 1: well served by using a quantum computer. These three categories

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Speaker 1: that quantum is going to make a difference similar in nature.

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Speaker 1: The world of mathematics, linear algebra that matters to maschine

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Speaker 1: learning and other problems. And the third is world of

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Speaker 1: search and graphs and what you can do with them

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Speaker 1: that matter a great deal. But I want to give

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Speaker 1: an example that's very famous of the consequences of quantum computing,

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Speaker 1: which is some of the implications that it will have

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Speaker 1: for cybersecurity and for security in general. And this came

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Speaker 1: from a very very famous algorithm that gave re energizing

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Speaker 1: of the field of quantum in the ninety nineties called

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Speaker 1: Short's algorithm, and it came from Peter Short, who was

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Speaker 1: then at bad LAPS and now is a professor at

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Speaker 1: m I T. And he published an algorithm that took

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Speaker 1: a problem that has to do with factoring. So basically,

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Speaker 1: the problem is if you take two prime numbers that

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Speaker 1: are say large, and you multiply those two prime numbers together,

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Speaker 1: and you get the product, the final number of the

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Speaker 1: product of those of those two If doing the multiplication,

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Speaker 1: it's very easy to do. Anybody can do it, right.

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Speaker 1: It's just multiplying two numbers. But if I give you

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Speaker 1: the product and I ask you, can you tell me,

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Speaker 1: given this number, what two prime numbers composed? That product

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Speaker 1: turns out to be very, very computationally expensive. And he

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Speaker 1: in his algorithm, he showed that if you had a

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Speaker 1: sufficient in large quantum computer you could do this efficiently.

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Speaker 1: And you say, well, what does that matter, Well, it

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Speaker 1: turns out it matters is because as the basis of

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Speaker 1: how we do encryption today and how we secure all

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Speaker 1: forms of communications and financial systems and everything, where basically

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Speaker 1: your private key is your prime number. Malcome, I would

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Speaker 1: have another private key which is my prime number. Those

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Speaker 1: two numbers are secret, and when we multiply them, that's

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Speaker 1: the public key that we share over the Internet and

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Speaker 1: so on the protocol. Everybody sees our public key, but

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Speaker 1: they cannot calculate or private keys. But if you had

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Speaker 1: a large enough quantum computer, now you could. So there's

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Speaker 1: a big implication that the encryption protocols of the world

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Speaker 1: need to be changed to prevent future quantum computers from decryption.

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Speaker 1: So that's not the fault of quantum computers, but it's

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Speaker 1: an example of the consequences we build all sorts of

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Speaker 1: assumptions in the world about what problems are easy and

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Speaker 1: hard to do mathematically, and uh, and this technology will

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Speaker 1: alter that equation. Yeah. Yeah, But there was something I

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Speaker 1: was thinking about when you were talking. I was imagining

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Speaker 1: the world of clinical trials of a promising new drug,

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Speaker 1: which are now conducted in exactly the same way basically

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Speaker 1: as they were conducted hundred years ago. You put it

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Speaker 1: in people and observe differences between you know, the experimental

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Speaker 1: i'm and a control are. And it's because the task

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Speaker 1: of modeling drugs interaction with very very different human beings

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Speaker 1: is too complicated. This is the kind of thing that

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Speaker 1: down the road we might be able to simulate a

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Speaker 1: drug trial. Yes, some the opportunities of of these advancements

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Speaker 1: But you're right, I mean, that's a good example where

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Speaker 1: All of that aspect of it is is the opportunity

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Speaker 1: can all identify you're in that position. You know, I

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Speaker 1: It is one of these things that will be definitely

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Speaker 1: And both our successes and the challenges as as we

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Speaker 1: we know how to. Let's explore this idea, the potential

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Speaker 1: put them in the proper context. What's happening with technologies

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Speaker 1: global warming or fighting pandemics. Accelerating the right of discovery

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Speaker 1: the question, how can this advances in computing accelerate the

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Speaker 1: scientific method? So let's peel the layer. What is behind

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Speaker 1: the scientific method? If we look at it very very simply,

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Speaker 1: So you gotta you know, know and exploit the knowledge

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Speaker 1: You say, well, how can these technologies help you? Take

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Speaker 1: the first one to search and learn from the past.

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Speaker 1: So AI in the form of natural language processing, in

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Speaker 1: huge graphs with which to search knowledge that already existed.

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Speaker 1: It's greatly helping us. I mean we we live it

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Speaker 1: the power of search right of information the web. But

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Speaker 1: as as scientists, you can do this if you can

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Speaker 1: its connections and help you as a scientist acquire information fast.

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Speaker 1: So that's a use of AI for the search. Then

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Speaker 1: generate hypothesis, there's a beautiful new area in and I

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Speaker 1: do the task of classification. Right. If I give you images,

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Speaker 1: you you know, I say, hey, designed to me a

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Speaker 1: chair that looks like an avocado, right, and the system

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Speaker 1: and so on. Right. So, so now you can use

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Speaker 1: creates these generative models, I want to verify whether they

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Speaker 1: they work like they like? They say, because I'm simbulating

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Speaker 1: and quantum and simulation to be able to do this better.

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Speaker 1: Then the next step says, well, let's realize it in practice.

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Speaker 1: Let's do experimentation now, so I can have robots that

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Speaker 1: synthesized with chemistry that are AI guided to optimally create

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Speaker 1: create the molecules and so on. So I like to

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Speaker 1: the scientific method, think about it as a method, and

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Speaker 1: now ask yourself how the loop of technologies that we're

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Speaker 1: scientists and humans. And that is what I think is

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Speaker 1: going to have revolutionary potential because I'm I'm closed with

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Speaker 1: the idea of what a difference it made you brought

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Speaker 1: up m r n A, What a difference it made

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Speaker 1: to have the tools available to us to compress the

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Speaker 1: time to discovery from the average time of fourteen years

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Speaker 1: for a vaccine to under one. And think of the

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Speaker 1: implications that happen well in future pandemics, in in climate change.

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Speaker 1: How are we going to compress a time to discovery?

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Speaker 1: And and that's going to be the power of the

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Speaker 1: scientific methods supercharged with computing, including quantum and act mm HM.

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Speaker 1: When I ask a question, it sounds like it is

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Speaker 1: impossible to be a pessimist and work on quantum computing.

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Speaker 1: I like that so so uh probably true, you know,

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Speaker 1: because when you have so many challenges and uh and

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Speaker 1: so many difficulty it takes a particular type of people

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Speaker 1: to have the courage to be able to overcome them.

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Speaker 1: But it gets combined when when you know that the

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Speaker 1: theory is very sound and correct, the fact that we

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Speaker 1: haven't been able to realize the technology that allows that

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Speaker 1: theory to be expressed is in itself a source of energy,

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Speaker 1: right And indeed, like you, you cannot be, you know,

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Speaker 1: a pessimist if you want to be at the banguard

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Speaker 1: of the creation of this technology. And also the implications

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Speaker 1: of it are so profound for some of the most

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Speaker 1: fundamental problems that that that is another source of optimism

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Speaker 1: required for the technology. Yeah, this has been so fascinating.

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Speaker 1: Thank you so much. I've really enjoyed this, Dr Gil,

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Speaker 1: Thank you so much. Thank you again to Dr Dario

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Speaker 1: Gil for his insights about the future of quantum computing.

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Speaker 1: It will be fascinating see how the conversions of old

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Speaker 1: and new can revolutionize the way we live and communicate.

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Speaker 1: Smart Talks with IBM is produced by Emily Rostack with

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Speaker 1: Carlie Migliori and Katherine Girrado, edited by Karen shakerge engineering

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Speaker 1: by Martin Gonzalez, mixed and mastered by Jason Gambrel. Music

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Speaker 1: by Gramsco. Special thanks to Molly Sosha, Andy Kelly Mia Label,

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Speaker 1: Jacob Weisberg had a Fane, Eric Sandler and Maggie Taylor,

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Speaker 1: and the teams at eight Bar and IBM. Smart Talks

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Speaker 1: with IBM is a production of Pushkin Industries and I

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Speaker 1: Heart Media. You can find more episodes at IBM dot

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Speaker 1: com slash smart Talks. You'll find more Pushkin podcasts on

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Speaker 1: the I Heart Radio app, Apple Podcasts, or wherever you

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Speaker 1: like to listen. I'm Malcolm Gladwell, See you next time.

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Speaker 1: The Beginning