October 15, 2025 • 5 mins
ABC News tech reporter Mike Dobuski joins Wake Up Call for ‘Wired Wednesday’! Mike talks about takes us inside IBM’s quantum computer.
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Episode Transcript
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Speaker 1 (00:00):
Let's say good morning now to ABC's Mike Debuski and
Mike I've been telling a wake up call listeners that
we're going to have to try to wrap our hands
our heads around this one because we're not only talking
about computers, but we're talking about quantum computing. What is it?
Speaker 2 (00:16):
Yes, that's right. So quantum computing really requires us to
understand how normal computing works. But it does have potentially,
I know, it does have potentially very big implications though,
so it is important to talk about. So have you
ever seen the matrix? You know, at the beginning of
the matrix, when the numbers come cascading down off the
top of the screen and they're ones or zeros and
they're green. Those are bits, and those are basically, to
(00:39):
make a very complex story very short, the building blocks
of modern computing. Right, you arrange enough ones and zeros,
you arrange enough bits next to each other, and that
creates a photograph on your phone or a video game
on your desktop. Right, that is the building block upon
which we build our modern computing infrastructure. Quantum computing uses
different building blocks, a completely different thing. They use what
(01:01):
are known as quantum bits or cubits, as they are
colloquially known instead of being a one or a zero.
This is where it gets a little complicated. They are
kind of both one and zero and also some percentage
of the two right until you run a program. They
can exist in this quantum state where they are sort
of both but also neither, and maybe kind of more
(01:24):
of one and maybe kind of more of zero. It's
very complicated, but the bottom line is that creates an
extra level of variability for these computers, and that means
that the big takeaway from quantum computers is that they
are able to solve problems that are way more complex
than normal computers can handle, which opens up a lot
of opportunity if these things eventually mature and become part
(01:46):
of the mainstream.
Speaker 1 (01:47):
Okay, so when you say it can solve a lot
of things that regular computers can't, what kinds of things
are you talking about.
Speaker 2 (01:52):
It's a great question, because up until recently this was
pretty theoretical. For example, last year, Google announced that they
got their quantum computer to solve a problem that would
have taken a classical computer ten septillion years, right, so
they were condensing the large longer than the known history
of the universe down to basically an instant. But it
(02:12):
was a pretty theoretical math problem, right. It was this
really complex, really niche thing that everyday people wouldn't normally encounter. Well,
last month HSBC the Bank announced that they used IBM's
quantum computer to reduce the variability and unpredictability of the
bond trading market. They were able to predict bond trading
(02:35):
and the price that bonds were able to go for
at thirty four percent more accuracy. And this was a test.
It was in the European bond market. But if IBM
is to be believed, that is eventually going to be
your money that is being moved around with these quantum
computers and the algorithms that go along with them. And
then you consider the applications in things like drug development
(02:57):
or artificial intelligence. Taking again a ten septillion year long
problem and condensing it down to the flip of a
switch or the snap of a finger, what could that
do to something like developing a cancer drug or developing
an artificial intelligence model. Shrinking that progress down dramatically, it
has really big implications.
Speaker 1 (03:16):
Okay, and then here's here's my out of this world question.
So remember the show Quantum Leap. I mean they are
they thinking about things like that too.
Speaker 2 (03:25):
I don't think we're quite at the quantum leap level yet. Unfortunately,
you know, as much as we would like to leap
around time and space, you know that future, I think
is pretty far off, sir, reporting no teleporting. And it's
also worth mentioning that as much of this sort of
blue sky thinking about like AI and drugs and financial
trading and whatnot, is great, these computers are still in
(03:47):
sort of an early stage. They still throw errors at
a point one percent rate, meaning one out of every
one thousand problems we give them, it returns an error,
And that sounds like okay, but it's way higher than
what you would expect a computer, and IBM says. I
was able to talk to some of the experts at
IBM earlier this week. They told me that by twenty
twenty nine they plan to get that down to a
(04:09):
tolerance level, a fault level that is equitable with existing computers,
bringing that down to make it throw errors less and
be a little bit more reliable as a computer. Interestingly enough,
these things, as complex as they are and as good
as they are at like solving, really complex problems. They
still make mistakes, so we have to account for that
(04:29):
going forward.
Speaker 1 (04:30):
It sounds like AI like it's gonna get better, but
right now it still makes a lot of mistakes.
Speaker 2 (04:34):
I think it's also it's very comparable to AI in
the sense that, yeah, it's gonna get better, but a
lot of things need to happen for it to get better,
and there's increasing doubt that it will. Right this idea
that there's you know, traditional computer advocates out there who say,
we actually shouldn't be investing all our time and efforts
and energy into quantum computing. Our traditional computers are able
(04:56):
to do, buy and large a lot of these things,
and if we were to develop them more, they would
be able to do it more quickly. So there's this
tension in the high end computing world that kind of
mimics what we're seeing with AI, where it's just like, Okay,
we're spending a lot of money on this stuff and
it's taken up a lot of energy and we're not
seeing a ton of practical applications that we can monetize
just yet. So will that portend a bad future for
(05:18):
either of these industries? I think we're just gonna have
to wait and see.
Speaker 1 (05:21):
All right, ABC's Mike Debuski, thanks for almost making that understandable.
Speaker 2 (05:25):
Of course I do my best. No, you did great.
Speaker 1 (05:28):
It's my brain that can't grasp it. Thanks so much, Mike.
Speaker 2 (05:30):
Take care,
Let's say good morning now to ABC's Mike Debuski and
Mike I've been telling a wake up call listeners that
we're going to have to try to wrap our hands
our heads around this one because we're not only talking
about computers, but we're talking about quantum computing. What is it?
Speaker 2 (00:16):
Yes, that's right. So quantum computing really requires us to
understand how normal computing works. But it does have potentially,
I know, it does have potentially very big implications though,
so it is important to talk about. So have you
ever seen the matrix? You know, at the beginning of
the matrix, when the numbers come cascading down off the
top of the screen and they're ones or zeros and
they're green. Those are bits, and those are basically, to
(00:39):
make a very complex story very short, the building blocks
of modern computing. Right, you arrange enough ones and zeros,
you arrange enough bits next to each other, and that
creates a photograph on your phone or a video game
on your desktop. Right, that is the building block upon
which we build our modern computing infrastructure. Quantum computing uses
different building blocks, a completely different thing. They use what
(01:01):
are known as quantum bits or cubits, as they are
colloquially known instead of being a one or a zero.
This is where it gets a little complicated. They are
kind of both one and zero and also some percentage
of the two right until you run a program. They
can exist in this quantum state where they are sort
of both but also neither, and maybe kind of more
(01:24):
of one and maybe kind of more of zero. It's
very complicated, but the bottom line is that creates an
extra level of variability for these computers, and that means
that the big takeaway from quantum computers is that they
are able to solve problems that are way more complex
than normal computers can handle, which opens up a lot
of opportunity if these things eventually mature and become part
(01:46):
of the mainstream.
Speaker 1 (01:47):
Okay, so when you say it can solve a lot
of things that regular computers can't, what kinds of things
are you talking about.
Speaker 2 (01:52):
It's a great question, because up until recently this was
pretty theoretical. For example, last year, Google announced that they
got their quantum computer to solve a problem that would
have taken a classical computer ten septillion years, right, so
they were condensing the large longer than the known history
of the universe down to basically an instant. But it
(02:12):
was a pretty theoretical math problem, right. It was this
really complex, really niche thing that everyday people wouldn't normally encounter. Well,
last month HSBC the Bank announced that they used IBM's
quantum computer to reduce the variability and unpredictability of the
bond trading market. They were able to predict bond trading
(02:35):
and the price that bonds were able to go for
at thirty four percent more accuracy. And this was a test.
It was in the European bond market. But if IBM
is to be believed, that is eventually going to be
your money that is being moved around with these quantum
computers and the algorithms that go along with them. And
then you consider the applications in things like drug development
(02:57):
or artificial intelligence. Taking again a ten septillion year long
problem and condensing it down to the flip of a
switch or the snap of a finger, what could that
do to something like developing a cancer drug or developing
an artificial intelligence model. Shrinking that progress down dramatically, it
has really big implications.
Speaker 1 (03:16):
Okay, and then here's here's my out of this world question.
So remember the show Quantum Leap. I mean they are
they thinking about things like that too.
Speaker 2 (03:25):
I don't think we're quite at the quantum leap level yet. Unfortunately,
you know, as much as we would like to leap
around time and space, you know that future, I think
is pretty far off, sir, reporting no teleporting. And it's
also worth mentioning that as much of this sort of
blue sky thinking about like AI and drugs and financial
trading and whatnot, is great, these computers are still in
(03:47):
sort of an early stage. They still throw errors at
a point one percent rate, meaning one out of every
one thousand problems we give them, it returns an error,
And that sounds like okay, but it's way higher than
what you would expect a computer, and IBM says. I
was able to talk to some of the experts at
IBM earlier this week. They told me that by twenty
twenty nine they plan to get that down to a
(04:09):
tolerance level, a fault level that is equitable with existing computers,
bringing that down to make it throw errors less and
be a little bit more reliable as a computer. Interestingly enough,
these things, as complex as they are and as good
as they are at like solving, really complex problems. They
still make mistakes, so we have to account for that
(04:29):
going forward.
Speaker 1 (04:30):
It sounds like AI like it's gonna get better, but
right now it still makes a lot of mistakes.
Speaker 2 (04:34):
I think it's also it's very comparable to AI in
the sense that, yeah, it's gonna get better, but a
lot of things need to happen for it to get better,
and there's increasing doubt that it will. Right this idea
that there's you know, traditional computer advocates out there who say,
we actually shouldn't be investing all our time and efforts
and energy into quantum computing. Our traditional computers are able
(04:56):
to do, buy and large a lot of these things,
and if we were to develop them more, they would
be able to do it more quickly. So there's this
tension in the high end computing world that kind of
mimics what we're seeing with AI, where it's just like, Okay,
we're spending a lot of money on this stuff and
it's taken up a lot of energy and we're not
seeing a ton of practical applications that we can monetize
just yet. So will that portend a bad future for
(05:18):
either of these industries? I think we're just gonna have
to wait and see.
Speaker 1 (05:21):
All right, ABC's Mike Debuski, thanks for almost making that understandable.
Speaker 2 (05:25):
Of course I do my best. No, you did great.
Speaker 1 (05:28):
It's my brain that can't grasp it. Thanks so much, Mike.
Speaker 2 (05:30):
Take care,
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