D-Wave CEO Dr. Alan Baratz: Quantum Explained, Current Applications, And Future Potential

Channel: Alex Kantrowitz

Published at: 2026-01-22

YouTube video id: EOfuh_Wdshw

Source: https://www.youtube.com/watch?v=EOfuh_Wdshw

Let's dive deep into the state of
quantum computing with one of the
leaders in the space. Today we're joined
by Dr. Alan Barrett, the CEO of D-Wave
in a conversation [music] brought to you
by D-Wave. I I've been waiting a long
time for us to do a deep dive into
quantum into what the state of the
technology is, where it's going, how
it's already having an impact today. And
I'm very excited to be able to do this
show today. Finally, for [music] the
first time on our channel, we're going
deep into quantum. And Dr. Barrett,
you're the perfect guest. uh to be here
[music] with us to do it. So, welcome to
the show. Great to see you.
>> Thanks, Alex. It's a pleasure to be here
and thank you for taking the time to
have this conversation.
>> My pleasure. By the way, folks, we are
going to be [music] doing a version of
this conversation at D-Wave Cubits, the
Cubits event that's going to be
happening in Florida at the end of this
month on January 27th [music] and 28th.
So, we'll share some more details about
that. But first, let's uh let's get
right into the subject matter here and
talk about quantum. So, Dr. Barrett, I'm
going to just start, you know, really
broad and uh and and give us a jumping
off point for those who have heard about
quantum uh but aren't deeply familiar
with where the computing where where the
technology is and where it's going. So,
let's let's start with this. What is
quantum computing? Is it theoretical? Is
it something that uh you know might be
coming down the line over time? uh what
is the state of the technology and where
is it going?
>> Sure. So uh I'm going to give you an
answer that probably you won't expect
but that is that quantum computing is
energyefficient
computing for solving very hard
computational problems and I say
energyefficient computing because
quantum computers consume very little
electricity and yet they are very
powerful. um they use quantum mechanics,
for example, superposition to be able to
perform computations orders of magnitude
faster than they can be performed
classically. And that opens up exciting
new opportunities across many different
industries and across society.
>> And so then what is what is quantum
mechanics? I mean we know that with like
typical computing you know you have
yours like CPUs and switches and things
like that but what is quantum what yeah
what is quantum mechanics and talk a
little bit about how quantum computing
actually works.
>> Yeah so I mentioned superp position
that's really the best place to start
because that's in some sense the most
straightforward way to explain how
quantum computing works and why it's so
powerful. So classical computers,
today's computers, whether they be CPUs
or GPUs, store information in bits and a
bit can be either a zero or a one.
Quantum computers store information in
cubits and uh cubits can be zero, one or
both at the same time or any combination
of zero and one. And when the cubits are
in that state where they're a
combination of zero and one, we call
that superp position. Now, why is that
so important and so valuable? Well, if
you think about the way classical
computers work, when they're solving a
problem, essentially they're able to
look at one potential solution to the
problem at a time, intelligently
iterating their way through to find the
best possible solution, but one solution
after the next because the bits can only
be zero or one. So at any given point in
time the collection of the bits in the
computer show one possible solution with
cubits because we can be in superp
position because we can have a
combination of zero and one for each
unit of information. We're effectively
able to operate on many different
possible solutions at the same time. And
that's what allows us to intelligently
move to find the best possible solution
very quickly, orders of magnitude faster
than you can do it with classical
computers.
>> And then talk a little bit about just
the mechanics of this. I [clears throat]
mean, we talk about quantum, you know,
is it like, you know, deeper into the
particles of matter that we don't fully
understand yet. And how is it possible
that something can be, you know, both a
zero and a one at the same time?
>> Yeah. So that that's the nature of
quantum mechanics. Uh you know uh it's
been called spooky. Uh it's very
difficult for most people to understand
frankly myself included. Um but it's
proven to be the way the universe
operates on the micro scale. So there
are many many interesting properties of
quantum mechanics that we are bringing
into the computing environment. I talked
about superp position. Another one is
entanglement. Very interesting. If I uh
perform some action on one cubit that
can impact another cubit that might be
very far away. That's entanglement. And
we use that as a part of how quantum
computers operate. Now, as far as the
physical realization of quantum
computers, there are a number of
different underlying technological
approaches that are being pursued today
in the development of quantum computers.
One is superconducting. Um the other is
trapped ion, another is neutral atom. Uh
another is photonics. So there are many
different technological approaches that
are being used to pursue the development
of quantum computers. Each of these can
be built in such a way that we can
introduce the quantum mechanical
characteristics like superposition, like
entanglement, like tunneling and use
those characteristics to solve hard
computational problems.
Interestingly, there's a big debate
going on right now around which is the
best technological approach for
leveraging quantum properties and
building highly performant quantum
computers. Is it superconducting? Is it
trapped ion? Is it neutral atom? And I
have a very specific view on that which
uh hopefully I'll share with you a
little bit later. Yeah, I definitely
want to get into your perspective on
what the best approach is and where
we're seeing progress and uh I as far as
I'm aware there's already some uh
breakthroughs that are happening already
and and usefulness of this technology.
But you know before I get to that and I
also want to talk about D-Wave um but
the questions are just you know spurting
out here. So, so let me ask you this.
With machine learning, we had an idea of
why machine learning was able to break
out, right? There was this idea of well,
it was a conceptual thing, but then
eventually there was enough data and
enough computing power that these
theoretical uh approaches like deep
learning started to show results and
then we had this machine learning boom.
And and I'm just wondering because we're
hearing so much about quantum now and
its potential, is there something that's
happening in the world of quantum
computing that is a similar reason for
why this technology could break out?
Like what is the parallel for the fact
that there's like been more data and
more compute and machine learning? What
is the parallel with quantum computing?
Yeah. So the thing that everybody is
focused on with respect to quantum sup
uh computing is what's uh been called
quantum supremacy. The idea is can a
quantum computer solve a problem that
cannot be solved classically period. Now
there have been a number of attempts to
demonstrate quantum supremacy.
Some of them have held up, some of them
have not. But in every case, the
computation has been a contrived problem
of no practical realworld value. But
there is exactly one demonstration of
true quantum supremacy on a useful
realworld problem that was done by
D-Wave by our company. Uh it was
published in uh the peer-review journal
science about a year ago. What we did
was we demonstrated that our quantum
computers are able to compute properties
of materials in minutes that would take
nearly a million years on the fastest
supercomputers in the world. So that's
true quantum supremacy. We are
performing a computation on our quantum
computer that cannot be solved
classically. But importantly, it's on a
useful realworld problem in the area of
material simulation. And this is the
first and frankly still only
demonstration of supremacy on a useful
real world problem. So we are at the
point actually today where at least at
D-Wave we have quantum computers that
have kind of uh made that transition to
be able to demonstrate useful quantum
supremacy and that has also allowed us
to achieve uh the point where we
actually have customers that are using
our quantum computers as a part of their
business operations today. including for
example one of the largest international
airlines, one of the largest chemical
companies, one of the largest mobile
cellular carriers, one of the largest
payment companies. So our quantum
computers have not only achieved useful
quantum supremacy, but they are
commercial today. Customers are using us
today as part of their business
operations.
>> Okay. And and I think this is going to
come as a surprise to many of our
viewers because the the general
perception is and we hear it all the
time like we I was recently at Google
and they had one of their research
events talking about how their quantum
computer was able to do like one uh al
or complete an equation or complete an
algorithm 10 times 10,000 times faster
than a typical computer. But then this
next sentence is always what is like
well it's well uh in the future and
we're not there yet. Um, but you're
talking about real world applications
that are in existence today. And I think
this comes down to a little bit of the
difference in approaches that you hinted
at earlier on. There's an approach
called annealing and an approach called
gate. And we're seeing some results with
the uh analing method already. So can
you unpack that a little bit and explain
the two different approaches and why
we're seeing some results with one and
and not the other yet?
>> Sure. So uh just like a few minutes ago
I talked about a variety of different
underlying technologies that can be used
to develop quantum computers. There are
a couple of different architectures that
can be used to develop quantum
computers. The two main architectural
approaches are called annealing and gate
model. The two approaches that you
mentioned just a minute ago. Now these
are very different architectural
approaches to quantum computing.
Annealing quantum computers frankly are
an easier technology to work with.
They're much less sensitive to errors
and as a result we are able to solve
important problems without the need for
error correction which is quite
different from gate model systems which
are very sensitive to errors and as a
result you really require error
correction before you can do anything
useful with those computers. Now, we can
talk in a minute about where we are
relative to error correction on gate
model quantum computers, but for a
kneeling quantum computers, um they're
commercial today. Our current um
generation analing quantum computer,
it's called Advantage 2, is a 4,500
cubit quantum computer. It's the largest
and most powerful computer in the world.
um it does not require nor does it have
error correction but yet as I said it's
the only quantum computer on which we've
been able to demonstrate supremacy on a
useful problem and it is the only
quantum computer that is being used by
customers today commercially as a part
of their business operations now there
are many many important applications
that annealing quantum computers can
solve they're very good at solving
having a class of problems known as
business optimization problems. So
workforce scheduling is a business
optimization problem. Manufacturing
plant floor optimization optimization
problem. Logistics management
optimization problem. Many in fact
frankly most of the important hard
problems that businesses need to solve
are optimization problems and annealing
quantum computers are very good at
solving those problems. While at the
same time we now know that gate model
quantum computers are not very good at
solving those problems. So for
optimization it's annealing. Gate model
on the other hand is good at solving a
different class of problems. Gate model
quantum computers are very good at
things like quantum chemistry for
developing new drugs or fluid dynamics.
Um this is a different class of problems
a very important class of problems very
valuable class of problems gate model is
very good at solving those problems and
kneeling not so good so we have a
bifurcation in the application
environment today where there are
problems that will always require
annealing there are problems that will
always require gate both are very
important and D-wave a very important
point that I'm now about to make is the
only company in the world with a dual
platform strategy and plan. What I mean
by that is we're the only company in the
world developing both annealing and gate
model quantum computers to address the
full market for quantum our annealing
systems commercial today in the market.
We've got a strong roadmap to continue
enhancing those systems into the future
and we're now also focused on developing
gate model systems. Uh in fact we just
recently announced an acquisition in
this space that we believe has
catapulted us into frankly a leadership
position in R&D for gate model quantum
computers alongside analing.
>> All right so let's go a little bit
deeper into both. Uh I want to start
with analing. You mentioned it's really
good for business process optimization
and you've talked in our conversation so
far about how quantum computing is
really good for energyefficient uh
computations that are just that blow the
traditional computers out of the water
when they work well. Um, when it comes
to business process optimization, I'm
curious if you can share a little bit
about what a quantum optimization and
optimization done within a kneeling
computer can enable a customer to do or
can enable a company to do um in a way
that maybe traditional methods like
machine learning can't because with
machine learning like with the
predictive analytics and the m and the
um optimization side of AI that's been
something that we've seen in market for
a long time. it's done a pretty good
job. So when it comes to the advantage
that quantum might have over some of
those traditional processes, what do you
see and what do you your customers see?
>> So let me start by giving you a concrete
example or two of uh customer
applications that uh we have in the
market today. Uh and then maybe I'll
come back and share some thoughts on uh
how AI and quantum relate to one another
and potentially work together
synergistically. So, let's take BASF,
one of the world's largest chemical
companies. Um, they need to fill orders
for customers for a variety of different
types of chemicals that frankly are
created by um a manufacturing process in
their facilities starting with a variety
of raw materials that come together to
form those chemicals.
Okay. The challenge is to uh optimize
how the plant floor operates to fill
those orders as quickly and efficiently
as possible. Um leveraging our quantum
computers, they have been able to reduce
the time required to do the production
scheduling from what was 10 hours down
to seconds. Okay, so a very significant
reduction in the time to do the
scheduling. Well, if the computer is
running for 10 seconds versus 10 hours,
that's consuming a lot less electricity.
So there you have an example in the real
world of the use of quantum computing to
deliver business value and reduce
electricity consumption. Another example
is a large mobile carrier in Japan, NT
Dokamo, has used our quantum computers
to optimize cell tower resources.
Basically, how the cell towers interact
with the mobile phones to essentially
determine which towers should be in
control as the phones are moving about.
leveraging our quantum computers,
they're actually able to support up to
10% more phones per tower by optimizing
how the control signals flow, which
means a significant reduction in
infrastructure cost for them. So,
another example of real business value
through computational optimization
leveraging quantum computers. All right,
let's now talk about AI and quantum
computing and how they relate to one
another. Um, I like to think about it as
falling into two categories. One,
AI and quantum computing are very
complimementaryary, each good at solving
different portions of a problem. So for
example, you might use AI to predict
demand for some future products and then
use quantum computing to optimize the
supply chain to meet that demand. Here
we have AI and quantum computing working
side by side, each focused on the
portion of the problem that it is best
at addressing. The second area where
there's synergy between AI and quantum
is the use of quantum to improve how we
do AI model training and inference and
specifically being able to do it with
less electricity consumption. I mean if
you ask anybody what's the biggest issue
with AI today, they'll say power
consumption. I mean that's why we've got
companies out there talking about buying
nuclear power plants. Okay. Well, what
if you could train models better,
faster, and with a lot less energy
consumption? That would be very
transformative. Well, we at D-Wave have
the first commercial example of that.
It's work that we did uh with a company
called Shiionogi. They developed a large
language model for generating molecular
structures and they're looking for new
molecular structures that are well
suited to human drugs and they've been
working with purely classical GPUbased
training up until recently. They've now
introduced our quantum computer into the
model training process and they are
finding that they are getting better
models trained much faster and by better
I mean many more of the molecular
structures are well suited to human
drugs. So here we have quantum computing
benefiting AI by making training and
inference even more efficient and
consuming less power.
>> Okay. And this is all on the analing
side. So
>> this is all on the analing side. So far
everything that I've talked about with
respect to applications is on the
annealing side because the annealing
quantum computers are the only ones that
are large enough and powerful enough to
solve real world problems to date. Gate
model quantum computers are still fully
in the R&D phase. They are research
prototypes being used for research
experimentation.
as we continue to address some frankly
very hard problems that still need to be
addressed to error correct and scale
gate model systems
>> and I want to hear a little bit about
the potential of gate model systems. You
talked a little bit about how it can
help us um you know with chemistry
research for instance but I'd love to
hear like a fleshed out thought in terms
of like where this technology actually
makes a difference once it's ready and
then just give us your best guess. I
mean, we everyone talks about the power
of this technology, but it's far in the
future and there's there's very little
information about how far in the future
it actually is. So, when do you think
it's actually going to be production
ready?
>> So, you'll hear a lot of different
viewpoints on how long it will take to
get to the point where we have truly
error corrected scaled gate model
quantum computers large enough to
actually be able to do useful work.
You'll hear predictions that range from
three years to 25 years. So that's a
pretty broad That's
>> a heck of a range range. Yeah, exactly.
Now, anybody that's telling you three
years
is smoking something
>> because we still have some very hard
problems that need to be solved around
error correction and scale. And at
D-Wave, we're working on these systems
alongside many other companies that are
working on gate model quantum computers.
So we understand the challenges very
well. We think we're looking at 7 to 10
years to get to the point where we have
truly error corrected scaled gate model
quantum computers that are capable of
doing things like helping you develop
designer drugs. A drug designed for you
to dramatically improve your quality of
life or for everlasting batteries so you
don't need to worry about uh oh my cell
phone just ran out of power. I got to
find some place to charge it. um or very
lightweight materials. These are all
things that gate model systems will be
able to help us address as we look to
the future. But as I said, gate model is
still in the R&D phase. We still have a
fair amount of fundamental
uh in uh science and engineering work
that needs to be done. And you know, we
at D-Wave just announced an acquisition
of quantum circuits uh a week ago that
we think has moved us from a participant
in the gate model space to a leader in
the gate model space. That was a very
exciting acquisition for us.
>> Yeah, I definitely want to talk a little
bit about how the gate model technology
enables some of the things that you
talked about like personal drug
discovery. Um but but I do want to get
to this acquisition first. So talk a
little bit about how the acquisition
changes uh what D-Wave has been doing on
the gate side and what you think it will
enable.
>> Yeah. Okay. So we're going to have a
little bit of fun now. So first of all,
as I've said, in order to get to the
point where we have really useful
commercial gate model quantum computers,
we need to solve error correction. So we
need to get to the point where we are
able to uh correct the errors to the
point where they don't destroy the
computation and we need to scale them
right we need to scale them to thousands
or hundreds of thousands or millions of
cubits. So error correction and scale
and um those are both hard problems. At
D-Wave, we've done a lot of work on how
to scale systems. Talk about that in a
minute. But the acquisition of quantum
circuits was really oriented toward
error correction. Now, I said I'm going
to have a little bit of fun. So, let's
uh let's get into it. Um there is a
debate going on in the quantum industry
around which is the best underlying
technology for building a gate model
quantum computer. Is it superconducting?
Is it trapped ion? Is it neutral atom?
Is it photonic? Is it any one of a
number of other approaches? Now
typically what you'll hear from the
trapped ion and the neutral atom
proponents is that our cubits are
natural cubits ions atoms and as a
result they are very high fidelity. Um
you know we have one cubit uh one cubit
gate fidelities of 99.99.
uh we have two cubit fidelities of 99.9
whereas superconducting fidelity is much
lower. But when you talk to the
superconducting folks they'll say
there's no way trapped ion or neutral
atom is going to win because our speeds
are so much faster. The speed of doing
computation on a superconducting quantum
computer is a thousand times faster than
the speed of doing computation on a
trapped ion or a neutral atom uh quantum
computer. That's just physics. It's the
speeds of the gates. Okay. So, which is
it? Is it that the uh superconducting
error correction is going to be really
hard because the fidelities aren't as
good as with trapped ion or neutral atom
and so we may never get error corrected
superconducting
or is it that ultimately we'll error
correct all these different technologies
but the gate speeds for superconducting
being a thousand times faster means that
it's going to win. Ah, well, we just
changed the equation. We just changed
the game. The acquisition by D-Wave of
quantum circuits gives us a cubit
technology that is very
transformational.
It's a superconducting cubit. But here's
the key thing. These cubits have
inherent error detection. And what that
means is these cubits have the same
fidelity as trapped ion or neutral atom.
So we now have superconducting cubits
that are a thousand times faster than
trapped ion or neutral atom. But because
of their inherent error detecting
capability, they have cubit fidelity
just like trapped ion and neutral atom.
Ah that's the best of both worlds.
that's going to win in my view. There's
no way around it. We've got highfidelity
cubits and very fast cubits. That's why
this acquisition was so important and so
exciting for D-Wave. It really will
allow us to drive error correction on
superconducting cubits much more
efficiently and much faster than anybody
else in the superconducting space is
able to do it. Then we go back to scale.
Once we error correct, we still need to
scale these systems up to large numbers
of cubits. Well, at D-Wave, we've been
working on control of large quantum
computers for many years. Our analing
quantum computers have 4,500 cubits and
we control them with about 200 IO lines.
Okay, let's think about those numbers
for a minute. We control 4,500 cubits
with 200 IO lines. If you go into the
gate model industry and you ask how many
IO lines, how many control lines does it
take to control your systems? They will
say three to five lines per cubit. Well,
if we were controlling our annealing
systems with three to five lines per
cubit, that 4500 cubit system would
require 12 to 20,000 IO lines. We do it
with 200. Basically, we've developed
what we call cryogenic control or onchip
control. The ability to control very
large quantum computers with a very
small number of control lines. And
that's the key stumbling block to
scaling gate model systems. So now at
D-Wave, we've got this amazing new cubit
technology from quantum circuits that
will allow us to error correct much
better and much faster on
superconducting systems than anybody
else can. and then we apply our control
technology to be able to scale. We've
nailed the two big problems with respect
to commercial gate model systems.
>> All right. Now, help us dream a bit
before we leave here. Uh when you talk a
little bit about the potential for this
technology, personalized medicine, uh
new chemistry, what what about this
technology enables those type of things?
>> Yeah. So um
we today don't have enough computational
power in classical computers to
basically uh simulate and determine
properties of molecular structures
that's just beyond the reach of
classical. So the only way we can
develop new drugs is by um kind of
making them and testing them, right? We
can't do it digitally. We can't do it
computationally. Quantum uh classical
computers just don't have the
computational power to be able to do
that. Quantum computers do. What this
means is that we'll be able to explore
new molecular structures
um much faster, much more efficiently
than is possible today, allowing us to
find these amazing new drugs or you know
amazing new um um materials uh that can
create all kinds of interesting things
to benefit society.
Okay, I want to hear before we go how
people can see quantum computing in
action and can you tell us a little bit
more about what's going to happen at
Cubits, the event that I mentioned at
the top, January 27th and 28th in
Florida.
>> So, Cubits is Dwayne's yearly um
customer conference. Um, it is where our
customers come together to talk about
the amazing application work that they
are doing and where we D-Wave spend time
talking about our products and our
product roadmap for the future. So, it's
a an excellent venue for anybody who's
interested in learning more about D-Wave
and where we're headed both from an
annealing perspective and a gate model
perspective as well as the kinds of
applications that we are enabling and
hearing directly from our customers
about the benefits that they are seeing
and leveraging our quantum computers.
And of course uh this year is going to
be quite exciting because we did just
announce the quantum circuits
acquisition. So this will be the first
year where you know we're spending a
fair amount of time on gate model
alongside everything that we're doing in
the analing space. And as you said it's
taking place on January 27th and 28th in
Boca Raton, Florida. And if you're
interested in, you know, what we're up
to and what our customers are up to,
this is a great opportunity to learn
more.
>> Yeah, I'm really looking forward to it.
I know that we're going to do a fireside
chat together. I'm also going to be
there for both days. And I I'll admit
I'm getting my feet wet when it comes to
Quantum. I think by the end of those two
days, you know, my goal is to be up to
speed on what's happening and [music] I
think it's going to be a great
opportunity. So, Dr. Al Barretts, thank
you again. Thank you. Great to great to
have you on the show and I'm looking
forward to continuing our conversation.
>> Thank you, Alex. I appreciate the time
and I'm really looking forward to
spending time with you face to face in
Bocaratan on the 27th.
>> Likewise. All right, everybody. Thank
you for watching. We'll have more
Quantum content on the channel, that I
promise. [music] So, thank you for
watching and we'll see you next time.