Live From D-Wave Qubits: CEO Dr. Alan Baratz on Quantum's Impact, Now and Into The Future

Channel: Alex Kantrowitz

Published at: 2026-02-06

YouTube video id: _B9IBUPbIc0

Source: https://www.youtube.com/watch?v=_B9IBUPbIc0

We have a great crowd uh here today and
I know that we're live streaming this
and we're going to put it on YouTube.
So, I want everybody at home to be able
to hear how many people we have with us.
So, you got to make some noise. Let's
hear you.
>> I'm Alex Can. I'm the host of Big
Technology Podcast and I'm thrilled to
be here with Dr. Alan Barretts for a
conversation about the cutting edge of
quantum computing. Dr. Dr. Barretts,
it's great to speak with you again.
>> Great to speak with you. You're making
this sound so professional.
>> Got to get people into it. This is this
is the way we enter.
>> Okay.
>> Lot of news today.
>> Yeah.
>> Lot of news. Can you give us the
highlights
>> and what the implications are?
>> Yeah. So uh first of all uh not brand
new news but uh within the last couple
of weeks we did announce that we uh have
acquired quantum circuits uh which
brings revolutionary new gate model
technology to D-Wave and we believe uh
accelerates us to the forefront of gate
model computing alongside the fact that
we're already the world leaders in
analing quantum computers. So making us
the leading quantum computing company.
Um, we've also announced, as you just
heard, that uh Florida Atlantic
University has purchased a D-Wave
Advantage system to be placed uh at
their Booker Raton campus just uh a
little bit down the road uh for use in
uh you know, workforce development uh
application development, new science,
leveraging quantum computing here in
Florida. Uh we uh ann What time is it?
>> Come on, give us news.
>> We we We we just announced that we have
signed a new uh quantum comput as a
service deal. It's a uh two-year $10
million deal uh to work with a Fortune
100 company to basically build and
deploy uh a collection of different uh
quantum applications uh within their
environment. This is the first large
enterprise deal I believe uh ever for
quantum computing certainly for D-Wave.
Uh and it's something that I have been
talking about for a while that we were
on the path to achieve uh this
transition from you know small proofs of
concept to large enterprise deals. Uh
and then of course we just announced
that um you know we are moving our
corporate headquarters from Palo Alto,
California to right here in Palm Beach
County. and uh we're really excited to
be here.
>> Now, one bit of news that caught my
attention is that you're announcing a
partnership with Andre, the defense
technology company. Uh all these
different scenarios ran through my head
of what that could be. Is it trying to
find new types of weapons? Is it
logistics? Is it simulations? What is
that partnership going to So it it's
trying to help the US government and the
department of war solve their complex
computational problems in support of
national defense. So um you know up
until now the US government has been
mostly focused on long-term research
around quantum but our annealing quantum
computers are able to solve hard
problems today including hard national
defense problems. So this is about
working to really help address some of
those hard problems leveraging quantum
systems right now today. And I think you
know uh you heard from Dale and from
Matthew that um you know they were
surprised at how capable our systems
were, how much they are able to do right
now today in addressing some of these
challenging problems.
>> Now this is a moment where a lot of us
are hearing even those of us outside of
the quantum industry are hearing much
more about quantum. seems like every
couple months there's a new announcement
of a breakthrough or an application that
seemed long away but is now possible. So
talk a little bit about why quantum, why
now and why we're seeing this urgency
around this technology at this moment.
>> Yeah. Um so first of all I do think that
we are at the point where there are hard
problems that classical is not able to
solve well enough. Um and these problems
cut across a variety of different
application areas. And in fact, uh you
know, you saw one of the comments from
Wakefield Research that 81% of
executives think that classical is out
of steam for solving the problems that
are important to them. So there's an
awareness of the fact that um something
needs to change in the compute
environment to be able to solve these
hard problems better and faster than
they're being solved today. Um and
quantum computing is the answer to that
problem. I think the thing that has been
less well understood certainly not well
recognized is that quantum can address
these problems right now today not three
years not 5 years not 10 years in the
future but right now today that's what
our enealing quantum computers are
capable of doing. So, as word is now
getting out that quantum can solve your
hard problems today, we're starting to
see an acceleration in interest in
actually leveraging that technology.
>> And give us some examples of what
quantum computing can do that a
classical computer could not.
>> Yeah. So, one of them well
we need to divide the use cases into two
different categories. Um I call them
evolutionary use cases and revolutionary
use cases. So you said what classical
computers can't do. To me those are more
revolutionary applications. There are
applications where classical computers
are solving the problems today. It's
just they're not solving them fast
enough or they're not solving them
optimally. And so there's more business
benefit operational efficiency to be
gained with better solutions. So our
analing quantum computers currently are
primarily focused on those evolutionary
applications whether it's you in the
area of workforce scheduling which is
like what Alex Candello showed us or in
the area of manufacturing optimization
or protein folding these are problems
that are actually being solved today
classically
just not well enough or fast enough and
our quantum computers can improve. Um
then there are applications that are
beyond the reach of classical fulllap.
You can't even get near optimal
solutions. You can't even get close. We
demonstrated an example of that uh early
last year when we showed that we could
simulate properties of magnetic
materials to aid in new uh materials
design that could be done on our quantum
computer in 20 minutes, but it would
take nearly a million years on the
fastest supercomputers in the world to
perform those computations. So those are
problems that you know can't even be
addressed today. And there are others
that fall into that category as well.
some of the things I talked about before
like global modeling, global design of
drugs and so on.
>> Okay, let's go a level deeper on
something you said which is uh that this
type of computing can be used for
improved workforce scheduling.
>> I think a critic might say look you can
do pretty good workforce scheduling with
classical computers today. Why do you
need quantum to do this? So talk a
little bit about what exactly quantum
gets you that you would not get with a
classical computer.
>> Yeah, I mean let's just go back to the
demo that Alex Candella showed, right? I
mean th this is a problem of forward
placement of uh emergency services
personnel and vehicles. Now you might
think that's a simple problem right
small number of um you know service
stations small number of vehicles small
number of officers but there's so many
possible places they could be placed.
there are interdependencies between uh
the placements and uh you know servicing
crimes or domestic disputes that it's a
really hard problem leveraging our
quantum computers response time was cut
in half and that really does save lives.
So this is a big important deal.
>> All right, let's get into the
technology. I mean, you've spoken before
about the benefits of superconducting
quantum computing, and I'd love to hear
your perspective on the trade-offs
between that and other approaches and
where D-Wave strategy fits.
>> Yeah. So, uh, this is a big debate in
the quantum industry. What's going to
win? Is it superconducting from
companies like D-Wave or IBM or Google
or Regetti? Or is it trapped ion from
companies like INQ or Continuum? or is
it neutral atom from Cera or Pascal or
photonix from Saquantum or Xanadu? It's
a huge debate that's going on right now.
And the way the debate kind of unfolds
the superconducting proponents say,
well, superconducting is a thousand
times faster. It's just physics. And why
would you ever want a quantum computer
that's a thousand times slower? So, of
course, superconducting is going to win.
But then the trapped ion or the neutral
atom proponents will say yes but our
cubits are natural cubits atoms ions
photons they have much higher fidelity
so it's going to be much easier to error
correct we'll be able to get to error
corrected gate model quantum computers
much faster than superconducting maybe
superconducting will never even get
there so that's kind of the way the
debate plays out but with the
acquisition of quantum circuits we just
totally put that debate to
Because this dual rail technology that
quantum circuits has developed is
absolutely revolutionary. It is
transformational. We are talking about
superconducting cubits. So it's the
speed of superconducting thousand times
faster than the other modalities but it
has the fidelity of trapped ions or
neutral atoms very high quality cubits
making it much easier to error correct
much more efficient to error correct. So
now you know in some sense we've got the
best of both worlds. Frankly I don't
know how the other modalities compete at
this point because they don't have the
speed that our cubits have. I also don't
know how the other superconducting
companies compete because they don't
have the fidelity. Error correction is
going to be much harder for them. This
really does put D-Wave in a
transformational leadership position.
you had the road map earlier up about
how um the dual world technology will
continue to grow um and eventually link
together a bunch of cubits. So talk a
little bit about what that enables at
each stage because I found that very
interesting.
>> Yeah. So um first of all our analing
quantum computers are commercial today
4,500 cubits solving real world business
problems demonstrating quantum supremacy
on useful problems.
Gate model quantum computers are not yet
commercial. Nobody today has commercial
gate model quantum computers. They are
just not fast enough, large enough,
quiet enough. By quiet enough, I mean
error-free. Okay? And it's going to take
time to get there. Um we must error
correct to get them to be quiet enough.
We got to get rid of those errors. Uh
and we have to be able to scale them. So
most people believe and I believe that
we need about 100 error corrected cubits
to start doing useful work. Okay. Well,
in order to error correct, we need
multiple physical cubits per error
corrected cubit. And that varies um you
know depending on you know the
technology and who you talk to. It could
be anywhere from 20 cubits to 2,000
cubits physical cubits per logical error
corrected cubit. Well, we're not there
yet. Nobody is there yet. We do not have
error corrected logical cubits. You'll
hear people say, "Oh, we have logical
cubits. We have this many logical
cubits."
I want to be kind.
That is a very misleading statement. Uh
these are not fully error corrected
cubits. They are small error correction.
The number of compute um uh terms that
they can process is still very small,
nowhere near large enough to be able to
solve useful realworld problems. We need
full error correction. So until we get
there, gate model systems are going to
be in a uh research and experimentation
mode. The same true with our gate model
systems. DR8, DR17, DR49, DR181, even
DR1000.
These will be research systems that can
be used to explore the development of
new tools for writing applications, uh
potentially new algorithms with the dual
rail cubits. Having an awareness of
errors is revolutionary and kind of
writing new algorithms or new
applications that leverage that
capability will be I think an important
new fertile ground for research and
experimentation. Not until we get to the
hundred logical cubits will these
systems actually become commercial. So
we still have a few years to go before
we
>> Okay,
>> I said a few.
>> What does a few mean?
>> A few means more than two. Okay.
Um, so meanwhile there is a lot of
momentum, uh, big acquisition like you
mentioned, lots of product
announcements. Just talk a little bit
about your recent news in the context of
where D-Wave is heading and your overall
strategy.
>> Yeah. Um, so as I mentioned previously,
we have a dual platform strategy. uh we
are the only company in the world that
is significantly invested in both
analing and gate model quantum computing
able to address the full set of customer
use cases and we have a strong roadmap
for uh continuing to bring ever more
powerful analing systems to market to
solve larger and more complex commercial
problems as well as to get to that
scaled error corrected gate model
quantum computer. But this also means
that unlike any other quantum computing
company which is entirely
in the R&D and research experimentation
mode, we have commercial systems. So
we're also focused on building the
business. This is about, you know,
growing system sales. We announced
another one today. This is about growing
application development and application
deployment work. you know, we talked to
two companies today that we're working
with on that. So, it's about building
the commercial business alongside of the
R&D work and, you know, kind of driving
to longer term commercial value with
gate model systems. So, that dual
platform strategy is very important and
very valuable.
>> You made a big statement earlier, a
number of them, but one uh in
particular. you were talking about how
quantum and AI can be used together and
you said maybe all these nuclear power
plants don't need to go online or I'm
you know paraphrasing but something
along that line. Um is that is that
really the case? Because if you think
about the amount of money that's being
invested in powering up and the
electricity and the compute uh that's
going into training these systems and
you mentioned uh your technology has
already been used to train a large
language model. Is it is this really a
technology that can um support
artificial intelligence training?
>> So uh yes at the right time. Okay. So I
I understand that we have a massive
power problem today and I understand
that um kind of business as usual
solving that problem is going to be very
challenging and you know we're going to
have to see massive power generation
stations coming online in order to
address the needs. But that's assuming
current course and speed. That doesn't
take into account potential
revolutionary new technologies that can
help to address this problem. I do
believe quantum is that technology. The
early evidence from our work with
Shanogi indicates that there is an
ability for quantum to deliver
better faster trained models uh and aid
in reducing power consumption. Um but
that's just a first demonstration. Uh in
our labs today we are seeing the ability
to drain a variety of different models
leveraging our quantum systems. again
showing great efficiencies uh and the
potential to reduce power consumption.
So I do think this will be a big part of
the solution to the energy problem. It's
a question of when uh you know will it
be this year? Will it be next year? Will
it be the following year? I I don't
think we're talking about the following
year. I think we will start to see this
benefit emerge this year or early next
year.
>> Somebody better call the AI labs. Yeah.
>> Yeah. I I think at one point I said time
to short the energy stocks.
>> Okay.
>> Maybe I shouldn't say that. I don't
know.
>> It's your words. Um so there's so much
attention to quantum like we talked
about earlier and there's a lot of hype.
There's a lot of stuff that you know
maybe we we talked about earlier people
are putting out there not so credible.
How do we sort the hype from the truth?
That's a hard question to answer. Um, at
the end of the day, I think you need to
uh ask questions
and um kind of apply judgment.
If you hear
ridiculous statements like numbers like
I don't know,
you know,
a million cubits in three years. I mean,
it doesn't quite even pass the red face
test.
>> Um, but I think it boils down to asking
questions. If if you've got somebody um
kind of giving a presentation and
throwing out what you think are
outlandish numbers and they won't let
you ask questions,
that would raise a red flag in my mind,
right? You need to be able to ask
questions. You need to be able to drill
down. You need to be able to to to come
up with an informed opinion.
get academics engaged in the discussion
to help evaluate what you're hearing. We
not enough of that is going on right
now. I think there's this tendency to
just let people say whatever they want
to say and you know maybe it'll all all
be good for the industry. I don't think
that's the right answer. I think, you
know, empty promises are create
problems, right? And we must we must
hold everybody accountable for, you
know, what they're saying and what
they're doing. And there has to be proof
points. When when we talk about
something like quantum supremacy, we
publish the paper. We publish the data.
Anybody can go out and recreate the
results, right? um you know you you have
to just make sure that there's data to
support the claims that are being made
but it's just common sense.
>> Yes. So then let me ask you I mean why
should we why should we trust your
technology and the road map? Is it
because you know that there's been some
publishing that you've done or what is
the what is the pitch here?
>> Not some publishing. I mean, we we um we
publish
all the analysis, the data sets for all
of our claims, right? Whenever we talk
about a new advance, uh a new um a new
scientific accomplishment, um we publish
the results and then look at the track
record, right? I mean, you know, we've
our advantage system is our sixth
generation analing quantum computer,
sixth generation. We are we've got an
excellent track record of delivering
what we say we are going to deliver. Ask
that same question of others. Have they
delivered what they've said they were
going to deliver? Or does their roadmap
keep changing? Does the technology base
keep changing? Right? um if they're
constantly changing and not converging,
that's a red flag.
We deliver we deliver what we say we're
going to deliver. We publish results. We
publish data sets with our results. And
if you feel like there's information
that you need from us that you don't
ask.
>> Mhm.
Okay. Let's close with this. Uh there
are people on the fence about D-Wave.
What do you say to them?
>> You're nuts.
I mean, seriously. I mean, just look at
everything we've discussed here today.
Look at what some of our guests have
said. I mean, we have quantum computing
technology that is real today, that is
delivering value today, that can help
solve your hard problems today. And we
now have revolutionary new gate model
technology that you know is kind of at
the forefront with respect to what is
possible developed by you know a world
leader in this space. I mean frankly all
of the superconducting cubits that are
in use today across everybody building
superconducting systems were developed
by Rob Schulov.
>> Mhm.
>> Right. Transmon cubit. Dual rail is
where he went when he realized that
something different was required to
actually be successful.
>> Well, Dr. Barretts, I have really
enjoyed we've been speaking a couple
times over the past few weeks. I've
really enjoyed learning about the
cutting edge of quantum from you and
excited to continue to follow the
journey and I hope we can speak again
soon.
>> I hope so as well.
>> All right. Thank you very much.