Smart Talks with IBM and Malcolm Gladwell: Quantum Computing is Tomorrow’s Computing, Today

Over the next decade, quantum computing has the potential to revolutionize the way we work and live. In this episode of Smart Talks, Malcolm talks to Dr. Darío Gil, Senior Vice President and Director of IBM Research, about quantum computing and its potential to accelerate discovery and development.

Learn more about your ad-choices at https://www.iheartpodcastnetwork.com

See omnystudio.com/listener for privacy information.

Episode Transcript

Speaker 1 (00:04):
Hello, Hello. This is Smart Talks with IBM, a podcast
from Pushkin Industries, High Heart Media and IBM about what
it means to look at today's most challenging problems in
a new way. I'm Malcolm Gladwell. In this episode, I'll
be discussing the capabilities of quantum computing with Dr Dario

(00:27):
gil Dr Gill is the senior Vice president and director
of IBM Research. He's also recognized globally as one of
the brightest minds in the quantum computing industry. But what
we know is that is theoretically sound and possible, and
that we are making very significant progress towards achieving good goal.

(00:48):
And that's why this is a quest of doing something
that has never been done before, but it is definitely possible.
Earlier this year, at the Wall Street Journals Virtual CIO
Network Summit, Dr Gil pro claimed that the next ten
years will be the decade in which quantum really comes
of age. So what is quantum computing and how will

(01:08):
it transform over the next decade and in what ways
will quantum computers change the way we interact with technology.
Let's dive in. Thanks for joining us. Start to kill.

(01:29):
It's a real pleasure thank you for having me, Malcolm,
it's a pleasure to be with you. I wanted to
start my talent us a little bit about yourself. You
did your graduate work at m I T. What did
you study there? I studied nanotechnology at m i T.
I joined the Nanostructure's laboratory. It was in the Department
of Electrical Engineering and Computer Science and their professor Hank Smith,

(01:54):
who was one of the pioneers of the field of
nano fabrication. You know, how do you manipulate and build
incredibly small part of our world? And so that sets
you up for the world that you're in now. Right
this the quantum computing flows naturally out of the idea

(02:14):
that this plenty of room at the bottom. Yes, it
is because of the different theories of physics. One that
is of course extraordinarily irrelevant for the world of the
small is quantum physics. So if we are to understand
how matter behaves at the atomic scale and electronic structure

(02:35):
and the interactions and uh what occurs at the level
of materials, you have to understand what is happening in
the world of quantum physics. For those who are not
from a technical background, can you give us the give
me this the simplest definition of what quantum computing is.
You know, we're all accustomed to using computers today, and

(02:57):
at the foundation of the computers we use every day
and our smartphones is the idea of bits or binary digits.
And interestingly, this is an idea that the fascination that
all the complexity in the world we could reuse it
in a mode of communication, which is zeros and ones
dates back as far back as Lightnings, but it was

(03:19):
really in the twenty century. In the nineteen fourties and fifties,
a Cloud Shannon, which is one of the great leaders
in the world of computing, told us we could create
these incredibly sophisticated modes of communication and computation just by
being able to map all the complexity and information in
the world to strings of zeros and ones, and computers

(03:39):
are machines that manipulate zeros and ones very very efficient.
So in quantum computing it actually revisits that idea. It
turns out that the most fundamental building block of computation
is not the zero on one, not the bit. That's
something known as the cubit sure for quantum bit, and
at its heart it melts that idea ye of information

(04:01):
with the idea of physics. So what quantum computers do
is they manipulate information, exploiting the loss of quantum physics
to be able to do calculations that are simply impossible
to do if you just use the binary digit the
zeros and ones. It's a richer way to represent information

(04:24):
and manipulate information by exploiting the properties of quantum mechanics
to do things are impossible classically. Yeah, would you say
you can tackle problems now that would be impossible mechanically.
What is that? Could you represent the difference in capacity
of these two ways of computing, like how how much
of a gap is here between quantum and conventional computer?

(04:48):
You know, and it's full potential the gaps it's an
exponential So let me let me explain what I mean
by that. If you want to simulate nature, so let's
say very practical that you want to build a better
battery technology for electric cons right, so those are based
on lithium chemistry. And if you want to say, build

(05:09):
a battery that is longer lasting or safer, contract faster.
So now you have in front of your material science
problems and what you can do is go through the
periodic table and see all the different elements and figure
out how you are going to combine them to create
the material that has the properties you like. Okay, so
how can you go about doing that? Well? One approach

(05:31):
is to, uh, do it empirically, just try and humans
have been doing that since time me memorial right, combine
elements and see hy works. Another methodological approach is if
you have a theory of how things work, you could
try to solve the problem long form and see if
you can have a close form solution to the problem.
And a third angle that really came about with the

(05:52):
advent of computers is you could simulate it. Right. You
could use computers to mimic how atoms behave and use
those equation issues and try to do the calculations to
see what the properties would be. What's the problem. The
problem is that no matter how big computers we use today,
the number of variables that we have to compute over

(06:14):
is roughly correlated to the number of electrons and electron
orbitals present in those elements. So the more sophisticated and
material we've gotta make, the more interactions between these electrons
we gotta be able to calculate, and that number grows exponentially,
you know, pretty soon we need to have a computer,

(06:36):
you know, with more components than there are atoms in
the universe. Right, so it's impractical. So what do we do.
We approximate place, and when we approximate, we don't get
the right answer. So we are in this stock loop
of rate of progress. What is interesting on quantum is
that for modeling those kinds of problems, instead of having
an exponential meaning the more electrons we add, you know,

(07:00):
the number of calculations blowing up. Now it's a relation
that looks more like linear, meaning I only need one
more cubit roughly speaking, to model another electron. So even
even have a complex molecule where I need you know,
dozens or hundreds of of orbital calculations and I need

(07:21):
to do I would need a machine with dozens of
hundreds of cubits rather than a classical machine with ten
trillion transistors. Right that we don't know how to make.
It's not an extension or a derivative from the kind
of computers that we've been using. It's an entirely new
class of computers. That's exactly right, And that is what's

(07:42):
so interesting. So there will be classical computing and quantum computing.
That's how important this is, right, that you phrased it
very nicely, which is not just another evolution. Is we've
actually left no element of the assumption of the current
information on computational model as sacred. Right, not even the
bit has survived the quantum information view of the world. Right,

(08:05):
the very foundation had to be revisited. So where are
we How close are we having quantum computers? Actually that
you you describe that that task kept trying to figure
out how to make a better battery. When do you
think we'll be able to use quantum computers for a
task like that? We already have built quantum computers. Actually,

(08:26):
Iban was the first company in the world in two
thousand and sixteen to build a small quantum computer and
making universally available. So the first part of the answer
is like, we already have quantum computers, so you can
learn how to program them. You can start mapping problems
around how you do it. The challenge we have is
that very difficult to build these machines, so we have
not yet crossed the path where they can do things

(08:50):
that are of practical value that or classical machines cannot.
So we gotta keep an eye now of when that
crossover is going to happen, and that is something that
is this tier of information and computation that would likely
happen in the next few years, and then that begins,
you know, a whole a whole new space right of opportunity. Yeah,

(09:11):
you had said earlier with the stage now where the
machines make errors, what's the source of the difficulty at
the moment. Yeah, then you can build these machines that
have special properties to represent information in unique ways that
gives them exponential power compared to classical machines. The massines
are subject to errors, but there's both a theory and

(09:34):
a way to implement an error correction technique that would
allow us to compute in definitely and with like minimum
levels of errors. That's that it would be a practical value.
But realizing that large scale machine is still a significant
journey with a lot of scientific and engineering breakthroughs need
to occur. But what we know is that it's theoretically

(09:54):
sound and possible, and that we are making very significant
progress towards achieving the goal. And that's why this is
a quest of doing something that has never been done before,
but it is definitely possible. Yeah, you began with the
example of the electric battery. Give me another example of
a of an industry or a problem which would be

(10:16):
well served by using a quantum computer. These three categories
that quantum is going to make a difference similar in nature.
The world of mathematics, linear algebra that matters to maschine
learning and other problems. And the third is world of
search and graphs and what you can do with them
that matter a great deal. But I want to give
an example that's very famous of the consequences of quantum computing,

(10:40):
which is some of the implications that it will have
for cybersecurity and for security in general. And this came
from a very very famous algorithm that gave re energizing
of the field of quantum in the ninety nineties called
Short's algorithm, and it came from Peter Short, who was
then at bad LAPS and now is a professor at

(11:01):
m I T. And he published an algorithm that took
a problem that has to do with factoring. So basically,
the problem is if you take two prime numbers that
are say large, and you multiply those two prime numbers together,
and you get the product, the final number of the
product of those of those two If doing the multiplication,

(11:25):
it's very easy to do. Anybody can do it, right.
It's just multiplying two numbers. But if I give you
the product and I ask you, can you tell me,
given this number, what two prime numbers composed? That product
turns out to be very, very computationally expensive. And he
in his algorithm, he showed that if you had a
sufficient in large quantum computer you could do this efficiently.

(11:49):
And you say, well, what does that matter, Well, it
turns out it matters is because as the basis of
how we do encryption today and how we secure all
forms of communications and financial systems and everything, where basically
your private key is your prime number. Malcome, I would
have another private key which is my prime number. Those

(12:10):
two numbers are secret, and when we multiply them, that's
the public key that we share over the Internet and
so on the protocol. Everybody sees our public key, but
they cannot calculate or private keys. But if you had
a large enough quantum computer, now you could. So there's
a big implication that the encryption protocols of the world

(12:30):
need to be changed to prevent future quantum computers from decryption.
So that's not the fault of quantum computers, but it's
an example of the consequences we build all sorts of
assumptions in the world about what problems are easy and
hard to do mathematically, and uh, and this technology will
alter that equation. Yeah. Yeah, But there was something I

(12:54):
was thinking about when you were talking. I was imagining
the world of clinical trials of a promising new drug,
which are now conducted in exactly the same way basically
as they were conducted hundred years ago. You put it
in people and observe differences between you know, the experimental
i'm and a control are. And it's because the task

(13:17):
of modeling drugs interaction with very very different human beings
is too complicated. This is the kind of thing that
down the road we might be able to simulate a
drug trial. Yes, some the opportunities of of these advancements
is to accelerate discovery, the rate of time from invention

(13:41):
to realizing the capability to compress it very significantly, perhaps
by a factor of ten x in time or ten
x in cost. So um, yeah, you're You're absolutely right,
Uh that the only path that we have to improve
or expert mental capacity to be able to determine and

(14:02):
compress how efficient we can do it is to be
more sophisticated as to how much we need to test,
and they trade off. The thing that you can balance
is if I can compute essentially to do virtual experiments,
but to do it with the level of accuracy that
is required and the revel of fidelity that we would
see in the real world, then it's a net game.

(14:23):
And indeed that is going to be you know, one
of the main vectors of use cases and applications for
quantum computer. I was thinking about the the m R
and A COVID vaccines, which were conceived, they developed using
the most cutting edge science imaginable, and then tested using
the least cut edge science. Right. You went from this

(14:45):
dazzling feed of century, you know, genetic biomedicine, and then
you painstakingly rounded up people, brought them in, gave them shots,
you know, ask them questions, had them fill out fill
out four. I mean, it's like and that's also an
example of this. You just you just brought this this
thing about what happens when we combine this new technology

(15:09):
with existing technologies. In that hypothetical case, that's a combination.
You're taking this brand new field of biomedicine and marrying
it to a a way of of revolutionizing the clinical
aspect of medicine. That's two systems in combination create a
kind of exponential change in your outcome. Yeah, and and

(15:33):
and that's the part that we always struggle as humans,
right because since you know, time progresses linearly for us,
the fact that there's these exponentials in the form of
technology is an example. But we've also seen exponentials. I
think people are understanding a little bit better, uh, you know,
tragically in this case the power of exponentials in the
context of a pandemic. But the fact that these exponentials

(15:55):
are present in our in our world and our universe,
and that through technology you get these combinations of technology
that allows you to create them. It's something that is
both the source of massive opportunity and aspects that have
to do with with with governance of how we need
to be smart enough to be able to guide them properly.

(16:15):
But you're right, I mean, that's a good example where
you brought up in terms of an experimental capability of
m R n A and UH and and interestingly enough,
the sort of more theoretical unification or some of these
ideas is that MR and A technology is again rooting
on the idea that biology is information and and that

(16:36):
if we're able to in this case UH the code
like in this case involves in the genetic sequencing of
the virus and from there figure out what parts of
the code I need to bring back into your immune
system to be able to find it efficiently in a
ways about being able to read information, process information, send
it back to you and you yourself are are the

(16:58):
computer right with this new program to deal with the
biology of it, So bringing information on that and how
efficiently we computed, you know, how you conduct clinical trials.
All of that aspect of it is is the opportunity
to have more mastery overall environments. Can you ask a
personal question if you look over the history of technology,

(17:21):
every now and again, there are people who are in
these magical moments where they are aware that the thing
they're working on is going to dramatically transform the world
I live in. You can imagine someone working in Edison's
lab or someone working in the Manhattan project in the
desert in you know, in or you know that we

(17:44):
can all identify you're in that position. You know, I
believe that to my core, and I indeed like I
feel that way, and the team feels this way. That
we have assembled a team that is a fine esteem
in the world that it is designing and imagining and

(18:04):
creating these quantum computers. And there's not a doubt in
our mind that as difficult as this quest is, it
has that potential. It's one of those things that it
answers the equation of what is possible to do with technology.
It is one of these things that will be definitely
in the history books in terms of information and computation

(18:24):
and what it means. And I think that that brings
us an enormous amount of energy into us, right because
when we come to work every day and when we
see the progress we're making, is this feeling of being
absolutely at the cutting edge where every day that the
team makes progress is the actual boundary of knowledge and

(18:44):
possibilities in the field. And it just feels magical, right,
And both our successes and the challenges as as we
push forward, you know, are colored by this this passion
of saying boy, but this is this is a frontier
of human and uh, you know, and we're all working
together to uh you know, do it as well as

(19:05):
we know how to. Let's explore this idea, the potential
of combining these different computing forms. Give me some more
practical examples of what combinations look like if we're gonna
put them in the proper context. What's happening with technologies
like quantum and AI. I'd like to say that they

(19:27):
need to fit in the context of a method. And
the method that we're most passionate about, it's not a
new one, is the scientific method. Or thesis is that
we should expand the reach of the scientific method, and
for the most important problems that we're confronting. Let's take
global warming or fighting pandemics. Accelerating the right of discovery

(19:52):
is incredibly important right this aspect of time. So here's
the question, how can this advances in computing accelerate the
scientific method? So let's peel the layer. What is behind
the scientific method? If we look at it very very simply,
we would say is the act of learning from the past.

(20:12):
So you gotta you know, know and exploit the knowledge
that has been accumulated that is typically in the form
of documents, uh, books, etcetera. You need to then be
able to generate hypothesis that can be verified or nullified.
You've gotta conduct experiments and then you gotta share it
with a community for feedback and go through the loop again.

(20:36):
You say, well, how can these technologies help you? Take
the first one to search and learn from the past.
So AI in the form of natural language processing, in
the form of being able to process documents and build
huge graphs with which to search knowledge that already existed.
It's greatly helping us. I mean we we live it

(20:56):
in a day to day life by you know, like
the power of search right of information the web. But
as as scientists, you can do this if you can
greatly enhance the ability to read scientific literature and see
its connections and help you as a scientist acquire information fast.
So that's a use of AI for the search. Then
you go the next step generate a hypothesis. Well, to

(21:19):
generate hypothesis, there's a beautiful new area in and I
called generative models. We may be a little bit more
familiar with the use of neural networks in SATURAI to
do the task of classification. Right. If I give you images,
you give me labels. Right, I said, well, this is
a yellow car, a red car, and so on, and
it gets done with an all network. Perhaps people are

(21:40):
less familiar with using now some of these new networks
to do generation in terms of classification. So I give
you you know, I say, hey, designed to me a
chair that looks like an avocado, right, and the system
can automatically give you hundreds of thousands of different designs
and so on. Right. So, so now you can use
this generative capability to imagine new molecules back to connect

(22:05):
it to our idea about chemistry and lithium chemistries that
have these properties. I want give me molecules that may
fit that criteria. And if I have an eye that
creates these generative models, I want to verify whether they
may work as they want. So now I can use
a quantum computer right in the future to say do
they work like they like? They say, because I'm simbulating

(22:25):
a model of of chemistry is now and combining AI
and quantum and simulation to be able to do this better.
Then the next step says, well, let's realize it in practice.
Let's do experimentation now, so I can have robots that
synthesized with chemistry that are AI guided to optimally create
the synthetic round, and the programming steps with which to

(22:46):
create the molecules and so on. So I like to
think about it is take a method that we know works,
the scientific method, think about it as a method, and
now ask yourself how the loop of technologies that we're
creating can enhance it and improve it in concert with
scientists and humans. And that is what I think is
going to have revolutionary potential because I'm I'm closed with

(23:09):
the idea of what a difference it made you brought
up m r n A, What a difference it made
to have the tools available to us to compress the
time to discovery from the average time of fourteen years
for a vaccine to under one. And think of the
implications that happen well in future pandemics, in in climate change.
How are we going to compress a time to discovery?

(23:33):
And and that's going to be the power of the
scientific methods supercharged with computing, including quantum and act mm HM.
When I ask a question, it sounds like it is
impossible to be a pessimist and work on quantum computing.
I like that so so uh probably true, you know,

(23:54):
because when you have so many challenges and uh and
so many difficulty it takes a particular type of people
to have the courage to be able to overcome them.
But it gets combined when when you know that the
theory is very sound and correct, the fact that we

(24:14):
haven't been able to realize the technology that allows that
theory to be expressed is in itself a source of energy,
right And indeed, like you, you cannot be, you know,
a pessimist if you want to be at the banguard
of the creation of this technology. And also the implications
of it are so profound for some of the most

(24:35):
fundamental problems that that that is another source of optimism
required for the technology. Yeah, this has been so fascinating.
Thank you so much. I've really enjoyed this, Dr Gil,
Thank you so much. Thank you again to Dr Dario
Gil for his insights about the future of quantum computing.

(24:58):
It will be fascinating see how the conversions of old
and new can revolutionize the way we live and communicate.
Smart Talks with IBM is produced by Emily Rostack with
Carlie Migliori and Katherine Girrado, edited by Karen shakerge engineering
by Martin Gonzalez, mixed and mastered by Jason Gambrel. Music

(25:23):
by Gramsco. Special thanks to Molly Sosha, Andy Kelly Mia Label,
Jacob Weisberg had a Fane, Eric Sandler and Maggie Taylor,
and the teams at eight Bar and IBM. Smart Talks
with IBM is a production of Pushkin Industries and I
Heart Media. You can find more episodes at IBM dot

(25:45):
com slash smart Talks. You'll find more Pushkin podcasts on
the I Heart Radio app, Apple Podcasts, or wherever you
like to listen. I'm Malcolm Gladwell, See you next time.
The Beginning