Speaker 1: Hello, Hello. This is Smart Talks with IBM, a podcast
Speaker 1: from Pushkin Industries, High Heart Media and IBM about what
Speaker 1: it means to look at today's most challenging problems in
Speaker 1: a new way. I'm Malcolm Gladwell. In this episode, I'll
Speaker 1: be discussing the capabilities of quantum computing with Dr Dario
Speaker 1: gil Dr Gill is the senior Vice president and director
Speaker 1: of IBM Research. He's also recognized globally as one of
Speaker 1: the brightest minds in the quantum computing industry. But what
Speaker 1: we know is that is theoretically sound and possible, and
Speaker 1: that we are making very significant progress towards achieving good goal.
Speaker 1: And that's why this is a quest of doing something
Speaker 1: that has never been done before, but it is definitely possible.
Speaker 1: Earlier this year, at the Wall Street Journals Virtual CIO
Speaker 1: Network Summit, Dr Gil pro claimed that the next ten
Speaker 1: years will be the decade in which quantum really comes
Speaker 1: of age. So what is quantum computing and how will
Speaker 1: it transform over the next decade and in what ways
Speaker 1: will quantum computers change the way we interact with technology.
Speaker 1: Let's dive in. Thanks for joining us. Start to kill.
Speaker 1: It's a real pleasure thank you for having me, Malcolm,
Speaker 1: it's a pleasure to be with you. I wanted to
Speaker 1: start my talent us a little bit about yourself. You
Speaker 1: did your graduate work at m I T. What did
Speaker 1: you study there? I studied nanotechnology at m i T.
Speaker 1: I joined the Nanostructure's laboratory. It was in the Department
Speaker 1: of Electrical Engineering and Computer Science and their professor Hank Smith,
Speaker 1: who was one of the pioneers of the field of
Speaker 1: nano fabrication. You know, how do you manipulate and build
Speaker 1: incredibly small part of our world? And so that sets
Speaker 1: you up for the world that you're in now. Right
Speaker 1: this the quantum computing flows naturally out of the idea
Speaker 1: that this plenty of room at the bottom. Yes, it
Speaker 1: is because of the different theories of physics. One that
Speaker 1: is of course extraordinarily irrelevant for the world of the
Speaker 1: small is quantum physics. So if we are to understand
Speaker 1: how matter behaves at the atomic scale and electronic structure
Speaker 1: and the interactions and uh what occurs at the level
Speaker 1: of materials, you have to understand what is happening in
Speaker 1: the world of quantum physics. For those who are not
Speaker 1: from a technical background, can you give us the give
Speaker 1: me this the simplest definition of what quantum computing is.
Speaker 1: You know, we're all accustomed to using computers today, and
Speaker 1: at the foundation of the computers we use every day
Speaker 1: and our smartphones is the idea of bits or binary digits.
Speaker 1: And interestingly, this is an idea that the fascination that
Speaker 1: all the complexity in the world we could reuse it
Speaker 1: in a mode of communication, which is zeros and ones
Speaker 1: dates back as far back as Lightnings, but it was
Speaker 1: really in the twenty century. In the nineteen fourties and fifties,
Speaker 1: a Cloud Shannon, which is one of the great leaders
Speaker 1: in the world of computing, told us we could create
Speaker 1: these incredibly sophisticated modes of communication and computation just by
Speaker 1: being able to map all the complexity and information in
Speaker 1: the world to strings of zeros and ones, and computers
Speaker 1: are machines that manipulate zeros and ones very very efficient.
Speaker 1: So in quantum computing it actually revisits that idea. It
Speaker 1: turns out that the most fundamental building block of computation
Speaker 1: is not the zero on one, not the bit. That's
Speaker 1: something known as the cubit sure for quantum bit, and
Speaker 1: at its heart it melts that idea ye of information
Speaker 1: with the idea of physics. So what quantum computers do
Speaker 1: is they manipulate information, exploiting the loss of quantum physics
Speaker 1: to be able to do calculations that are simply impossible
Speaker 1: to do if you just use the binary digit the
Speaker 1: zeros and ones. It's a richer way to represent information
Speaker 1: and manipulate information by exploiting the properties of quantum mechanics
Speaker 1: to do things are impossible classically. Yeah, would you say
Speaker 1: you can tackle problems now that would be impossible mechanically.
Speaker 1: What is that? Could you represent the difference in capacity
Speaker 1: of these two ways of computing, like how how much
Speaker 1: of a gap is here between quantum and conventional computer?
Speaker 1: You know, and it's full potential the gaps it's an
Speaker 1: exponential So let me let me explain what I mean
Speaker 1: by that. If you want to simulate nature, so let's
Speaker 1: say very practical that you want to build a better
Speaker 1: battery technology for electric cons right, so those are based
Speaker 1: on lithium chemistry. And if you want to say, build
Speaker 1: a battery that is longer lasting or safer, contract faster.
Speaker 1: So now you have in front of your material science
Speaker 1: problems and what you can do is go through the
Speaker 1: periodic table and see all the different elements and figure
Speaker 1: out how you are going to combine them to create
Speaker 1: the material that has the properties you like. Okay, so
Speaker 1: how can you go about doing that? Well? One approach
Speaker 1: is to, uh, do it empirically, just try and humans
Speaker 1: have been doing that since time me memorial right, combine
Speaker 1: elements and see hy works. Another methodological approach is if
Speaker 1: you have a theory of how things work, you could
Speaker 1: try to solve the problem long form and see if
Speaker 1: you can have a close form solution to the problem.
Speaker 1: And a third angle that really came about with the
Speaker 1: advent of computers is you could simulate it. Right. You
Speaker 1: could use computers to mimic how atoms behave and use
Speaker 1: those equation issues and try to do the calculations to
Speaker 1: see what the properties would be. What's the problem. The
Speaker 1: problem is that no matter how big computers we use today,
Speaker 1: the number of variables that we have to compute over
Speaker 1: is roughly correlated to the number of electrons and electron
Speaker 1: orbitals present in those elements. So the more sophisticated and
Speaker 1: material we've gotta make, the more interactions between these electrons
Speaker 1: we gotta be able to calculate, and that number grows exponentially,
Speaker 1: you know, pretty soon we need to have a computer,
Speaker 1: you know, with more components than there are atoms in
Speaker 1: the universe. Right, so it's impractical. So what do we do.
Speaker 1: We approximate place, and when we approximate, we don't get
Speaker 1: the right answer. So we are in this stock loop
Speaker 1: of rate of progress. What is interesting on quantum is
Speaker 1: that for modeling those kinds of problems, instead of having
Speaker 1: an exponential meaning the more electrons we add, you know,
Speaker 1: the number of calculations blowing up. Now it's a relation
Speaker 1: that looks more like linear, meaning I only need one
Speaker 1: more cubit roughly speaking, to model another electron. So even
Speaker 1: even have a complex molecule where I need you know,
Speaker 1: dozens or hundreds of of orbital calculations and I need
Speaker 1: to do I would need a machine with dozens of
Speaker 1: hundreds of cubits rather than a classical machine with ten
Speaker 1: trillion transistors. Right that we don't know how to make.
Speaker 1: It's not an extension or a derivative from the kind
Speaker 1: of computers that we've been using. It's an entirely new
Speaker 1: class of computers. That's exactly right, And that is what's
Speaker 1: so interesting. So there will be classical computing and quantum computing.
Speaker 1: That's how important this is, right, that you phrased it
Speaker 1: very nicely, which is not just another evolution. Is we've
Speaker 1: actually left no element of the assumption of the current
Speaker 1: information on computational model as sacred. Right, not even the
Speaker 1: bit has survived the quantum information view of the world. Right,
Speaker 1: the very foundation had to be revisited. So where are
Speaker 1: we How close are we having quantum computers? Actually that
Speaker 1: you you describe that that task kept trying to figure
Speaker 1: out how to make a better battery. When do you
Speaker 1: think we'll be able to use quantum computers for a
Speaker 1: task like that? We already have built quantum computers. Actually,
Speaker 1: Iban was the first company in the world in two
Speaker 1: thousand and sixteen to build a small quantum computer and
Speaker 1: making universally available. So the first part of the answer
Speaker 1: is like, we already have quantum computers, so you can
Speaker 1: learn how to program them. You can start mapping problems
Speaker 1: around how you do it. The challenge we have is
Speaker 1: that very difficult to build these machines, so we have
Speaker 1: not yet crossed the path where they can do things
Speaker 1: that are of practical value that or classical machines cannot.
Speaker 1: So we gotta keep an eye now of when that
Speaker 1: crossover is going to happen, and that is something that
Speaker 1: is this tier of information and computation that would likely
Speaker 1: happen in the next few years, and then that begins,
Speaker 1: you know, a whole a whole new space right of opportunity. Yeah,
Speaker 1: you had said earlier with the stage now where the
Speaker 1: machines make errors, what's the source of the difficulty at
Speaker 1: the moment. Yeah, then you can build these machines that
Speaker 1: have special properties to represent information in unique ways that
Speaker 1: gives them exponential power compared to classical machines. The massines
Speaker 1: are subject to errors, but there's both a theory and
Speaker 1: a way to implement an error correction technique that would
Speaker 1: allow us to compute in definitely and with like minimum
Speaker 1: levels of errors. That's that it would be a practical value.
Speaker 1: But realizing that large scale machine is still a significant
Speaker 1: journey with a lot of scientific and engineering breakthroughs need
Speaker 1: to occur. But what we know is that it's theoretically
Speaker 1: sound and possible, and that we are making very significant
Speaker 1: progress towards achieving the goal. And that's why this is
Speaker 1: a quest of doing something that has never been done before,
Speaker 1: but it is definitely possible. Yeah, you began with the
Speaker 1: example of the electric battery. Give me another example of
Speaker 1: a of an industry or a problem which would be
Speaker 1: well served by using a quantum computer. These three categories
Speaker 1: that quantum is going to make a difference similar in nature.
Speaker 1: The world of mathematics, linear algebra that matters to maschine
Speaker 1: learning and other problems. And the third is world of
Speaker 1: search and graphs and what you can do with them
Speaker 1: that matter a great deal. But I want to give
Speaker 1: an example that's very famous of the consequences of quantum computing,
Speaker 1: which is some of the implications that it will have
Speaker 1: for cybersecurity and for security in general. And this came
Speaker 1: from a very very famous algorithm that gave re energizing
Speaker 1: of the field of quantum in the ninety nineties called
Speaker 1: Short's algorithm, and it came from Peter Short, who was
Speaker 1: then at bad LAPS and now is a professor at
Speaker 1: m I T. And he published an algorithm that took
Speaker 1: a problem that has to do with factoring. So basically,
Speaker 1: the problem is if you take two prime numbers that
Speaker 1: are say large, and you multiply those two prime numbers together,
Speaker 1: and you get the product, the final number of the
Speaker 1: product of those of those two If doing the multiplication,
Speaker 1: it's very easy to do. Anybody can do it, right.
Speaker 1: It's just multiplying two numbers. But if I give you
Speaker 1: the product and I ask you, can you tell me,
Speaker 1: given this number, what two prime numbers composed? That product
Speaker 1: turns out to be very, very computationally expensive. And he
Speaker 1: in his algorithm, he showed that if you had a
Speaker 1: sufficient in large quantum computer you could do this efficiently.
Speaker 1: And you say, well, what does that matter, Well, it
Speaker 1: turns out it matters is because as the basis of
Speaker 1: how we do encryption today and how we secure all
Speaker 1: forms of communications and financial systems and everything, where basically
Speaker 1: your private key is your prime number. Malcome, I would
Speaker 1: have another private key which is my prime number. Those
Speaker 1: two numbers are secret, and when we multiply them, that's
Speaker 1: the public key that we share over the Internet and
Speaker 1: so on the protocol. Everybody sees our public key, but
Speaker 1: they cannot calculate or private keys. But if you had
Speaker 1: a large enough quantum computer, now you could. So there's
Speaker 1: a big implication that the encryption protocols of the world
Speaker 1: need to be changed to prevent future quantum computers from decryption.
Speaker 1: So that's not the fault of quantum computers, but it's
Speaker 1: an example of the consequences we build all sorts of
Speaker 1: assumptions in the world about what problems are easy and
Speaker 1: hard to do mathematically, and uh, and this technology will
Speaker 1: alter that equation. Yeah. Yeah, But there was something I
Speaker 1: was thinking about when you were talking. I was imagining
Speaker 1: the world of clinical trials of a promising new drug,
Speaker 1: which are now conducted in exactly the same way basically
Speaker 1: as they were conducted hundred years ago. You put it
Speaker 1: in people and observe differences between you know, the experimental
Speaker 1: i'm and a control are. And it's because the task
Speaker 1: of modeling drugs interaction with very very different human beings
Speaker 1: is too complicated. This is the kind of thing that
Speaker 1: down the road we might be able to simulate a
Speaker 1: drug trial. Yes, some the opportunities of of these advancements
Speaker 1: is to accelerate discovery, the rate of time from invention
Speaker 1: to realizing the capability to compress it very significantly, perhaps
Speaker 1: by a factor of ten x in time or ten
Speaker 1: x in cost. So um, yeah, you're You're absolutely right,
Speaker 1: Uh that the only path that we have to improve
Speaker 1: or expert mental capacity to be able to determine and
Speaker 1: compress how efficient we can do it is to be
Speaker 1: more sophisticated as to how much we need to test,
Speaker 1: and they trade off. The thing that you can balance
Speaker 1: is if I can compute essentially to do virtual experiments,
Speaker 1: but to do it with the level of accuracy that
Speaker 1: is required and the revel of fidelity that we would
Speaker 1: see in the real world, then it's a net game.
Speaker 1: And indeed that is going to be you know, one
Speaker 1: of the main vectors of use cases and applications for
Speaker 1: quantum computer. I was thinking about the the m R
Speaker 1: and A COVID vaccines, which were conceived, they developed using
Speaker 1: the most cutting edge science imaginable, and then tested using
Speaker 1: the least cut edge science. Right. You went from this
Speaker 1: dazzling feed of century, you know, genetic biomedicine, and then
Speaker 1: you painstakingly rounded up people, brought them in, gave them shots,
Speaker 1: you know, ask them questions, had them fill out fill
Speaker 1: out four. I mean, it's like and that's also an
Speaker 1: example of this. You just you just brought this this
Speaker 1: thing about what happens when we combine this new technology
Speaker 1: with existing technologies. In that hypothetical case, that's a combination.
Speaker 1: You're taking this brand new field of biomedicine and marrying
Speaker 1: it to a a way of of revolutionizing the clinical
Speaker 1: aspect of medicine. That's two systems in combination create a
Speaker 1: kind of exponential change in your outcome. Yeah, and and
Speaker 1: and that's the part that we always struggle as humans,
Speaker 1: right because since you know, time progresses linearly for us,
Speaker 1: the fact that there's these exponentials in the form of
Speaker 1: technology is an example. But we've also seen exponentials. I
Speaker 1: think people are understanding a little bit better, uh, you know,
Speaker 1: tragically in this case the power of exponentials in the
Speaker 1: context of a pandemic. But the fact that these exponentials
Speaker 1: are present in our in our world and our universe,
Speaker 1: and that through technology you get these combinations of technology
Speaker 1: that allows you to create them. It's something that is
Speaker 1: both the source of massive opportunity and aspects that have
Speaker 1: to do with with with governance of how we need
Speaker 1: to be smart enough to be able to guide them properly.
Speaker 1: But you're right, I mean, that's a good example where
Speaker 1: you brought up in terms of an experimental capability of
Speaker 1: m R n A and UH and and interestingly enough,
Speaker 1: the sort of more theoretical unification or some of these
Speaker 1: ideas is that MR and A technology is again rooting
Speaker 1: on the idea that biology is information and and that
Speaker 1: if we're able to in this case UH the code
Speaker 1: like in this case involves in the genetic sequencing of
Speaker 1: the virus and from there figure out what parts of
Speaker 1: the code I need to bring back into your immune
Speaker 1: system to be able to find it efficiently in a
Speaker 1: ways about being able to read information, process information, send
Speaker 1: it back to you and you yourself are are the
Speaker 1: computer right with this new program to deal with the
Speaker 1: biology of it, So bringing information on that and how
Speaker 1: efficiently we computed, you know, how you conduct clinical trials.
Speaker 1: All of that aspect of it is is the opportunity
Speaker 1: to have more mastery overall environments. Can you ask a
Speaker 1: personal question if you look over the history of technology,
Speaker 1: every now and again, there are people who are in
Speaker 1: these magical moments where they are aware that the thing
Speaker 1: they're working on is going to dramatically transform the world
Speaker 1: I live in. You can imagine someone working in Edison's
Speaker 1: lab or someone working in the Manhattan project in the
Speaker 1: desert in you know, in or you know that we
Speaker 1: can all identify you're in that position. You know, I
Speaker 1: believe that to my core, and I indeed like I
Speaker 1: feel that way, and the team feels this way. That
Speaker 1: we have assembled a team that is a fine esteem
Speaker 1: in the world that it is designing and imagining and
Speaker 1: creating these quantum computers. And there's not a doubt in
Speaker 1: our mind that as difficult as this quest is, it
Speaker 1: has that potential. It's one of those things that it
Speaker 1: answers the equation of what is possible to do with technology.
Speaker 1: It is one of these things that will be definitely
Speaker 1: in the history books in terms of information and computation
Speaker 1: and what it means. And I think that that brings
Speaker 1: us an enormous amount of energy into us, right because
Speaker 1: when we come to work every day and when we
Speaker 1: see the progress we're making, is this feeling of being
Speaker 1: absolutely at the cutting edge where every day that the
Speaker 1: team makes progress is the actual boundary of knowledge and
Speaker 1: possibilities in the field. And it just feels magical, right,
Speaker 1: And both our successes and the challenges as as we
Speaker 1: push forward, you know, are colored by this this passion
Speaker 1: of saying boy, but this is this is a frontier
Speaker 1: of human and uh, you know, and we're all working
Speaker 1: together to uh you know, do it as well as
Speaker 1: we know how to. Let's explore this idea, the potential
Speaker 1: of combining these different computing forms. Give me some more
Speaker 1: practical examples of what combinations look like if we're gonna
Speaker 1: put them in the proper context. What's happening with technologies
Speaker 1: like quantum and AI. I'd like to say that they
Speaker 1: need to fit in the context of a method. And
Speaker 1: the method that we're most passionate about, it's not a
Speaker 1: new one, is the scientific method. Or thesis is that
Speaker 1: we should expand the reach of the scientific method, and
Speaker 1: for the most important problems that we're confronting. Let's take
Speaker 1: global warming or fighting pandemics. Accelerating the right of discovery
Speaker 1: is incredibly important right this aspect of time. So here's
Speaker 1: the question, how can this advances in computing accelerate the
Speaker 1: scientific method? So let's peel the layer. What is behind
Speaker 1: the scientific method? If we look at it very very simply,
Speaker 1: we would say is the act of learning from the past.
Speaker 1: So you gotta you know, know and exploit the knowledge
Speaker 1: that has been accumulated that is typically in the form
Speaker 1: of documents, uh, books, etcetera. You need to then be
Speaker 1: able to generate hypothesis that can be verified or nullified.
Speaker 1: You've gotta conduct experiments and then you gotta share it
Speaker 1: with a community for feedback and go through the loop again.
Speaker 1: You say, well, how can these technologies help you? Take
Speaker 1: the first one to search and learn from the past.
Speaker 1: So AI in the form of natural language processing, in
Speaker 1: the form of being able to process documents and build
Speaker 1: huge graphs with which to search knowledge that already existed.
Speaker 1: It's greatly helping us. I mean we we live it
Speaker 1: in a day to day life by you know, like
Speaker 1: the power of search right of information the web. But
Speaker 1: as as scientists, you can do this if you can
Speaker 1: greatly enhance the ability to read scientific literature and see
Speaker 1: its connections and help you as a scientist acquire information fast.
Speaker 1: So that's a use of AI for the search. Then
Speaker 1: you go the next step generate a hypothesis. Well, to
Speaker 1: generate hypothesis, there's a beautiful new area in and I
Speaker 1: called generative models. We may be a little bit more
Speaker 1: familiar with the use of neural networks in SATURAI to
Speaker 1: do the task of classification. Right. If I give you images,
Speaker 1: you give me labels. Right, I said, well, this is
Speaker 1: a yellow car, a red car, and so on, and
Speaker 1: it gets done with an all network. Perhaps people are
Speaker 1: less familiar with using now some of these new networks
Speaker 1: to do generation in terms of classification. So I give
Speaker 1: you you know, I say, hey, designed to me a
Speaker 1: chair that looks like an avocado, right, and the system
Speaker 1: can automatically give you hundreds of thousands of different designs
Speaker 1: and so on. Right. So, so now you can use
Speaker 1: this generative capability to imagine new molecules back to connect
Speaker 1: it to our idea about chemistry and lithium chemistries that
Speaker 1: have these properties. I want give me molecules that may
Speaker 1: fit that criteria. And if I have an eye that
Speaker 1: creates these generative models, I want to verify whether they
Speaker 1: may work as they want. So now I can use
Speaker 1: a quantum computer right in the future to say do
Speaker 1: they work like they like? They say, because I'm simbulating
Speaker 1: a model of of chemistry is now and combining AI
Speaker 1: and quantum and simulation to be able to do this better.
Speaker 1: Then the next step says, well, let's realize it in practice.
Speaker 1: Let's do experimentation now, so I can have robots that
Speaker 1: synthesized with chemistry that are AI guided to optimally create
Speaker 1: the synthetic round, and the programming steps with which to
Speaker 1: create the molecules and so on. So I like to
Speaker 1: think about it is take a method that we know works,
Speaker 1: the scientific method, think about it as a method, and
Speaker 1: now ask yourself how the loop of technologies that we're
Speaker 1: creating can enhance it and improve it in concert with
Speaker 1: scientists and humans. And that is what I think is
Speaker 1: going to have revolutionary potential because I'm I'm closed with
Speaker 1: the idea of what a difference it made you brought
Speaker 1: up m r n A, What a difference it made
Speaker 1: to have the tools available to us to compress the
Speaker 1: time to discovery from the average time of fourteen years
Speaker 1: for a vaccine to under one. And think of the
Speaker 1: implications that happen well in future pandemics, in in climate change.
Speaker 1: How are we going to compress a time to discovery?
Speaker 1: And and that's going to be the power of the
Speaker 1: scientific methods supercharged with computing, including quantum and act mm HM.
Speaker 1: When I ask a question, it sounds like it is
Speaker 1: impossible to be a pessimist and work on quantum computing.
Speaker 1: I like that so so uh probably true, you know,
Speaker 1: because when you have so many challenges and uh and
Speaker 1: so many difficulty it takes a particular type of people
Speaker 1: to have the courage to be able to overcome them.
Speaker 1: But it gets combined when when you know that the
Speaker 1: theory is very sound and correct, the fact that we
Speaker 1: haven't been able to realize the technology that allows that
Speaker 1: theory to be expressed is in itself a source of energy,
Speaker 1: right And indeed, like you, you cannot be, you know,
Speaker 1: a pessimist if you want to be at the banguard
Speaker 1: of the creation of this technology. And also the implications
Speaker 1: of it are so profound for some of the most
Speaker 1: fundamental problems that that that is another source of optimism
Speaker 1: required for the technology. Yeah, this has been so fascinating.
Speaker 1: Thank you so much. I've really enjoyed this, Dr Gil,
Speaker 1: Thank you so much. Thank you again to Dr Dario
Speaker 1: Gil for his insights about the future of quantum computing.
Speaker 1: It will be fascinating see how the conversions of old
Speaker 1: and new can revolutionize the way we live and communicate.
Speaker 1: Smart Talks with IBM is produced by Emily Rostack with
Speaker 1: Carlie Migliori and Katherine Girrado, edited by Karen shakerge engineering
Speaker 1: by Martin Gonzalez, mixed and mastered by Jason Gambrel. Music
Speaker 1: by Gramsco. Special thanks to Molly Sosha, Andy Kelly Mia Label,
Speaker 1: Jacob Weisberg had a Fane, Eric Sandler and Maggie Taylor,
Speaker 1: and the teams at eight Bar and IBM. Smart Talks
Speaker 1: with IBM is a production of Pushkin Industries and I
Speaker 1: Heart Media. You can find more episodes at IBM dot
Speaker 1: com slash smart Talks. You'll find more Pushkin podcasts on
Speaker 1: the I Heart Radio app, Apple Podcasts, or wherever you
Speaker 1: like to listen. I'm Malcolm Gladwell, See you next time.
Speaker 1: The Beginning