November 18, 2025 • 53 mins
Malcolm Gladwell heads to San Francisco Tech Week to talk with IBM’s new Director of Research Jay Gambetta in front of a live audience. They discuss IBM’s plans to scale quantum computing power, the groundbreaking experiments already underway, and what impact these new computers could have on chemistry, medicine, and even finance.
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Speaker 1 (00:00):
Welcome to tech Stuff, a production from iHeartRadio. This season,
non Smart Talks with IBM, Malcolm Glabwell is back, and
this time he's taking the show on the road. Malcolm
is stepping outside the studio to explore how IBM clients
are using artificial intelligence to solve real world challenges and
transform the way they do business. From accelerating scientific breakthroughs
(00:23):
to reimagining education. It's a fresh look at innovation in action,
where big ideas meet cutting edge solutions. You'll hear from
industry leaders, creative thinkers, and of course Malcolm Glabwell himself
as he guides you through each story. New episodes of
Smart Talks with IBM drop every month on the iHeartRadio app,
(00:43):
Apple Podcasts, or wherever you get your podcasts. Learn more
at IBM dot com slash smart Talks.
Speaker 2 (00:53):
Hello, this is Malcolm Gladwell and you're listening to Smart
Talks with IBM. Every year, Tech Week brings thousands of
people together to network and learn about what's emerging across
the technology ecosystem, and at this year's conference in San Francisco,
I had an amazing opportunity to sit down in front
of a live audience with Jay Gambetta. Jay has been
(01:13):
with IBM for years and was recently promoted to Director
of Research. In this job, Jay has an important mission
helping the company build the future of computing. In the
last episode of Smart Talks, I began to learn about
quantum computing from IBM Chairman and CEO Arvind Krishna. But
this conversation I had with Jay went even deeper and
(01:35):
convinced me that the development of quantum isn't just a fun,
exciting new paradigm of computing. It may be one of
the most important scientific achievements of my lifetime. Jay, Good morning, morning,
Welcome to Smart Talks with IBM. Thank you special live
(01:56):
recording here for tech Week and congratulations. How long have
you been Head of Research at IBM Since October one?
It's October tenth today, so nine days, nine days. Can
you just talk a little about the position. This is
one of the most important positions in research in the world.
Speaker 3 (02:15):
IBM Research has been around for eighty years and it's
done some tremendous technology, a lot of inventions and fundamentals
for semiconductors, algorithms.
Speaker 4 (02:26):
AI.
Speaker 3 (02:27):
Yeah, I think if we look back to where a
lot of the innovation and the technology of the world
comes from. I think you can find Ibram's footprints on it,
and you can find IBM research. So yeah, I'm very
excited for the opportunity, but I'm also aware that there's
big shoes to fill, and I'm looking forward to how
we take IBM research forward. Obviously, I'm going to be
(02:48):
bringing a lot of the quantum side, which we're going
to talk about later. Beyond quantum, there's important work that
needs to happen in AI hybrid cloud, and I think
we're going to also enter to this new period of
mathematics where we get to use quantum machines and also
AI machines, and there's some really good, hard mathematical questions
(03:09):
to answer.
Speaker 2 (03:10):
How many people do you have working for you?
Speaker 3 (03:12):
I mean researchers in the three thousand researchers across many
different labs around the world. Our main lab is in Yorktown,
but then we have the lab actually out on the
West coast in Armadan or sbl now, and then we
have one in Zurich, Japan, and a few others around
the world.
Speaker 2 (03:29):
Tell me a little bit before we get into quantum.
I'm just curious about your path. So you're Australian. Yep,
we were talking about earlier backstage. Your accent has become muted.
You should crank it up because it's.
Speaker 3 (03:42):
Yeah, I'm slowly losing my Australian accent. I've been in
the US since two thousand and four, so accent, you know,
to sound very Australian. Yeah, but how do you practice it?
Maybe I got to go back to Australia. Here a
more Australians say gooday, how's it going? Things like that.
Speaker 2 (03:58):
And you didn't grow up thinking you're going to be
a scientist one day. Now.
Speaker 3 (04:02):
I grew up in a pretty normal life. My dreams
as a kid was building things, so I was either
going to be a carpenter or a mechanic. But I
had some great teachers that inspired me to go to university.
And I didn't even know, honestly what a scientist was.
And then I found myself at university doing science, particular physics,
(04:23):
and I ended up loving it. So you go from
there to what do you do your PhD? So I
did my undergrad in Australia. I did it actually in
laser science, so I think I watched some TV show
in lasers seemed interesting, so I wanted to learn about lasers,
and then I realized in trying to understand lasers, there
(04:44):
was this quantum mechanics, and so I was like, all right,
I want to actually understand this quantum mechanics. So I
did my equivalent of what you and the US school masters.
We call it honors in Australia, but we do a
research project. I said, I wanted to shoot lasers into
Adam and measure cross sections and I got really into
quantum physics. So then I decided, all right, I don't
(05:06):
understand this quantum physics. I want to do my PhD
in Interpretations of quantum mechanics. So I jumped in and said,
all right, what is this quantum mechanics? Why is everyone
arguing on these different interpretations. Then I finished my PhD
in Australia doing that. Then I moved over. At the
end of my PhD interpretations, it's more people arguing about
(05:29):
the equations whilst I think it's really important. I decided
if it's going to be like a collapse equation versus
many worlds, or a hidden variable model, or that just
quantum mechanics decoheres because we don't see supersitions in the
everyday world because it interacts with environment. The only way
(05:49):
to answer that question was to build a quantum computer.
And so then I decided at the end of my PhD,
I wanted to work out how to build a quantum computer.
And then I left there and I went to Yale.
And then at Yale, that's where I got into superconducting cubits,
which just a few days ago, one of the professors
there just won the Nobel Prize this year.
Speaker 2 (06:09):
Oh wow, I'm very interested in tracing because your career
follows the arc of quantum computing in a certain way.
Right at the time when you asked the question, what
I really want to do is to figure out how
to build a quantum computer. Where are we in quantum
computing at that point?
Speaker 3 (06:27):
Yeah, So that would have been nineteen ninety So there
was Shaw's algorithm came out, let's say ninety five. There
was a lot of theory. And then the reason I
went to Yale is because people had started to show
that they could see quantum effects in electrical circuits. So
these macroscopic objects they were starting to behave quantum mechanical
(06:49):
There was a really significant breakthrough in nineteen ninety nine
where Yazoo Nakamura in Japan showed that a qubit could
exist in these electrical circuits. I found out the group
at Yale were really trying to take these electrical circuits
and couple them together. And so it was like, if
I can build something using electrical circuits and they're big,
(07:12):
that that's the best way that you can decide to
test and understand whether quantum mechanics breaks down at a
macroscopic scale or not. Can we actually make them behave
as cubits? And I agree When I came to Yale,
the cubits were not very good. They were actually a
couple of nanoseconds. They were unstable. Electron would jump onto
(07:33):
the chip and then they would change all their configurations,
so you have to restart your experiment. And so for
the first time at Yale, it's kind of what the
challenge there was, how do we make a cubit? How
do we make a stable cubit? And that took about
five years, and that took us up to two thousand
and seven. And I think the rest of the world
(07:53):
looks and says quantums like just blowing up, but it's
actually been like almost phases theory showing that wet the algorithms,
how do we make a cubit? How do we couple
of the cubits together? And now we're in the scaling phase.
Speaker 2 (08:06):
Describe for us because many people in this room, me included,
have only a kind of surface level understanding of what
we mean when we use that phrase. What is the
difference between classical computing and quantum computing? What does that
word mean?
Speaker 3 (08:21):
Yeah, so you can go down the physics way and
talk about supersition and entanglement, which we can go in later,
but actually feel it's a bit of a distraction. So
when you think of classical computers, what they were is
there were machines that were very good at adding numbers together,
like simple addition, and they really showed that they could
(08:44):
add these numbers together really really fast. And now with
GPUs and other AI accelerators, we can add those numbers
together in parallel, and so the whole classical computing can
come down to just arithmetic, just adding numbers together. It
turns out that there's a math that is the quantum
mechanics shown to be true. It's more like a group
(09:06):
theory type structure, and the way quantum works is it
has a different math as are primitive, and if we
can exploit that new math and build a machine that
does it, it allows us to answer different questions. And
so think of it as a branching from classical compute
that is very good at adding just numbers together to
something that allows us to work with an algebra that
(09:29):
is much much harder to represent with addition. And that
algebra happens to be the same algebra that defines the
fundamental equations of nature, shirting this equation. So this is
why you say it computes the same way nature does,
but there are many other interesting problems. So the way
I explain it to people is think of it as
bringing a new primitive to computer science and allowing us
(09:54):
to work how to go with it. And I like
the analogy. Well, actually, maybe go back. So if you
went back in time, so we're one hundred years of quantum,
and you went back in time and you asked, what
is the foundation is a chemistry or physics? What would
have probably the scientists of one hundred years ago would
have said is they would have said, you know, chemistry
is about the small, physics is about planets and things
(10:17):
like this, and one hundred years ago when Heisenberg or Einstein,
all the greats, Schroding her himself invented quantum mechanics. It
was this concept that nature is discrete, not continuous. It
actually brought all the physical sciences together. And now quantum
mechanics is like it is the foundation of the science.
(10:39):
And so now what quantum computing is by that analogy
is computer science. The foundation of the math is coming
together with the physical science to allow us to compute
using math that if you were to try to represent
it with classical computers, it takes exponential time.
Speaker 2 (10:56):
Yeah, and it was a classical computer an expence in
a way that someone is well informed as I am
can understand it. A customer computer. It works primarily on
problems that can be easily represented in numerical form in numbers. Yes,
quantum allows you to step outside to a class of
problems that don't necessarily have a simple numerical representation. Yeah.
Speaker 3 (11:17):
And so imagine I got some medicine or or some
set of operation, but call it A, and I then
follow it by a different operation B. If A followed
by B gave a different answer than B first followed
by A. So in mathematics we call that commuting. But
like you can think of a correlation there one one
(11:38):
gives you a different outcome to the other. That means
there's an algebra behind it that Representing that algebra traditionally
on classical computers is really really hard, whereas that algebra,
if we can get creative, we can come up with
ways of representing that math. So we step as you say,
we step out aside of the simple math to a
(11:59):
new to allow us to calculate interesting problems.
Speaker 2 (12:03):
So quite in a sense, compliments, it doesn't replace judicial.
Speaker 3 (12:08):
I think this is one of the this is you're
exactly on is people think quantum is going to be
replacing classical If your problem is good at adding numbers together,
you should just keep using classical computers. I think the
future is going to be heterogeneous accelerators, and it will
definitely have quantum as one. But in some sense, the
next generation of superstars are going to be those applied
(12:29):
mathematicians that know, how do I write a problem using
the simple math of classical computers or the more complicated
math for quantum computers, and how do I actually iterate
between them? And things like this. This is where I
think the next generation of students are going to come
up with much more novel ideas. I can give you
examples of what we want to do on quantum, but
(12:50):
like you're giving them a fundamental, foundational new thing, and
so I'm optimistic they will do much better jobs than
my generation.
Speaker 2 (13:00):
Well, yeah, we're to get to some of the albums
in a moment. But I wanted you to the most
kind of down that you said as a kid, you
thought you might want to be a mechanic because you'd
like to build things. Describe to me what it takes
to build a quantum computer, Like, what are you doing
that's different from building a classical computer.
Speaker 3 (13:19):
Yeah, so maybe I'll give you analogy and then I'll
go in. So the way classical computers, we've got them
to get to smaller and smaller sizes like five seven animeters,
five animeters and things, is actually inventing material to kill
quantum effects. So you actually put dielectrics and other things
(13:40):
in there to kill the quantum tunneling effects, and you
want them to behave more classically in the quantum world,
you want to get rid of all the classical effects.
So you want to get rid of the ability of
the cubits to interact with the environment, and in the
in the sort of technical world, we call it this
quantum com The more ways you want to control the
(14:02):
quantum computer, you open it up to interacting with everything else,
like interacting with its environment. So the biggest challenge has
always been how do we give more control but don't
bring in other sources of noise. So I want to
be able to do gates on the cubit, but I
don't want it to decohere. I want to couple the cubits,
(14:25):
but I don't want them to couple to other things.
So the hardest challenge is the energy inside the cubits
is a nine gigahertz, and if your tames that by
HBO ten to the niggave thirty four with nine, you're
at a tender the negative twenty like three or something
in energy. That's a tiny amount of energy. So you're
(14:45):
trying to have a tiny, tiny amount of energy to control,
and you don't want that to interact with anything. So
you have to cool them down, you have to isolate them,
and you have to make the quantum effects dominate over
the classical effects.
Speaker 2 (15:00):
So practically, if I'm trying to do that. Right now,
how big are these machines?
Speaker 3 (15:05):
So the cubits themselves are not that big, So the
cubits themselves are like a few microns. But yeah, most
of the size so you can see some of our
I got a pleasure of showing you around to one
of the machines in Yorktown. You saw that they're like
twenty foot by twenty foot in size. Most of that
is all that equipment to isolate the cubit chip, which
(15:27):
is only a few millimeters when you put it together
from the rest of the environment. We will, as we
get better at that, miniaturize all the isolation. But that's
cooling it down to a few milli calvin, so about
a thousand times colder than outer space. It's isolating the
noise on any electrical signal so that no noise from
(15:49):
the outside world gets into the system. And so that's
a lot of isolators, filters, and things like that that
we've had to invent to allow us to make the
quantum properties of the chip go.
Speaker 2 (16:01):
It's like the Princess and the peak, mounds and mounds
and mounds of mattresses trying to isolate the impact of
this little thing and that maybe that's the best way
to describe it.
Speaker 3 (16:10):
Yeah, and you've got to keep it really really prestige.
Speaker 2 (16:14):
But that when you show me. So in the in
the lobby of the Watson Research Center in New Yorktown,
which by the way, is just the coolest building. It's
like a it's like a modernist it's awesome master piece. Anyway,
in the lobby there is there are these is it
two machines.
Speaker 3 (16:31):
It's it's inside a container that has three machines.
Speaker 2 (16:35):
Three machines, So what can you can you tell me
what would one of those machines cost to build right now?
Speaker 3 (16:41):
So typically we put them together in a way where
we upgrade them because we want to as I as
I was talking about before, one of the things we
want to do is always get algorithms done on our machines.
And I've got a roadmap of building bigger and bigger machines.
So usually one of those quantum processors today is out
(17:02):
of date in six months. So we want to build
this future of computing that leverages quantum computing where every
six months we've outdated a quantum processor. Eventually, hopefully we
get to a point where it's like stable and it
can be many years operating. But we want to get
(17:22):
as large a quantum computer in the hands of people
to explore the math as possible to come up with
those new algorithms. So we've had a philosophy of having
them open, working with universities and things like that. So
to answer a question of costs, yes, there's cost in
building the system, but we are operating in them much
more in a service model where people pay to use
(17:43):
the machine because we have to continuously calibrate it and
operate it and so depending on various different things. Professors,
we have a credits program where they get free access.
Some universities and enterprises they can buy premium access and
get more access. So think of not like a cost
of it, because it's almost like a continuum. I want
(18:05):
to make sure that the best quantum processors that I
can build get in the hands of students and professors
and interested enterprises that want to explore these machines as
fast as possible. And typically every six months we upgrade it. Yeah,
you don't start over, you upgrade. We upgrade various different pieces,
the processor, the electronics. Some upgrades are just simply replaced
(18:29):
the processor. But as an example, I think many people
have probably seen photos of quantum computers and you see
this scary thing with all these wires hanging down, as
I've referred to as the chandelier, and it's got all
these wires with loops and things like that. They're called
co x cables. When we first put the quantum computer
on the cloud in twenty sixteen, you could probably only
(18:51):
fit about fifty cubits inside one cryostat. We've had to
upgrade all those cables so that we can fit around
one thousand to get to three thousand, and that's about minaturizing.
So to answer your question, an upgrade, it depends. It
can be just the processor or it can be the
complete insides. And we're actually in our third generation of
(19:12):
our electronics to control the systems to make them faster,
less noise. Internally. We've got exciting results of going to
something like cold cryocemos. So you can bring down the
cost in terms of energy of running these quantum computers
almost to negligible, and you could imagine future quantum computers.
(19:33):
I'm not going to require much energy to run, so
unlike classical compute that requires lots of energy. The biggest
machines that we envision is only in the few megawatts,
but we have to upgrade to future controls that use
less energy. So it depends it's my long answer, short
answer to how it upgrades, and it depends on what
(19:53):
it is.
Speaker 2 (19:54):
The only observation that I felt I was capable of
making when you showed me the quantum machine is it's gorgeous.
I look a art.
Speaker 3 (20:02):
I've always believed that, and I think that there's an
IBM saying good design is good business. But we've always
taken pride in making sure what we build. I don't know,
I feel if you're going to build something that is new,
that can change, you should take the time to make
sure it looks and feels good.
Speaker 2 (20:23):
Will you donated to MoMA when you're through with that particular?
Speaker 3 (20:27):
Actually, I think we just put an old version of
one of our insights with the United Airlines and the AAPS,
which is the American Physical Society and the University of Chicago.
There's a replica right now. If you fly into one
of the terminals in Chicago, you can walk and see one.
Speaker 2 (20:45):
Oh really, yeah, well the most advanced thing at a
air I'm sure.
Speaker 3 (20:49):
Probably, but yeah, I hopefully. I think, yeah, we're open
to that. But yeah, I appreciate that you love the design.
Speaker 2 (20:55):
It was beautiful. So I last week I interviewed for
another episode Smotox, your CEO, Ivin Krishne, and when we
got to the quantum question. I mean, he's always alliant
and brilliant, and but quantum, he's like lit up. I
mean right in thinking that IBM is much more invested
(21:18):
in quantum than anybody else. Is that a fair statement? Oh?
Speaker 3 (21:20):
Yeah, most definitely.
Speaker 2 (21:22):
Why Why did IBM choose to kind of make this
such a priority.
Speaker 3 (21:26):
So when I took to the history of the physics side,
there's this interesting thing in the history of computing. So
we build computer like classical computers today using bits and
ce moss, and they consume energy. Do you know that
there is a way in classical where you can actually
compute without using energy. It's called reversal computing. Turns out
(21:48):
to be a terrible idea, it's not practical to build.
But IBM investigated that with Ralph Laura and Charlie Bennett
early on, and they proved the concept that we're versable computing.
The first use of quantum information theory. One of the
first actually was from IBM. When I did my PhD,
(22:09):
I remember actually picking up this paper on quantum teleportation
and seeing IBM written there, and at the time I
remember thinking that they make PCs. Well, what the hell
are they doing this foundational paper on quantum teleportation? Why
are they doing it? So to answer your question, actually,
IBM was the first in quantum information science because it's
(22:30):
the fundamental of computation. Can we actually come up with
compute that we can go beyond the classical So way
before anyone was talking about it, they were doing fundamental theory.
And then as we've built it, we've always When I
first came there, the experimental team was small. In twenty eleven,
we've had a small team that we're focusing on single
(22:54):
cubits coupling in them. I think in twenty twelve was
the first time we showed really good two Cuba gates
and no one was talking about quantum computing that And
then I remember in about twenty sixteen I said to
actually Arvin was the director of research, then can we
actually put our quantum computer on the cloud? Well that's
(23:17):
probably twenty fifteen, and it was always supporting that. So
as we've done more and more we've been able to
do it. It's had this program going now, I agree
is very visible, like because we're in this scaling phase
and so we're invested to keep scaling it and to
get why is At IBM research, what we always do
(23:40):
is answer what is the future of computing? Whether it's
coming up with new algorithms, coming up with better AI,
coming up with quantum, or coming up with just how
do different accelerators go together. It's our DNA to answer
the question of what is the future.
Speaker 2 (23:56):
Need a perfect problem for IBM because you kind of
need to have a legacy of buildings, building actual physical machines.
Speaker 3 (24:04):
Yeah, it's why I came to IBM. I wanted the experience,
the culture of building hard things that others have not
done before.
Speaker 2 (24:16):
Where do you imagine we are in the timeline of
this technology? It will come a point when it will mature.
My cell phone is a mature technology at this point.
How far are we from that point with condom?
Speaker 3 (24:29):
So I think there's various aspects of it. So we
set in twenty and seventy we set our goal that
in twenty twenty three we would be able to build
a machine that was beyond classical computers to simulate it,
and we achieved that in twenty twenty three. So to
run a bigger we call it a quantum circuit. The
details of it din't matter, but to run a quantum
(24:51):
workload that if you were to simulate that workload how
a quantum computer operates on a classical computer, you couldn't
do it. So we set that does our first and
now I've made it publicly that by twenty twenty nine
we'll build the first fault tolerant corantum computer. That is,
one that can completely handle the noise to the level
(25:13):
to allow you to run a very very large, large problem.
Speaker 2 (25:16):
So an example of a large problem.
Speaker 3 (25:18):
Yeah, a large quantum problem. So for around a couple
of one hundred cubits and one hundred million operations, you're
talking still interesting science problems like simulating a molecule, or
calculating a small optimization problem, or calculating say some part
(25:39):
of a matrix update in some type of differential. So
it'll still be scientific, but it'll be at the point
where it's beyond, well beyond any classical approximate method. And
then I think that's twenty twenty nine that's twenty twenty nine,
so we're four.
Speaker 2 (25:55):
Years away from something that can start to handle.
Speaker 3 (25:58):
Interesting problem, serious problems. I do believe the scientists will
find interesting heuristic problems before that, and so over the
next four years, you're going to continue to see more
and more let's call them heuristic not provable quantum problems
that run on quantum computers that come out. We're seeing
more and more come from many of our partners and ourselves.
(26:19):
Heuristic problems have value, but they have to be tested,
they have to stand up over time. You have to
run them many, many times, you have to try different ones,
and many times heuristic can lead to formal problems. So
you're going to see because we're beyond now the point
that you can simulate these quantum computers with any classical computer.
They're kind of like a scientific tool. So they're exploring
(26:40):
the heuristic.
Speaker 2 (26:41):
What do you have to get done between now and
twenty twenty nine to get there?
Speaker 3 (26:44):
So we had to reinvent how we wanted to do
error correction. So we have to demonstrate modules and if
we can demonstrate these error corrected module and our goal
is actually it's called Crookobarro I name all our chips
after birds, so it's called Kookobara. It is named after
an Australian verte. I think I still say kooka burrow
the way Australians do. We need to then show that
(27:07):
we can make a single module and then we want
to connect two of those modules together, and I call
that one cockatoo, which is another Australian vert. And then
if we can do that, so that's twenty six and
twenty seven, and then we want to scale the scale
those modules, and that we call starling and we want
to scale that in twenty twenty nine. So get a module,
(27:28):
join two modules together and scale and so each module
is going to be around one thousand cubits.
Speaker 2 (27:34):
The challenge to getting there is it finding the right
material or how would you describe what? That's the beauty
to be done.
Speaker 3 (27:41):
That's the beauty of it is if we would have
been here two years ago, I couldn't tell you how
it would be done. So we had a huge breakthrough.
We came up with a new code, a new quantumeric
Russian code, and that code. The biggest im part of
that code that is the most important is it's modular
(28:01):
in nature. So previous codes without getting too technical, they
were very monolithic and you had to build a very
big device, and I wouldn't have known we would have
to invent tools like new Simos tools to do that.
So we came up with this new code. We started
on twenty nineteen, we published in twenty twenty four. We
(28:21):
kind of had most of things worked out in twenty
twenty three. That's why we got confident to release the thing.
So the biggest breakthrough we had is coming up with
a code that's modular in nature, and think of that
as a like a blueprint. And so now we have
the blueprint, and now we're doing engineering tasks to implement
every part of that blueprint.
Speaker 2 (28:42):
And so the minute you had that breakthrough, then you
began to have confidence at something exactly these goals could
be met.
Speaker 3 (28:48):
And then you can't. And then anyone that's done engineering
will know what I'm talking about when I say this
is cycles are learning. It takes so long from test
idea to build two tests. In hardware, the cycles of
learning are much much lower than software, Like you can
be really really faster in the software. So then we've
planned out our iterations over the next few years, and
(29:12):
so we have to successfully demonstrate them. I may slip,
because sometimes you may estimate your time wrong, but we
now have exactly what we want to do for the
next four years.
Speaker 2 (29:25):
I want to go back to that breakthrough for a moment.
What does the word breaks we mean in that context, Like,
it's not that you get a call in the morning
from somebody who says, I did it. Do you see
it coming? Or is it a surprise when they get there.
Speaker 3 (29:37):
So the way this one worked is Sogo Brave, who's
an algorithm person at IBM, one of the smartest and
quantum information.
Speaker 2 (29:45):
Don't mention his name to everyone value you'll come for him.
Speaker 3 (29:49):
Everyone in quantum already knows his name. I don't think
there's an idea that has not originated from him in quantum.
So we're looking at other codes and we'll go all right,
we've got to get serious about these codes. And others
were starting to propose to bring these and then we
call them LDBC codes from the classical space into the quantum.
(30:13):
And I asked him, we need to get ahead of
this and understand what they're doing it. He's like the
most modest perfuse late, Jay, let me learn about them
and I'll generate a report for us and we'll read
through it. And then I said, great, Then I don't know.
Six months later, he comes back with one hundred page
report on everyone. Everyone had done an LTPC codes. I'm like, awesome.
(30:35):
So I started then to read from them. And then
we said, all right, how do we under the assumptions
of the hardware we can build? Can we get an
LTPC code knowing what we can build? And that's a
great question, and so we put a small team together
to investigate and honestly took two to three years, and
(30:58):
we iterated, and we used the constraints, so we had
the sort of theory, and then we had the constraints
of what we could build. And we iterated for a
few years, and then at the end of that we
came out with a solution that, yes, it is possible
to meet all the constraints of the hardware and build
a code that will work.
Speaker 2 (31:18):
I'm just curious about So you had this task, this
problem you want to solve, and when you set out
on the task of trying to solve the problem, what's
your certainty level that you'll get a solution.
Speaker 3 (31:31):
Well, that's the beauty of science. For things where you
kind of have a few ideas. My philosophy is try
a few for the ones that need to be in
that like wow moment. It's honestly, you've got to set
the ambition really, really high, but know when to stop.
It was a great team that went together to get
(31:53):
that breakthrough, and we knew that we needed to come
up with a code that met the requirements of the experiment.
And I think what was different before then is the
theorists that were doing error correction codes didn't necessarily know
the constraints of experiments, so it was like really more
pen and paper. So this became one all right, given
(32:15):
these sets of constraints, is it possible?
Speaker 2 (32:18):
When that's questions about this? Sorry, And I love these
kind of moments when things become clear. At the time
the problem was solved, were you aware of the implications
of the solution or did that takes you knew exactly.
Speaker 3 (32:31):
What we set out exactly like either we were going
to have to work out how to cool down a
very large piece of silicon, which would require a lot
of engineering and building tools beyond what anyone has ever
built in the silicon semoss industry. To implement the known
codes or we had to come up with a different one,
(32:53):
and once I knew that we had one that I
didn't need to reinvent any tools to build. The implications
are clear how.
Speaker 2 (33:01):
Much time elapsed between the time you heard the problem
was solved and the time you told Arvin Krishna, the CEO,
the problem was solved.
Speaker 3 (33:10):
I'm sure the next time I spoke to him, I update,
but I don't remember. The beauty of Avin is he
trusts the scientists will do it, and so he doesn't
really check on us. We update him when when it
is and he he empowers us to do really hard problems.
Speaker 2 (33:24):
Yeah, so let's talk about uses. I mean they're really
like cool, big shiny machine. I think you'll get paid
twenty twenty nine. But there's all kinds of really interesting
problems you're already working on.
Speaker 3 (33:37):
Yes, this is like another interesting area is I can
prove in pen and paper algorithms that we want to
run that. Like, it's not that we don't know what
to do with a quantum computer. There are hundreds of algorithms.
So you can go to I think it's called quantumzoo
dot com and you can see many many algorithms people
are coming up with more of more of them that
(33:59):
they prove by pen and paper. But imagine now we
have a machine that you can't simulate. How do you
actually discover algorithms in a scientific way? How do you
look and discover algorithms using a quantum computer. We're in
this exciting period right now, and so even though I
(34:21):
can prove these ones that we can run in the future,
there's a big white space between what the machines we
have and we're going to build and continue to do
and those ones that want the provable ones. And I'm
an optimistic person by nature. I think getting those machines
in the hands of students to explore and look at
(34:42):
heuristic algorithms. So looking at the equivalent of doing numerical
algorithms on computers, which there's many histories of numerical algorithms
being discovered on classical computers before we had formal proofs
that we rely on today people would even are you
the way AI works was driven numerically, even though we
(35:04):
have input into it. There are ones in optimization driven numerically.
We are entering that phase. So the computer scientists now
need to go play with these primitives. Our prediction is
over the next couple of years, we're going to see
valuable numerical equivalent algorithms emerge. And where the scientists are
(35:25):
going is in four categories. One is simulating nature, so
looking at either hay Enerji physics, chemistry, light problems. As
an example, with our partners in Japan, they took one
of our quantum computers and for Gackle, a very large
classical supercomputer, and they ran a problem where quantum was
(35:47):
just a sub routine of the problem that was running
on all of for Gackle, and they were able to
look at an interesting molecule, a molecule that if you
would go by pan and paper you would have said,
it's going to take me a very long time to
run that. They were able to on that quite accurately,
heuristically and already get results that are comparable with the
best classical methods. So they are extremely excited because they
(36:08):
want to push that further, and they're sort of showing
that you can take a classical supercomputer with quantum as
a subroutine and start to push the level.
Speaker 2 (36:16):
They were This was trying to solve a medical problem.
Speaker 3 (36:19):
Is this one is a like most people don't realize,
like iron sulfur, just something as simple as iron and sulfur,
we can't solve that exactly, Like iron sulfur, molecules are
too hard. So really small small molecules are really really hard,
too hard for classical computers to solve. People think we
can solve a lot of things. It actually turns out
(36:41):
we can't solve very much.
Speaker 2 (36:42):
You say solve ins instance, you know precisely how that
molecule works and is constructed.
Speaker 3 (36:47):
No, precisely what the energy levels of that molecule is
and how they come together, and then be able to
do that on a classical computer and compare it to
a quantum.
Speaker 2 (36:57):
It would be really really useful to know that specific.
Speaker 3 (37:00):
Because if you can have energy levels, then you can
estimate reaction rates. If you can estimate reaction rates, you
can see how different types of chemicals will react. That
can then lead to better informing eventually how to build
materials or even drug design. I just want to be
careful and not say, oh, we're going to solve drug
design or that, because there's many scientific steps to make
(37:24):
that so and so what quantum gives you as a
different tool to give you more accuracy and then lead
to making the different methods work.
Speaker 2 (37:33):
You can subcontract out aspects of a problem quantum right now,
and that just gets you further along and you would.
Speaker 3 (37:41):
Have been so at the moment. Even this result still
does not beat the best approximate classical method. It's comparable.
So the art of chemistry for the last hundred years
has been about approximating. So what we've done is we
have got very very good are coming up with ways
(38:01):
of approximating nature. And a lot of the things that
we do and we exploit and we use to estimate approximations.
They don't a stimulate nature of the way nature is.
They approximate it. And there's I could list many different
acronyms of different methods that go into approximating nature. What
quantum gives us is to eventually get beyond that approximation
(38:25):
and do it the way nature works. And so we
aren't beating those approximation methods. And this is why I think,
this is why it's still in the science. But they're
getting comparable. Getting comparable with a new tool where the
previous tool is a dead end makes scientists very excited. Yeah,
that nuance is where it is, and so that's in
machine learning, sorry Hamiltonian. Then there's examples in differential equations,
(38:49):
So can I actually come up with differential equations and
solve them? And if I can solve them, you could
look at things like an avious Stokes equation goes into weather.
There's financial differential equations that you can better predict. So
differential equations. There's many different examples there. And then I
would say that two others are optimization, and then there's
quantum versions of machine learning that are very exciting as well.
Speaker 2 (39:13):
Cleveland Clinic one of the organizations that you guys have
worked with. Why would the Cleveland Clinic be calling you up?
Speaker 3 (39:19):
Because that problem that they want to look at. So
they've also done similar problem to the recent lab. So
they've taken that method now and they've looked at molecules
that matter for drug design. So they're fundamentally looking at
those molecules that matter for eventually replacing some of the steps.
(39:39):
So they're investing to see how reliable it can be done.
And so there's a scientist there that's done many iterations
now using the techniques that were done first with the
team in Japan. They've now replicated that for new molecules
that are essential primitives for eventually designing drugs and things
that may matter for medical.
Speaker 2 (40:00):
And also there's some finance firms yep, HBC, Vango, yep,
and their interest is.
Speaker 3 (40:06):
What so that was the differential equation and optimization. So
if you are doing very large calculations like risk portfolio,
or if you want to model the Black Shaws equation
or things like this that are fundamental for them to
make better predictions, come up with better trades and things
like this. That is a very hard computational task. And
(40:28):
so rather than quantum replacing that whole problem, can quantum
be a subroutine in there? And what HSBC showed is
they showed they could take their real data, they could
take their real classical method and they just replaced a
tiny part of it. They replaced a tiny part of
it with a quantum subroutine that allowed them to come
up with better predictions of the weights that then when
(40:51):
they were to compare TRIALA versus Trial B, it was
thirty four percent better at predicting algorithmic tron And that's
a big deal for them.
Speaker 2 (41:00):
It's huge.
Speaker 3 (41:01):
Yes, Now, do they need to do more trials? Do
they need to see is this a heuristic algorithm? Do
we need to be careful? Is there other classical algorithms
that go into these are great questions that are now
being investigated. So think of this period of heuristic algorithms
is really a period of scientific discovery using these machines,
(41:22):
knowing that we want to continue and build the ones
which have determinist their algorithms that can run.
Speaker 2 (41:29):
Do the people who would profit the most by starting
to run quantum experiments realize that they would profit so
much from running quantum experience And does the world know this.
You've given us a couple of specific examples, but generally speaking,
there must be a very large universe of people who
could gain from at least starting to play in the space.
Speaker 3 (41:52):
So the enterprises that use computation as key for their
survival understand the limits of classical computation and they're very
interested to get started. The universities are very interested. Could
we get more students doing more algorithms? One hundred percent?
Some of the limitations on the rate of algorithm discovery
(42:14):
is because people are thinking through the classical way of
writing algorithms. My belief is yes, So this is why
we want to get more and more students and things,
because it's just starting. But I would say in general,
most people are aware of it. Could we get more,
could we accelerate it?
Speaker 2 (42:30):
Yes?
Speaker 3 (42:30):
Do we need to make better hardware, do we need
to come up with better libraries, yes? Do we need
better software yes, But it's all happening over the next
few years.
Speaker 2 (42:40):
Is it hard to get someone who's spent their entire
life thinking in terms of solving problems to classical means
to make the transition to this new paradigm.
Speaker 3 (42:48):
There's a lot of examples when you approach something with
the classical intuition, it's not the right way to do
it when you approach it through the quantum. But if
people are being taught to understand the fundamentals of the math,
then a lot of the techniques carry across. I don't
recommend people need to learn about entanglement or supersition because
(43:11):
whilst the physicists will argue like spooky action a distance
and all these type of things, entanglement is the power. Yes,
that's how physicists are labeled. How quantum is different. But
I would say, do we need some physicists really worrying
thinking about that?
Speaker 1 (43:26):
Yes?
Speaker 3 (43:26):
But We need more applied mathematicians that are realizing they
can use this as a as a different way of
looking at the problems.
Speaker 2 (43:33):
Yeah, I when I asked you one question, No, we're
describing a a It's more than a new technology. We're
talking about a new paradigm. It's a way of thinking
about problems. Can you compare this to kind of previous
technological paradigms. If I'm thinking at the last couple hundred years,
what does this rank in terms of a new field
(43:55):
that we've opened up.
Speaker 3 (43:57):
It's a hard question to answer, but I often say
the history of computing, this will be the first time
that computation has branched between classical and quantum. I like
thinking reading a lot in the past. One of the
things that I think was a way we changed as
a society was the invention of zero. Before zero, math
(44:19):
was limited. Realizing that numbers have a number as zero
allowed us to develop a whole set of new mathematics
that then went on and defined like everything from waves
to calculus to all of that. Yes, we can describe
it with that same math, but when we describe it
with that math, it gets exponentially big and gets impractical
(44:41):
to do. Now we can actually work on it. I
would say, if I had to give you a quick answer,
maybe going all the way back to when we were
accepted zero.
Speaker 2 (44:52):
I thought you were going to say, like the airplane,
but in fact, yeah, you went several orders of magnitude
beyond that.
Speaker 3 (44:57):
Yes, but I think it's so fundamental.
Speaker 2 (45:01):
This is absolutely fascinating. Thank you so much for chatting
with me about it.
Speaker 3 (45:05):
Thank you. Fret time.
Speaker 2 (45:08):
Hey listeners. So normally we end this episode here, but
the Tech Week attendees asked Jay some really great questions,
questions I wish I'd asked, so we wanted to include
those here. Enjoy.
Speaker 5 (45:21):
Hi, J, thank you so much for the great presentation.
My name is Trixie Apiado. I work for Willis Towers Watson,
an insurance broker. I help seisos identify and quantify their
cyber risk so they can prepare for threats before they happen.
And so quantum threats keep me up at night. You
mentioned so many good problems that quantum can solve. It
(45:43):
can also break encryptions in our classical computer systems. So
what safeguards or policies do you implement in your teams
to build quantum capabilities responsibly and what can we do
for people in this room, US builders and users to
secure our data in systems before quantum computers become more
(46:06):
energy efficient, cheaper, and more available.
Speaker 3 (46:09):
So it's a great question. So yes, one of the
algorithms for quantum computing is to break our traditional encryption.
So at IBM Research we were aware of this from
day one. We've come up with algorithms that we believe
and have very strong evidence will not be broken by
a quantum or classical computer, and has selected them. So
(46:34):
first the scientific technical question, security is saved. There are
algorithms that exist that we can implement that neither a
quantum or classical computer can break. So the technical answer
is we're all okay. The more complicated answer is a
social and society answer. Encryption was built in classical computing
(46:57):
in a way that was never thought of being grade it.
It's mixed everywhere. Some of it is downstream, some of
it is like software that you may use, Some of
it is software that you've developed. And I get that
if you've got a product and you want to have
it secure for the next ten years, you probably want
to think about how you're going to upgrade it, or
(47:19):
if you have data that needs to be secure for
the next ten years, it needs to upgrade to new encryption.
So the real challenge is more of a social business
problem of how do we actually transition from old encryption
to new encryption knowing this is going to happen. So
we at IBM have been very proactive on this. We've
(47:40):
developed tools where we can determine where encryption is used,
We've developed tools which can show you how to replace it,
and we early on have made sure the Mainframe when
we made these algorithms. So I think it was Z
sixteen that was the first version of the Mainframe to
have these quantum safe algorithms implemented. So my answer to
(48:03):
your question is, yes, there's a real problem, but it's
not a technical problem. It's a social and business problem.
And I'm not minimizing that. I understand that that is
a lot of work you need to start now. You
need to come up and do a you need to
make it part of your IT transformation. You need to
get onto it. And I realize, I realize it's not
(48:27):
going to take zero time because it's not an easy
problem to do. So the short answer is one we
developed algorithms that we can't, and we're developing tools to
help you in that transformation.
Speaker 5 (48:38):
Thank you so much.
Speaker 2 (48:40):
Thank you. My name is Emma.
Speaker 6 (48:41):
I'm a product manager at Expedia, working on software side
of things. My question is around the non technical roles
outside of the researchers, the mathematicians, the builders. How can
the rest of us, whether it be policymakers, those in
the legal fields, those thinking about what use cases quantum
can solve for in a few what should we be
thinking about and how can we prepare for that.
Speaker 3 (49:04):
It's a good question. I think this is part of
the requirement of the scientists to being able to articulate
where they are. We need a forum for those type
of discussions. I think a lot of this can fit
within the forums that we already have for classical and AI,
and I think we need to just be asking how
do we actually bring them into them Because I don't
(49:25):
think of quantum as a replacement of compute. I think
of it as an accelerator that expands what is possible,
and I think we can ask those questions in those forums.
Are we doing enough now? I think I agree with you. No,
I don't know the answer to it.
Speaker 6 (49:40):
I think it's a really interesting perspective because those existing
forums do start to bring in those other fields as well,
so it could warrant the same sort of discussion.
Speaker 3 (49:50):
And yeah, acts, and I understand those forums. Right now,
AI is probably dominating and it should be like we
going through a period of time where AI is impacting society.
The technology is impacting society in big ways. So I
totally understand that most of their focus should be on AI,
(50:12):
but we should start to ask where is quantum in
that as well?
Speaker 4 (50:16):
Hi, I'm Gobi and I'm a graduating PhD student at
Northwestern and also a member of south Park Commons, which
is a fund here. You mentioned earlier that some problems
are best solved by classical versus some problems are best
solved by quantum. When we're thinking about this, if we're
not experts in quantum, but we're thinking about this from
an AI perspective, could you just clarify when we think
(50:38):
about quantum, what is deterministic and what is not deterministic.
Speaker 3 (50:43):
I think the future of computing we've got to get
our heads around is that not everything is deterministic, and
it's much more going to be probilistic. How do you
handle error bars? How do you put confidence? I think
a lot of those questions which you're referring to INAI
are going to completely imply and quantum. I actually think
it's a mistake to compare AI verse quantum. I actually
(51:08):
think of quantum as much. It's quantum verse classical compute,
and AI is going to come across on top. So
as we go forward and we get a better understanding thing,
I'm not going to say quantum is going to replace
the classical compute that enables AI, but I think some
of the math you do in AI will be able
to go to both. So what can we formally prove?
(51:31):
I can come up with a problem where I take
a circle and a color, half of it red and
half oft of blue, and then I say, I'm going
to apply an operation that takes those dots make it. Say,
let's say ten dots over here red, ten dots over
here blue, and I'm going to wind them around many,
many times. I can then show you that if you
(51:51):
feed that into a classical computer it's a classical random
number generator. You can give yourself as much data as
you want. You will never be able to say did
the red come from the left side or the right side.
You would take infinite data. It is like you would
have to break a classical random number generator. I can
show you a quantum algorithm that can do that deterministically.
(52:15):
So where we're thinking is when the data appears to
be completely unstructured or you looks essentially like a complete
random number to the classical methods, there are quantum methods
that can actually potentially find that structure.
Speaker 2 (52:34):
That's it for this episode of Smart Talks with IBM.
If you haven't already, be sure to check out my
conversation with IBM Chairman and CEO Arvind Krishna, and stay tuned.
Another episode is coming soon. Smart Talks with IBM is
produced by Matt Romano, Amy Gains, McQuaid, Trina Menino, and
(52:55):
Jake Harper. Engineering by Nina Bird Lawrence, Mastering by Sarah Buguer,
music by Gramoscope, Strategy by Tatiana Lieberman, Cassidy Meyer and
Sofia Derlon. Smart Talks with IBM is a production of
Pushkin Industries and Ruby Studio at iHeartMedia. To find more
Pushkin podcasts, listen on the iHeartRadio app, Apple Podcasts, or
(53:19):
wherever you listen to podcasts. I'm Malcolm Godwell. This is
a paid advertisement from IBM. The conversations on this podcast
don't necessarily represent IBM's positions, strategies, or opinions.
Welcome to tech Stuff, a production from iHeartRadio. This season,
non Smart Talks with IBM, Malcolm Glabwell is back, and
this time he's taking the show on the road. Malcolm
is stepping outside the studio to explore how IBM clients
are using artificial intelligence to solve real world challenges and
transform the way they do business. From accelerating scientific breakthroughs
(00:23):
to reimagining education. It's a fresh look at innovation in action,
where big ideas meet cutting edge solutions. You'll hear from
industry leaders, creative thinkers, and of course Malcolm Glabwell himself
as he guides you through each story. New episodes of
Smart Talks with IBM drop every month on the iHeartRadio app,
(00:43):
Apple Podcasts, or wherever you get your podcasts. Learn more
at IBM dot com slash smart Talks.
Speaker 2 (00:53):
Hello, this is Malcolm Gladwell and you're listening to Smart
Talks with IBM. Every year, Tech Week brings thousands of
people together to network and learn about what's emerging across
the technology ecosystem, and at this year's conference in San Francisco,
I had an amazing opportunity to sit down in front
of a live audience with Jay Gambetta. Jay has been
(01:13):
with IBM for years and was recently promoted to Director
of Research. In this job, Jay has an important mission
helping the company build the future of computing. In the
last episode of Smart Talks, I began to learn about
quantum computing from IBM Chairman and CEO Arvind Krishna. But
this conversation I had with Jay went even deeper and
(01:35):
convinced me that the development of quantum isn't just a fun,
exciting new paradigm of computing. It may be one of
the most important scientific achievements of my lifetime. Jay, Good morning, morning,
Welcome to Smart Talks with IBM. Thank you special live
(01:56):
recording here for tech Week and congratulations. How long have
you been Head of Research at IBM Since October one?
It's October tenth today, so nine days, nine days. Can
you just talk a little about the position. This is
one of the most important positions in research in the world.
Speaker 3 (02:15):
IBM Research has been around for eighty years and it's
done some tremendous technology, a lot of inventions and fundamentals
for semiconductors, algorithms.
Speaker 4 (02:26):
AI.
Speaker 3 (02:27):
Yeah, I think if we look back to where a
lot of the innovation and the technology of the world
comes from. I think you can find Ibram's footprints on it,
and you can find IBM research. So yeah, I'm very
excited for the opportunity, but I'm also aware that there's
big shoes to fill, and I'm looking forward to how
we take IBM research forward. Obviously, I'm going to be
(02:48):
bringing a lot of the quantum side, which we're going
to talk about later. Beyond quantum, there's important work that
needs to happen in AI hybrid cloud, and I think
we're going to also enter to this new period of
mathematics where we get to use quantum machines and also
AI machines, and there's some really good, hard mathematical questions
(03:09):
to answer.
Speaker 2 (03:10):
How many people do you have working for you?
Speaker 3 (03:12):
I mean researchers in the three thousand researchers across many
different labs around the world. Our main lab is in Yorktown,
but then we have the lab actually out on the
West coast in Armadan or sbl now, and then we
have one in Zurich, Japan, and a few others around
the world.
Speaker 2 (03:29):
Tell me a little bit before we get into quantum.
I'm just curious about your path. So you're Australian. Yep,
we were talking about earlier backstage. Your accent has become muted.
You should crank it up because it's.
Speaker 3 (03:42):
Yeah, I'm slowly losing my Australian accent. I've been in
the US since two thousand and four, so accent, you know,
to sound very Australian. Yeah, but how do you practice it?
Maybe I got to go back to Australia. Here a
more Australians say gooday, how's it going? Things like that.
Speaker 2 (03:58):
And you didn't grow up thinking you're going to be
a scientist one day. Now.
Speaker 3 (04:02):
I grew up in a pretty normal life. My dreams
as a kid was building things, so I was either
going to be a carpenter or a mechanic. But I
had some great teachers that inspired me to go to university.
And I didn't even know, honestly what a scientist was.
And then I found myself at university doing science, particular physics,
(04:23):
and I ended up loving it. So you go from
there to what do you do your PhD? So I
did my undergrad in Australia. I did it actually in
laser science, so I think I watched some TV show
in lasers seemed interesting, so I wanted to learn about lasers,
and then I realized in trying to understand lasers, there
(04:44):
was this quantum mechanics, and so I was like, all right,
I want to actually understand this quantum mechanics. So I
did my equivalent of what you and the US school masters.
We call it honors in Australia, but we do a
research project. I said, I wanted to shoot lasers into
Adam and measure cross sections and I got really into
quantum physics. So then I decided, all right, I don't
(05:06):
understand this quantum physics. I want to do my PhD
in Interpretations of quantum mechanics. So I jumped in and said,
all right, what is this quantum mechanics? Why is everyone
arguing on these different interpretations. Then I finished my PhD
in Australia doing that. Then I moved over. At the
end of my PhD interpretations, it's more people arguing about
(05:29):
the equations whilst I think it's really important. I decided
if it's going to be like a collapse equation versus
many worlds, or a hidden variable model, or that just
quantum mechanics decoheres because we don't see supersitions in the
everyday world because it interacts with environment. The only way
(05:49):
to answer that question was to build a quantum computer.
And so then I decided at the end of my PhD,
I wanted to work out how to build a quantum computer.
And then I left there and I went to Yale.
And then at Yale, that's where I got into superconducting cubits,
which just a few days ago, one of the professors
there just won the Nobel Prize this year.
Speaker 2 (06:09):
Oh wow, I'm very interested in tracing because your career
follows the arc of quantum computing in a certain way.
Right at the time when you asked the question, what
I really want to do is to figure out how
to build a quantum computer. Where are we in quantum
computing at that point?
Speaker 3 (06:27):
Yeah, So that would have been nineteen ninety So there
was Shaw's algorithm came out, let's say ninety five. There
was a lot of theory. And then the reason I
went to Yale is because people had started to show
that they could see quantum effects in electrical circuits. So
these macroscopic objects they were starting to behave quantum mechanical
(06:49):
There was a really significant breakthrough in nineteen ninety nine
where Yazoo Nakamura in Japan showed that a qubit could
exist in these electrical circuits. I found out the group
at Yale were really trying to take these electrical circuits
and couple them together. And so it was like, if
I can build something using electrical circuits and they're big,
(07:12):
that that's the best way that you can decide to
test and understand whether quantum mechanics breaks down at a
macroscopic scale or not. Can we actually make them behave
as cubits? And I agree When I came to Yale,
the cubits were not very good. They were actually a
couple of nanoseconds. They were unstable. Electron would jump onto
(07:33):
the chip and then they would change all their configurations,
so you have to restart your experiment. And so for
the first time at Yale, it's kind of what the
challenge there was, how do we make a cubit? How
do we make a stable cubit? And that took about
five years, and that took us up to two thousand
and seven. And I think the rest of the world
(07:53):
looks and says quantums like just blowing up, but it's
actually been like almost phases theory showing that wet the algorithms,
how do we make a cubit? How do we couple
of the cubits together? And now we're in the scaling phase.
Speaker 2 (08:06):
Describe for us because many people in this room, me included,
have only a kind of surface level understanding of what
we mean when we use that phrase. What is the
difference between classical computing and quantum computing? What does that
word mean?
Speaker 3 (08:21):
Yeah, so you can go down the physics way and
talk about supersition and entanglement, which we can go in later,
but actually feel it's a bit of a distraction. So
when you think of classical computers, what they were is
there were machines that were very good at adding numbers together,
like simple addition, and they really showed that they could
(08:44):
add these numbers together really really fast. And now with
GPUs and other AI accelerators, we can add those numbers
together in parallel, and so the whole classical computing can
come down to just arithmetic, just adding numbers together. It
turns out that there's a math that is the quantum
mechanics shown to be true. It's more like a group
(09:06):
theory type structure, and the way quantum works is it
has a different math as are primitive, and if we
can exploit that new math and build a machine that
does it, it allows us to answer different questions. And
so think of it as a branching from classical compute
that is very good at adding just numbers together to
something that allows us to work with an algebra that
(09:29):
is much much harder to represent with addition. And that
algebra happens to be the same algebra that defines the
fundamental equations of nature, shirting this equation. So this is
why you say it computes the same way nature does,
but there are many other interesting problems. So the way
I explain it to people is think of it as
bringing a new primitive to computer science and allowing us
(09:54):
to work how to go with it. And I like
the analogy. Well, actually, maybe go back. So if you
went back in time, so we're one hundred years of quantum,
and you went back in time and you asked, what
is the foundation is a chemistry or physics? What would
have probably the scientists of one hundred years ago would
have said is they would have said, you know, chemistry
is about the small, physics is about planets and things
(10:17):
like this, and one hundred years ago when Heisenberg or Einstein,
all the greats, Schroding her himself invented quantum mechanics. It
was this concept that nature is discrete, not continuous. It
actually brought all the physical sciences together. And now quantum
mechanics is like it is the foundation of the science.
(10:39):
And so now what quantum computing is by that analogy
is computer science. The foundation of the math is coming
together with the physical science to allow us to compute
using math that if you were to try to represent
it with classical computers, it takes exponential time.
Speaker 2 (10:56):
Yeah, and it was a classical computer an expence in
a way that someone is well informed as I am
can understand it. A customer computer. It works primarily on
problems that can be easily represented in numerical form in numbers. Yes,
quantum allows you to step outside to a class of
problems that don't necessarily have a simple numerical representation. Yeah.
Speaker 3 (11:17):
And so imagine I got some medicine or or some
set of operation, but call it A, and I then
follow it by a different operation B. If A followed
by B gave a different answer than B first followed
by A. So in mathematics we call that commuting. But
like you can think of a correlation there one one
(11:38):
gives you a different outcome to the other. That means
there's an algebra behind it that Representing that algebra traditionally
on classical computers is really really hard, whereas that algebra,
if we can get creative, we can come up with
ways of representing that math. So we step as you say,
we step out aside of the simple math to a
(11:59):
new to allow us to calculate interesting problems.
Speaker 2 (12:03):
So quite in a sense, compliments, it doesn't replace judicial.
Speaker 3 (12:08):
I think this is one of the this is you're
exactly on is people think quantum is going to be
replacing classical If your problem is good at adding numbers together,
you should just keep using classical computers. I think the
future is going to be heterogeneous accelerators, and it will
definitely have quantum as one. But in some sense, the
next generation of superstars are going to be those applied
(12:29):
mathematicians that know, how do I write a problem using
the simple math of classical computers or the more complicated
math for quantum computers, and how do I actually iterate
between them? And things like this. This is where I
think the next generation of students are going to come
up with much more novel ideas. I can give you
examples of what we want to do on quantum, but
(12:50):
like you're giving them a fundamental, foundational new thing, and
so I'm optimistic they will do much better jobs than
my generation.
Speaker 2 (13:00):
Well, yeah, we're to get to some of the albums
in a moment. But I wanted you to the most
kind of down that you said as a kid, you
thought you might want to be a mechanic because you'd
like to build things. Describe to me what it takes
to build a quantum computer, Like, what are you doing
that's different from building a classical computer.
Speaker 3 (13:19):
Yeah, so maybe I'll give you analogy and then I'll
go in. So the way classical computers, we've got them
to get to smaller and smaller sizes like five seven animeters,
five animeters and things, is actually inventing material to kill
quantum effects. So you actually put dielectrics and other things
(13:40):
in there to kill the quantum tunneling effects, and you
want them to behave more classically in the quantum world,
you want to get rid of all the classical effects.
So you want to get rid of the ability of
the cubits to interact with the environment, and in the
in the sort of technical world, we call it this
quantum com The more ways you want to control the
(14:02):
quantum computer, you open it up to interacting with everything else,
like interacting with its environment. So the biggest challenge has
always been how do we give more control but don't
bring in other sources of noise. So I want to
be able to do gates on the cubit, but I
don't want it to decohere. I want to couple the cubits,
(14:25):
but I don't want them to couple to other things.
So the hardest challenge is the energy inside the cubits
is a nine gigahertz, and if your tames that by
HBO ten to the niggave thirty four with nine, you're
at a tender the negative twenty like three or something
in energy. That's a tiny amount of energy. So you're
(14:45):
trying to have a tiny, tiny amount of energy to control,
and you don't want that to interact with anything. So
you have to cool them down, you have to isolate them,
and you have to make the quantum effects dominate over
the classical effects.
Speaker 2 (15:00):
So practically, if I'm trying to do that. Right now,
how big are these machines?
Speaker 3 (15:05):
So the cubits themselves are not that big, So the
cubits themselves are like a few microns. But yeah, most
of the size so you can see some of our
I got a pleasure of showing you around to one
of the machines in Yorktown. You saw that they're like
twenty foot by twenty foot in size. Most of that
is all that equipment to isolate the cubit chip, which
(15:27):
is only a few millimeters when you put it together
from the rest of the environment. We will, as we
get better at that, miniaturize all the isolation. But that's
cooling it down to a few milli calvin, so about
a thousand times colder than outer space. It's isolating the
noise on any electrical signal so that no noise from
(15:49):
the outside world gets into the system. And so that's
a lot of isolators, filters, and things like that that
we've had to invent to allow us to make the
quantum properties of the chip go.
Speaker 2 (16:01):
It's like the Princess and the peak, mounds and mounds
and mounds of mattresses trying to isolate the impact of
this little thing and that maybe that's the best way
to describe it.
Speaker 3 (16:10):
Yeah, and you've got to keep it really really prestige.
Speaker 2 (16:14):
But that when you show me. So in the in
the lobby of the Watson Research Center in New Yorktown,
which by the way, is just the coolest building. It's
like a it's like a modernist it's awesome master piece. Anyway,
in the lobby there is there are these is it
two machines.
Speaker 3 (16:31):
It's it's inside a container that has three machines.
Speaker 2 (16:35):
Three machines, So what can you can you tell me
what would one of those machines cost to build right now?
Speaker 3 (16:41):
So typically we put them together in a way where
we upgrade them because we want to as I as
I was talking about before, one of the things we
want to do is always get algorithms done on our machines.
And I've got a roadmap of building bigger and bigger machines.
So usually one of those quantum processors today is out
(17:02):
of date in six months. So we want to build
this future of computing that leverages quantum computing where every
six months we've outdated a quantum processor. Eventually, hopefully we
get to a point where it's like stable and it
can be many years operating. But we want to get
(17:22):
as large a quantum computer in the hands of people
to explore the math as possible to come up with
those new algorithms. So we've had a philosophy of having
them open, working with universities and things like that. So
to answer a question of costs, yes, there's cost in
building the system, but we are operating in them much
more in a service model where people pay to use
(17:43):
the machine because we have to continuously calibrate it and
operate it and so depending on various different things. Professors,
we have a credits program where they get free access.
Some universities and enterprises they can buy premium access and
get more access. So think of not like a cost
of it, because it's almost like a continuum. I want
(18:05):
to make sure that the best quantum processors that I
can build get in the hands of students and professors
and interested enterprises that want to explore these machines as
fast as possible. And typically every six months we upgrade it. Yeah,
you don't start over, you upgrade. We upgrade various different pieces,
the processor, the electronics. Some upgrades are just simply replaced
(18:29):
the processor. But as an example, I think many people
have probably seen photos of quantum computers and you see
this scary thing with all these wires hanging down, as
I've referred to as the chandelier, and it's got all
these wires with loops and things like that. They're called
co x cables. When we first put the quantum computer
on the cloud in twenty sixteen, you could probably only
(18:51):
fit about fifty cubits inside one cryostat. We've had to
upgrade all those cables so that we can fit around
one thousand to get to three thousand, and that's about minaturizing.
So to answer your question, an upgrade, it depends. It
can be just the processor or it can be the
complete insides. And we're actually in our third generation of
(19:12):
our electronics to control the systems to make them faster,
less noise. Internally. We've got exciting results of going to
something like cold cryocemos. So you can bring down the
cost in terms of energy of running these quantum computers
almost to negligible, and you could imagine future quantum computers.
(19:33):
I'm not going to require much energy to run, so
unlike classical compute that requires lots of energy. The biggest
machines that we envision is only in the few megawatts,
but we have to upgrade to future controls that use
less energy. So it depends it's my long answer, short
answer to how it upgrades, and it depends on what
(19:53):
it is.
Speaker 2 (19:54):
The only observation that I felt I was capable of
making when you showed me the quantum machine is it's gorgeous.
I look a art.
Speaker 3 (20:02):
I've always believed that, and I think that there's an
IBM saying good design is good business. But we've always
taken pride in making sure what we build. I don't know,
I feel if you're going to build something that is new,
that can change, you should take the time to make
sure it looks and feels good.
Speaker 2 (20:23):
Will you donated to MoMA when you're through with that particular?
Speaker 3 (20:27):
Actually, I think we just put an old version of
one of our insights with the United Airlines and the AAPS,
which is the American Physical Society and the University of Chicago.
There's a replica right now. If you fly into one
of the terminals in Chicago, you can walk and see one.
Speaker 2 (20:45):
Oh really, yeah, well the most advanced thing at a
air I'm sure.
Speaker 3 (20:49):
Probably, but yeah, I hopefully. I think, yeah, we're open
to that. But yeah, I appreciate that you love the design.
Speaker 2 (20:55):
It was beautiful. So I last week I interviewed for
another episode Smotox, your CEO, Ivin Krishne, and when we
got to the quantum question. I mean, he's always alliant
and brilliant, and but quantum, he's like lit up. I
mean right in thinking that IBM is much more invested
(21:18):
in quantum than anybody else. Is that a fair statement? Oh?
Speaker 3 (21:20):
Yeah, most definitely.
Speaker 2 (21:22):
Why Why did IBM choose to kind of make this
such a priority.
Speaker 3 (21:26):
So when I took to the history of the physics side,
there's this interesting thing in the history of computing. So
we build computer like classical computers today using bits and
ce moss, and they consume energy. Do you know that
there is a way in classical where you can actually
compute without using energy. It's called reversal computing. Turns out
(21:48):
to be a terrible idea, it's not practical to build.
But IBM investigated that with Ralph Laura and Charlie Bennett
early on, and they proved the concept that we're versable computing.
The first use of quantum information theory. One of the
first actually was from IBM. When I did my PhD,
(22:09):
I remember actually picking up this paper on quantum teleportation
and seeing IBM written there, and at the time I
remember thinking that they make PCs. Well, what the hell
are they doing this foundational paper on quantum teleportation? Why
are they doing it? So to answer your question, actually,
IBM was the first in quantum information science because it's
(22:30):
the fundamental of computation. Can we actually come up with
compute that we can go beyond the classical So way
before anyone was talking about it, they were doing fundamental theory.
And then as we've built it, we've always When I
first came there, the experimental team was small. In twenty eleven,
we've had a small team that we're focusing on single
(22:54):
cubits coupling in them. I think in twenty twelve was
the first time we showed really good two Cuba gates
and no one was talking about quantum computing that And
then I remember in about twenty sixteen I said to
actually Arvin was the director of research, then can we
actually put our quantum computer on the cloud? Well that's
(23:17):
probably twenty fifteen, and it was always supporting that. So
as we've done more and more we've been able to
do it. It's had this program going now, I agree
is very visible, like because we're in this scaling phase
and so we're invested to keep scaling it and to
get why is At IBM research, what we always do
(23:40):
is answer what is the future of computing? Whether it's
coming up with new algorithms, coming up with better AI,
coming up with quantum, or coming up with just how
do different accelerators go together. It's our DNA to answer
the question of what is the future.
Speaker 2 (23:56):
Need a perfect problem for IBM because you kind of
need to have a legacy of buildings, building actual physical machines.
Speaker 3 (24:04):
Yeah, it's why I came to IBM. I wanted the experience,
the culture of building hard things that others have not
done before.
Speaker 2 (24:16):
Where do you imagine we are in the timeline of
this technology? It will come a point when it will mature.
My cell phone is a mature technology at this point.
How far are we from that point with condom?
Speaker 3 (24:29):
So I think there's various aspects of it. So we
set in twenty and seventy we set our goal that
in twenty twenty three we would be able to build
a machine that was beyond classical computers to simulate it,
and we achieved that in twenty twenty three. So to
run a bigger we call it a quantum circuit. The
details of it din't matter, but to run a quantum
(24:51):
workload that if you were to simulate that workload how
a quantum computer operates on a classical computer, you couldn't
do it. So we set that does our first and
now I've made it publicly that by twenty twenty nine
we'll build the first fault tolerant corantum computer. That is,
one that can completely handle the noise to the level
(25:13):
to allow you to run a very very large, large problem.
Speaker 2 (25:16):
So an example of a large problem.
Speaker 3 (25:18):
Yeah, a large quantum problem. So for around a couple
of one hundred cubits and one hundred million operations, you're
talking still interesting science problems like simulating a molecule, or
calculating a small optimization problem, or calculating say some part
(25:39):
of a matrix update in some type of differential. So
it'll still be scientific, but it'll be at the point
where it's beyond, well beyond any classical approximate method. And
then I think that's twenty twenty nine that's twenty twenty nine,
so we're four.
Speaker 2 (25:55):
Years away from something that can start to handle.
Speaker 3 (25:58):
Interesting problem, serious problems. I do believe the scientists will
find interesting heuristic problems before that, and so over the
next four years, you're going to continue to see more
and more let's call them heuristic not provable quantum problems
that run on quantum computers that come out. We're seeing
more and more come from many of our partners and ourselves.
(26:19):
Heuristic problems have value, but they have to be tested,
they have to stand up over time. You have to
run them many, many times, you have to try different ones,
and many times heuristic can lead to formal problems. So
you're going to see because we're beyond now the point
that you can simulate these quantum computers with any classical computer.
They're kind of like a scientific tool. So they're exploring
(26:40):
the heuristic.
Speaker 2 (26:41):
What do you have to get done between now and
twenty twenty nine to get there?
Speaker 3 (26:44):
So we had to reinvent how we wanted to do
error correction. So we have to demonstrate modules and if
we can demonstrate these error corrected module and our goal
is actually it's called Crookobarro I name all our chips
after birds, so it's called Kookobara. It is named after
an Australian verte. I think I still say kooka burrow
the way Australians do. We need to then show that
(27:07):
we can make a single module and then we want
to connect two of those modules together, and I call
that one cockatoo, which is another Australian vert. And then
if we can do that, so that's twenty six and
twenty seven, and then we want to scale the scale
those modules, and that we call starling and we want
to scale that in twenty twenty nine. So get a module,
(27:28):
join two modules together and scale and so each module
is going to be around one thousand cubits.
Speaker 2 (27:34):
The challenge to getting there is it finding the right
material or how would you describe what? That's the beauty
to be done.
Speaker 3 (27:41):
That's the beauty of it is if we would have
been here two years ago, I couldn't tell you how
it would be done. So we had a huge breakthrough.
We came up with a new code, a new quantumeric
Russian code, and that code. The biggest im part of
that code that is the most important is it's modular
(28:01):
in nature. So previous codes without getting too technical, they
were very monolithic and you had to build a very
big device, and I wouldn't have known we would have
to invent tools like new Simos tools to do that.
So we came up with this new code. We started
on twenty nineteen, we published in twenty twenty four. We
(28:21):
kind of had most of things worked out in twenty
twenty three. That's why we got confident to release the thing.
So the biggest breakthrough we had is coming up with
a code that's modular in nature, and think of that
as a like a blueprint. And so now we have
the blueprint, and now we're doing engineering tasks to implement
every part of that blueprint.
Speaker 2 (28:42):
And so the minute you had that breakthrough, then you
began to have confidence at something exactly these goals could
be met.
Speaker 3 (28:48):
And then you can't. And then anyone that's done engineering
will know what I'm talking about when I say this
is cycles are learning. It takes so long from test
idea to build two tests. In hardware, the cycles of
learning are much much lower than software, Like you can
be really really faster in the software. So then we've
planned out our iterations over the next few years, and
(29:12):
so we have to successfully demonstrate them. I may slip,
because sometimes you may estimate your time wrong, but we
now have exactly what we want to do for the
next four years.
Speaker 2 (29:25):
I want to go back to that breakthrough for a moment.
What does the word breaks we mean in that context, Like,
it's not that you get a call in the morning
from somebody who says, I did it. Do you see
it coming? Or is it a surprise when they get there.
Speaker 3 (29:37):
So the way this one worked is Sogo Brave, who's
an algorithm person at IBM, one of the smartest and
quantum information.
Speaker 2 (29:45):
Don't mention his name to everyone value you'll come for him.
Speaker 3 (29:49):
Everyone in quantum already knows his name. I don't think
there's an idea that has not originated from him in quantum.
So we're looking at other codes and we'll go all right,
we've got to get serious about these codes. And others
were starting to propose to bring these and then we
call them LDBC codes from the classical space into the quantum.
(30:13):
And I asked him, we need to get ahead of
this and understand what they're doing it. He's like the
most modest perfuse late, Jay, let me learn about them
and I'll generate a report for us and we'll read
through it. And then I said, great, Then I don't know.
Six months later, he comes back with one hundred page
report on everyone. Everyone had done an LTPC codes. I'm like, awesome.
(30:35):
So I started then to read from them. And then
we said, all right, how do we under the assumptions
of the hardware we can build? Can we get an
LTPC code knowing what we can build? And that's a
great question, and so we put a small team together
to investigate and honestly took two to three years, and
(30:58):
we iterated, and we used the constraints, so we had
the sort of theory, and then we had the constraints
of what we could build. And we iterated for a
few years, and then at the end of that we
came out with a solution that, yes, it is possible
to meet all the constraints of the hardware and build
a code that will work.
Speaker 2 (31:18):
I'm just curious about So you had this task, this
problem you want to solve, and when you set out
on the task of trying to solve the problem, what's
your certainty level that you'll get a solution.
Speaker 3 (31:31):
Well, that's the beauty of science. For things where you
kind of have a few ideas. My philosophy is try
a few for the ones that need to be in
that like wow moment. It's honestly, you've got to set
the ambition really, really high, but know when to stop.
It was a great team that went together to get
(31:53):
that breakthrough, and we knew that we needed to come
up with a code that met the requirements of the experiment.
And I think what was different before then is the
theorists that were doing error correction codes didn't necessarily know
the constraints of experiments, so it was like really more
pen and paper. So this became one all right, given
(32:15):
these sets of constraints, is it possible?
Speaker 2 (32:18):
When that's questions about this? Sorry, And I love these
kind of moments when things become clear. At the time
the problem was solved, were you aware of the implications
of the solution or did that takes you knew exactly.
Speaker 3 (32:31):
What we set out exactly like either we were going
to have to work out how to cool down a
very large piece of silicon, which would require a lot
of engineering and building tools beyond what anyone has ever
built in the silicon semoss industry. To implement the known
codes or we had to come up with a different one,
(32:53):
and once I knew that we had one that I
didn't need to reinvent any tools to build. The implications
are clear how.
Speaker 2 (33:01):
Much time elapsed between the time you heard the problem
was solved and the time you told Arvin Krishna, the CEO,
the problem was solved.
Speaker 3 (33:10):
I'm sure the next time I spoke to him, I update,
but I don't remember. The beauty of Avin is he
trusts the scientists will do it, and so he doesn't
really check on us. We update him when when it
is and he he empowers us to do really hard problems.
Speaker 2 (33:24):
Yeah, so let's talk about uses. I mean they're really
like cool, big shiny machine. I think you'll get paid
twenty twenty nine. But there's all kinds of really interesting
problems you're already working on.
Speaker 3 (33:37):
Yes, this is like another interesting area is I can
prove in pen and paper algorithms that we want to
run that. Like, it's not that we don't know what
to do with a quantum computer. There are hundreds of algorithms.
So you can go to I think it's called quantumzoo
dot com and you can see many many algorithms people
are coming up with more of more of them that
(33:59):
they prove by pen and paper. But imagine now we
have a machine that you can't simulate. How do you
actually discover algorithms in a scientific way? How do you
look and discover algorithms using a quantum computer. We're in
this exciting period right now, and so even though I
(34:21):
can prove these ones that we can run in the future,
there's a big white space between what the machines we
have and we're going to build and continue to do
and those ones that want the provable ones. And I'm
an optimistic person by nature. I think getting those machines
in the hands of students to explore and look at
(34:42):
heuristic algorithms. So looking at the equivalent of doing numerical
algorithms on computers, which there's many histories of numerical algorithms
being discovered on classical computers before we had formal proofs
that we rely on today people would even are you
the way AI works was driven numerically, even though we
(35:04):
have input into it. There are ones in optimization driven numerically.
We are entering that phase. So the computer scientists now
need to go play with these primitives. Our prediction is
over the next couple of years, we're going to see
valuable numerical equivalent algorithms emerge. And where the scientists are
(35:25):
going is in four categories. One is simulating nature, so
looking at either hay Enerji physics, chemistry, light problems. As
an example, with our partners in Japan, they took one
of our quantum computers and for Gackle, a very large
classical supercomputer, and they ran a problem where quantum was
(35:47):
just a sub routine of the problem that was running
on all of for Gackle, and they were able to
look at an interesting molecule, a molecule that if you
would go by pan and paper you would have said,
it's going to take me a very long time to
run that. They were able to on that quite accurately,
heuristically and already get results that are comparable with the
best classical methods. So they are extremely excited because they
(36:08):
want to push that further, and they're sort of showing
that you can take a classical supercomputer with quantum as
a subroutine and start to push the level.
Speaker 2 (36:16):
They were This was trying to solve a medical problem.
Speaker 3 (36:19):
Is this one is a like most people don't realize,
like iron sulfur, just something as simple as iron and sulfur,
we can't solve that exactly, Like iron sulfur, molecules are
too hard. So really small small molecules are really really hard,
too hard for classical computers to solve. People think we
can solve a lot of things. It actually turns out
(36:41):
we can't solve very much.
Speaker 2 (36:42):
You say solve ins instance, you know precisely how that
molecule works and is constructed.
Speaker 3 (36:47):
No, precisely what the energy levels of that molecule is
and how they come together, and then be able to
do that on a classical computer and compare it to
a quantum.
Speaker 2 (36:57):
It would be really really useful to know that specific.
Speaker 3 (37:00):
Because if you can have energy levels, then you can
estimate reaction rates. If you can estimate reaction rates, you
can see how different types of chemicals will react. That
can then lead to better informing eventually how to build
materials or even drug design. I just want to be
careful and not say, oh, we're going to solve drug
design or that, because there's many scientific steps to make
(37:24):
that so and so what quantum gives you as a
different tool to give you more accuracy and then lead
to making the different methods work.
Speaker 2 (37:33):
You can subcontract out aspects of a problem quantum right now,
and that just gets you further along and you would.
Speaker 3 (37:41):
Have been so at the moment. Even this result still
does not beat the best approximate classical method. It's comparable.
So the art of chemistry for the last hundred years
has been about approximating. So what we've done is we
have got very very good are coming up with ways
(38:01):
of approximating nature. And a lot of the things that
we do and we exploit and we use to estimate approximations.
They don't a stimulate nature of the way nature is.
They approximate it. And there's I could list many different
acronyms of different methods that go into approximating nature. What
quantum gives us is to eventually get beyond that approximation
(38:25):
and do it the way nature works. And so we
aren't beating those approximation methods. And this is why I think,
this is why it's still in the science. But they're
getting comparable. Getting comparable with a new tool where the
previous tool is a dead end makes scientists very excited. Yeah,
that nuance is where it is, and so that's in
machine learning, sorry Hamiltonian. Then there's examples in differential equations,
(38:49):
So can I actually come up with differential equations and
solve them? And if I can solve them, you could
look at things like an avious Stokes equation goes into weather.
There's financial differential equations that you can better predict. So
differential equations. There's many different examples there. And then I
would say that two others are optimization, and then there's
quantum versions of machine learning that are very exciting as well.
Speaker 2 (39:13):
Cleveland Clinic one of the organizations that you guys have
worked with. Why would the Cleveland Clinic be calling you up?
Speaker 3 (39:19):
Because that problem that they want to look at. So
they've also done similar problem to the recent lab. So
they've taken that method now and they've looked at molecules
that matter for drug design. So they're fundamentally looking at
those molecules that matter for eventually replacing some of the steps.
(39:39):
So they're investing to see how reliable it can be done.
And so there's a scientist there that's done many iterations
now using the techniques that were done first with the
team in Japan. They've now replicated that for new molecules
that are essential primitives for eventually designing drugs and things
that may matter for medical.
Speaker 2 (40:00):
And also there's some finance firms yep, HBC, Vango, yep,
and their interest is.
Speaker 3 (40:06):
What so that was the differential equation and optimization. So
if you are doing very large calculations like risk portfolio,
or if you want to model the Black Shaws equation
or things like this that are fundamental for them to
make better predictions, come up with better trades and things
like this. That is a very hard computational task. And
(40:28):
so rather than quantum replacing that whole problem, can quantum
be a subroutine in there? And what HSBC showed is
they showed they could take their real data, they could
take their real classical method and they just replaced a
tiny part of it. They replaced a tiny part of
it with a quantum subroutine that allowed them to come
up with better predictions of the weights that then when
(40:51):
they were to compare TRIALA versus Trial B, it was
thirty four percent better at predicting algorithmic tron And that's
a big deal for them.
Speaker 2 (41:00):
It's huge.
Speaker 3 (41:01):
Yes, Now, do they need to do more trials? Do
they need to see is this a heuristic algorithm? Do
we need to be careful? Is there other classical algorithms
that go into these are great questions that are now
being investigated. So think of this period of heuristic algorithms
is really a period of scientific discovery using these machines,
(41:22):
knowing that we want to continue and build the ones
which have determinist their algorithms that can run.
Speaker 2 (41:29):
Do the people who would profit the most by starting
to run quantum experiments realize that they would profit so
much from running quantum experience And does the world know this.
You've given us a couple of specific examples, but generally speaking,
there must be a very large universe of people who
could gain from at least starting to play in the space.
Speaker 3 (41:52):
So the enterprises that use computation as key for their
survival understand the limits of classical computation and they're very
interested to get started. The universities are very interested. Could
we get more students doing more algorithms? One hundred percent?
Some of the limitations on the rate of algorithm discovery
(42:14):
is because people are thinking through the classical way of
writing algorithms. My belief is yes, So this is why
we want to get more and more students and things,
because it's just starting. But I would say in general,
most people are aware of it. Could we get more,
could we accelerate it?
Speaker 2 (42:30):
Yes?
Speaker 3 (42:30):
Do we need to make better hardware, do we need
to come up with better libraries, yes? Do we need
better software yes, But it's all happening over the next
few years.
Speaker 2 (42:40):
Is it hard to get someone who's spent their entire
life thinking in terms of solving problems to classical means
to make the transition to this new paradigm.
Speaker 3 (42:48):
There's a lot of examples when you approach something with
the classical intuition, it's not the right way to do
it when you approach it through the quantum. But if
people are being taught to understand the fundamentals of the math,
then a lot of the techniques carry across. I don't
recommend people need to learn about entanglement or supersition because
(43:11):
whilst the physicists will argue like spooky action a distance
and all these type of things, entanglement is the power. Yes,
that's how physicists are labeled. How quantum is different. But
I would say, do we need some physicists really worrying
thinking about that?
Speaker 1 (43:26):
Yes?
Speaker 3 (43:26):
But We need more applied mathematicians that are realizing they
can use this as a as a different way of
looking at the problems.
Speaker 2 (43:33):
Yeah, I when I asked you one question, No, we're
describing a a It's more than a new technology. We're
talking about a new paradigm. It's a way of thinking
about problems. Can you compare this to kind of previous
technological paradigms. If I'm thinking at the last couple hundred years,
what does this rank in terms of a new field
(43:55):
that we've opened up.
Speaker 3 (43:57):
It's a hard question to answer, but I often say
the history of computing, this will be the first time
that computation has branched between classical and quantum. I like
thinking reading a lot in the past. One of the
things that I think was a way we changed as
a society was the invention of zero. Before zero, math
(44:19):
was limited. Realizing that numbers have a number as zero
allowed us to develop a whole set of new mathematics
that then went on and defined like everything from waves
to calculus to all of that. Yes, we can describe
it with that same math, but when we describe it
with that math, it gets exponentially big and gets impractical
(44:41):
to do. Now we can actually work on it. I
would say, if I had to give you a quick answer,
maybe going all the way back to when we were
accepted zero.
Speaker 2 (44:52):
I thought you were going to say, like the airplane,
but in fact, yeah, you went several orders of magnitude
beyond that.
Speaker 3 (44:57):
Yes, but I think it's so fundamental.
Speaker 2 (45:01):
This is absolutely fascinating. Thank you so much for chatting
with me about it.
Speaker 3 (45:05):
Thank you. Fret time.
Speaker 2 (45:08):
Hey listeners. So normally we end this episode here, but
the Tech Week attendees asked Jay some really great questions,
questions I wish I'd asked, so we wanted to include
those here. Enjoy.
Speaker 5 (45:21):
Hi, J, thank you so much for the great presentation.
My name is Trixie Apiado. I work for Willis Towers Watson,
an insurance broker. I help seisos identify and quantify their
cyber risk so they can prepare for threats before they happen.
And so quantum threats keep me up at night. You
mentioned so many good problems that quantum can solve. It
(45:43):
can also break encryptions in our classical computer systems. So
what safeguards or policies do you implement in your teams
to build quantum capabilities responsibly and what can we do
for people in this room, US builders and users to
secure our data in systems before quantum computers become more
(46:06):
energy efficient, cheaper, and more available.
Speaker 3 (46:09):
So it's a great question. So yes, one of the
algorithms for quantum computing is to break our traditional encryption.
So at IBM Research we were aware of this from
day one. We've come up with algorithms that we believe
and have very strong evidence will not be broken by
a quantum or classical computer, and has selected them. So
(46:34):
first the scientific technical question, security is saved. There are
algorithms that exist that we can implement that neither a
quantum or classical computer can break. So the technical answer
is we're all okay. The more complicated answer is a
social and society answer. Encryption was built in classical computing
(46:57):
in a way that was never thought of being grade it.
It's mixed everywhere. Some of it is downstream, some of
it is like software that you may use, Some of
it is software that you've developed. And I get that
if you've got a product and you want to have
it secure for the next ten years, you probably want
to think about how you're going to upgrade it, or
(47:19):
if you have data that needs to be secure for
the next ten years, it needs to upgrade to new encryption.
So the real challenge is more of a social business
problem of how do we actually transition from old encryption
to new encryption knowing this is going to happen. So
we at IBM have been very proactive on this. We've
(47:40):
developed tools where we can determine where encryption is used,
We've developed tools which can show you how to replace it,
and we early on have made sure the Mainframe when
we made these algorithms. So I think it was Z
sixteen that was the first version of the Mainframe to
have these quantum safe algorithms implemented. So my answer to
(48:03):
your question is, yes, there's a real problem, but it's
not a technical problem. It's a social and business problem.
And I'm not minimizing that. I understand that that is
a lot of work you need to start now. You
need to come up and do a you need to
make it part of your IT transformation. You need to
get onto it. And I realize, I realize it's not
(48:27):
going to take zero time because it's not an easy
problem to do. So the short answer is one we
developed algorithms that we can't, and we're developing tools to
help you in that transformation.
Speaker 5 (48:38):
Thank you so much.
Speaker 2 (48:40):
Thank you. My name is Emma.
Speaker 6 (48:41):
I'm a product manager at Expedia, working on software side
of things. My question is around the non technical roles
outside of the researchers, the mathematicians, the builders. How can
the rest of us, whether it be policymakers, those in
the legal fields, those thinking about what use cases quantum
can solve for in a few what should we be
thinking about and how can we prepare for that.
Speaker 3 (49:04):
It's a good question. I think this is part of
the requirement of the scientists to being able to articulate
where they are. We need a forum for those type
of discussions. I think a lot of this can fit
within the forums that we already have for classical and AI,
and I think we need to just be asking how
do we actually bring them into them Because I don't
(49:25):
think of quantum as a replacement of compute. I think
of it as an accelerator that expands what is possible,
and I think we can ask those questions in those forums.
Are we doing enough now? I think I agree with you. No,
I don't know the answer to it.
Speaker 6 (49:40):
I think it's a really interesting perspective because those existing
forums do start to bring in those other fields as well,
so it could warrant the same sort of discussion.
Speaker 3 (49:50):
And yeah, acts, and I understand those forums. Right now,
AI is probably dominating and it should be like we
going through a period of time where AI is impacting society.
The technology is impacting society in big ways. So I
totally understand that most of their focus should be on AI,
(50:12):
but we should start to ask where is quantum in
that as well?
Speaker 4 (50:16):
Hi, I'm Gobi and I'm a graduating PhD student at
Northwestern and also a member of south Park Commons, which
is a fund here. You mentioned earlier that some problems
are best solved by classical versus some problems are best
solved by quantum. When we're thinking about this, if we're
not experts in quantum, but we're thinking about this from
an AI perspective, could you just clarify when we think
(50:38):
about quantum, what is deterministic and what is not deterministic.
Speaker 3 (50:43):
I think the future of computing we've got to get
our heads around is that not everything is deterministic, and
it's much more going to be probilistic. How do you
handle error bars? How do you put confidence? I think
a lot of those questions which you're referring to INAI
are going to completely imply and quantum. I actually think
it's a mistake to compare AI verse quantum. I actually
(51:08):
think of quantum as much. It's quantum verse classical compute,
and AI is going to come across on top. So
as we go forward and we get a better understanding thing,
I'm not going to say quantum is going to replace
the classical compute that enables AI, but I think some
of the math you do in AI will be able
to go to both. So what can we formally prove?
(51:31):
I can come up with a problem where I take
a circle and a color, half of it red and
half oft of blue, and then I say, I'm going
to apply an operation that takes those dots make it. Say,
let's say ten dots over here red, ten dots over
here blue, and I'm going to wind them around many,
many times. I can then show you that if you
(51:51):
feed that into a classical computer it's a classical random
number generator. You can give yourself as much data as
you want. You will never be able to say did
the red come from the left side or the right side.
You would take infinite data. It is like you would
have to break a classical random number generator. I can
show you a quantum algorithm that can do that deterministically.
(52:15):
So where we're thinking is when the data appears to
be completely unstructured or you looks essentially like a complete
random number to the classical methods, there are quantum methods
that can actually potentially find that structure.
Speaker 2 (52:34):
That's it for this episode of Smart Talks with IBM.
If you haven't already, be sure to check out my
conversation with IBM Chairman and CEO Arvind Krishna, and stay tuned.
Another episode is coming soon. Smart Talks with IBM is
produced by Matt Romano, Amy Gains, McQuaid, Trina Menino, and
(52:55):
Jake Harper. Engineering by Nina Bird Lawrence, Mastering by Sarah Buguer,
music by Gramoscope, Strategy by Tatiana Lieberman, Cassidy Meyer and
Sofia Derlon. Smart Talks with IBM is a production of
Pushkin Industries and Ruby Studio at iHeartMedia. To find more
Pushkin podcasts, listen on the iHeartRadio app, Apple Podcasts, or
(53:19):
wherever you listen to podcasts. I'm Malcolm Godwell. This is
a paid advertisement from IBM. The conversations on this podcast
don't necessarily represent IBM's positions, strategies, or opinions.
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