Hello, and welcome to the Physics World Weekly
podcast. I'm Hamish Johnston.
This week, we'll be celebrating
the winners of the Physics World
breakthrough of the year award for 2024.
This episode is supported by the journal Reports
on Progress in Physics,
which offers
unparalleled
visibility
for your groundbreaking
research.
This year's award is all about error correction
in quantum computing,
and we're honoring 2 independent
teams.
I've spoken to the lead researchers of both
groups,
and we're presenting those conversations
in 2 different episodes
of the podcast.
So it's a real bonus this week, not
one, but 2 weekly podcasts for your listening
pleasure.
In this podcast, I'm in conversation
with Google's Hartmut Nevin,
who led a team that has made a
major breakthrough
in implementing
quantum error correction
in a processor
that uses
superconducting
qubits.
In a separate episode, I chat with Mikhail
Lukin
and Dolev
Blufstein
at Harvard University,
who, along with colleagues, have implemented
quantum error correction
on an array of trapped
atomic cubits.
In principle, quantum computers can solve some problems
that cannot be computed
on conventional
processors.
However,
the quantum processors available today
are very susceptible
to disruption by environmental
noise,
and this destroys
the delicate quantum states that are used to
store and process information.
When quantum computing was first proposed,
some physicists
thought that this problem was insurmountable.
But thanks to the development of quantum error
correction,
practical
quantum computers
that can solve useful problems
could soon be a reality.
Quantum error correction works by distributing
1 quantum bit of information,
called a logical cubit,
across several different physical cubits,
such as superconducting
circuits.
In principle,
the robustness of a logical cubit should be
improved
by increasing
the number of physical cubits.
But there's a problem.
Boosting the number of physical cubits
itself
introduces
errors,
and therefore,
creating an optimal
quantum error correction system
is no easy task.
This year, we've awarded one half of the
20 24 breakthrough of the year award to
Hartmut
Niven
and colleagues
at Google Quantum AI
and their collaborators,
and that's for implementing
quantum error correction
below
the surface code threshold
in a superconducting
chip.
For the first time,
exponential
error suppression
in a logical cubit has been achieved
as the number of physical cubits
increases.
And to chat about this achievement,
Hartmut
joins me down the line from California.
Hello.
Welcome to the podcast,
and congratulations
on your team's achievement.
Thank you for having me.
So Hartmut, I suppose first things first,
with with my questions. What is
quantum error correction,
and why is it needed?
A quantum error correction
is a necessary
technology
that allows you to scale up to large
quantum computers with many cubits
that can participate in many algorithmic steps.
And and the reason
is because each
individual qubit in a quantum computer, at least
a a quantum computer that exists today,
is is very noisy or subject to failure.
So you you sort of have to club
them together to to get one good cubit,
cubit. Is that how it works?
Yes. The way how I like to think
about it, people often say quantum information is
very fragile.
We need to protect it.
I like to think about it a little
bit differently.
Quantum information is very contagious.
Qubits like to talk to each other, and
they also like to talk to Qubits
outside of our chip, outside of our control,
and that leads to leaking information,
leaking out of the processor, and we need
to prevent it. So
quantum error correction is really a set of
technologies or a technology
that allows us to
control all the information
necessary for a quantum computation.
I see. Okay. And and in the work
that you've done with the Willow processor,
how how have you done that? How have
you dealt with this leakage of information? How
have you made sure that,
the information is where you want it to
be when you do your quantum calculation.
So,
quantum error correction,
like classical error correction,
draws on the
time tested principle in engineering.
That if you want to make something more
stable, you introduce redundancy.
Understand this is, a physics world audience, so
maybe this example is too simple. But,
often say, hey. If you want to
fly, let's say, from Germany here to LA,
if you have an airplane with 1 engine,
that will work. But 2 engines is safer.
And if you have 4 engines, it's yet
better because if one of them fails, you
still
easily make it over.
We use the same principle
in quantum error correction. So we want to
represent 1 logical qubit or the information contained
in 1 logical qubit.
So how we do this is we orchestrate
a set of physical cubits, a little array
of, let's say, 3 by 3 or 5
by 5 or 7 by 7
physical cubits
that make one better protected
logical cubit.
To our delight, what we were able, to
demonstrate
is that as we went to larger arrays
of physical qubits,
the error rate was reduced. So what we
were able to do as we went from
codistance 3 to 5 to 7,
each time as we increase the codistance,
the error rates were reduced by a factor
of 2,
effectively leading to an exponential
reduction in error rate
and therefore creating,
the most convincing prototype of a logical qubit
till to date.
I see. And is that something that surprised
you
when when you set out to do this
research? Were were you expecting to see that
that exponential
effect?
No. Theory had predicted this. It wasn't,
reduced to practice yet.
So we could actually,
predict this, quite well.
What is important if you want to achieve
such a result
is
your it's a system engineering challenge. So it's
not good enough if just your
single qubit gates are very good or just
your 2 qubit gates are very good. Your
state preparation,
your readout, all components have to be,
very well,
engineered and have to be what is called
below threshold. See, this only works,
or if you want to have more cubits
but less error.
This only works if your cubits have achieved
a certain basic quality, and that is known
as the field
as being below threshold. So if all components
of the system are reasonably good,
then you can orchestrate them into something
really very good. That is essentially how quantum
error correction works.
I see. And and and so the system
that you have available at the moment,
would you describe it as a sort of
a proof of principle
system? Or are you able to to actually
use it to solve
practical,
computing problems, and and even problems that can't
be solved
easily by a a conventional classical computer.
So
the
quantum error correction demonstration
made just a single good logical qubit.
Of course, a single logical qubit is not
good enough to run any interesting algorithm. You
will need, many of them.
And also, you need it yet better. We
achieved roughly a 1 in a 1000 error
rate,
which means that then you can
run about a 1,000 operations
in your algorithm.
Because the way you can think about error
rates, if it's, let's say, 1 in a
1000 or 1 in a 1000000, what it
means, you have your cubits, you apply your,
gates,
and then you have the 1 in a
1000 or 1 in a 1000000
chance that you
crash your machine. You get a blue screen
and you have to restart your computation.
So 1 in a 1000 error rate means
we can really run algorithms with about a
1,000 gates. So this gives you a certain
limit of complexity.
Many of the famous algorithms,
for,
quantum simulations
or factoring large numbers,
they often need way more,
gates than a 1,000 and need 1,000,000 or
even 1,000,000,000.
So, therefore, we have to,
make
yet lower error rate logical cubits. And we
can, of course, do this, rather easily in
a way because once you have an exponential
reduction,
you just ask for this algorithm
what error rate do I need. And then
you go to the appropriate code distance, which,
let's say if you want something really low,
like 1 in 10,000,000,000 error rate, that's something
we want to eventually achieve.
Then you go to a code distance of
21 or 23, or if you improve the
overall
hardware, maybe we can do it with 19
or 17. Meaning, then you have a 19
by 19
array of physical cubits that make one very
good, very low
logical error rate, cubit.
And then, of course and you don't need
just one logical cubit, but you will need,
let's say, a1000 of them. And that is
since the endpoint
of our development roadmap. Our roadmap
calls for
it consists of 6 salient milestones.
And the last milestone, at least for now
on this roadmap,
calls for building a 1,000,000
physical qubit,
quantum processor,
which would, with today's methods,
translate into about a 1,000 highly protected logical
qubits.
Now you have a system that can confidently
generate commercial value.
I see. And and can you talk a
bit about the the the physical hardware?
The am I right in thinking that you're
you're using,
superconducting
qubits. Is that right?
Yes. That's that's correct. There's a different ways
how you can represent
a quantum bit, a cubit. Essentially, any
quantum mechanical two system state
can be used.
We use superconducting
cubits.
So,
anybody here on the audience who has let's
say when they were teenager built a little
radio out of an electronics kit, they may
be familiar with LC
circuits
or electrical oscillator where you can sync the
the electrons
slash back and forth between
a capacitor and an inductor.
Our cubits are basically this LC,
circuits,
but they are superconducting,
and live at very low temperature below the
transition temperature of the superconductor.
So they
essentially implement
a quantum mechanical
harmonic oscillator.
And
people may remember from a 1st semester
quantum physics,
class that
in a quantum mechanical oscillator,
you have
discretized
energy levels.
And we use the lowest energy level and
the first excited energy level as our logical
zero and logical one.
I see. And and how how do you
connect up these,
these superconducting
oscillators? How exactly do you get communication
and coordination
so that you can do your error correction
and, ultimately,
computations.
So so there are different ways. Once you
have your,
I'll see oscillators,
your, your cubits.
Maybe one piece I didn't,
say,
that's still important.
I told you we essentially,
implement
cubits as quantum mechanical
oscillators.
And I told you that we use the
lowest energy level and see,
the next highest energy level as our 0
and 1. We do one more piece because
in a harmonic,
quantum mechanical oscillator, then there will also be
a level 2, an energy level 3, and
so on. And they all have exactly,
the same energy
distance,
hbar,
times omega.
So that is not so good because, if
I have my qubit, let's say, in the
first excited state and I send it a
pile with with just this energy difference, and
I can exactly make sure it goes back
to my 0. It might also go up
to,
level 2, and we don't want this because
this is outside of the code space. So
we put one more ingredient in, and that
is the Josephson junction. And the Josephson junction
is a nonlinear circuit element,
and it makes our oscillator
slightly un harmonic.
What this means is now the energy levels
are not equidistant
anymore,
but the
first separation between 0 and 1 is a
little bit larger energy difference than between 12,
and that is still larger than 23. So
you get this letter of shrinking,
distance energy levels.
And that's quite useful because now we can
send in
control palaces that will only
cause transitions
between the lowest,
two energy levels and the others out of
code space are not involved. So that really
completes,
the superconducting
qubit.
Now once I have a superconducting qubit, how
can I couple them? There there are various
ways how you can, couple them. There are
capacitive
couplings, inductive couplings. You can use
little,
qubits,
in between that act as a coupler. You
they just have to get into interaction. So
you you can maybe think of the mechanical
analog, you know, pendulums,
and, you put a little spring between them,
and then they feel each other. That is
an
interaction between,
2 oscillators. So there are multiple design choices
you have. And what's the exact,
best choice for coupling is that is actually
still a bit of matter of research.
I see. And and
you mentioned that,
I suppose, your ultimate goal is to get
a a quantum processor with
that offers about a 1,000,
logical
cubits. And so so that would require many,
many more actual physical
superconducting
cubits. Is is that the sort of thing
that that can be miniaturized
onto
onto a chip? Or or would that have
to take up a, let's say, an entire
lab,
at a university?
So,
roughly speaking,
our our cubits are not super small. Actually,
if I were to give you a chip,
which has an array of cubits and you
squint, you can actually see,
the individual cubits. They are a little bit
smaller than a square
millimeter.
So, essentially,
out of these components, we would make larger
and larger arrays.
But currently,
a little bit as opposed to
classical,
CMOS,
technologies,
we are not aiming or it's not a
priority for us to make the,
cubits smaller and smaller
because,
we actually like that they have the size
because then it's easier to control them with,
microwave pulses, so it's easier to read them
out. So we would just have to make,
sufficiently large chips. Eventually, you have to, join
multiple chips
to make a chip that has the surface
area to,
cover, let's say, a 1,000 or even a
1000000,
physical cubits.
I see. And and so
where are you in in the development,
of that at the moment? Have you have
you managed to to create,
a system of integrated physical cubits that's large
enough
to give you a 1,000
logical cubits? Or is that something that's down
the road a bit further?
So I mentioned the road map that, the
Google Quantum AI team published.
And this road map consists of 6 milestones.
So the first milestone
was we achieved it, in 2019.
It was showing for the very first time
that a quantum
processor
could
compute a task in minutes. That's a zen
fastest supercomputer would have needed 10000 years to
do.
The second milestone we also achieved already was
similar to the current experiment,
was to show that as we go from
code distance 3 to 5, the error rate
comes down.
But that milestone was
defined as just being the break even point.
So, yes, the error rate did come down,
but just by a hair,
4%. So that was not that impressive yet.
So the the current,
result, which we refer to as a yard
stone, is actually between 2 big milestone
that
improved,
on milestone 2 by it's not 4% anymore.
It's by a factor of 2 that the
error rate came down and it came down
twice from, again, code is 3 to 5
to 7.
So milestone 3, which is sort of the
midpoint of our road map, will be a
single
very good logical qubit
with a 1 in a 1000000 logical error
rate.
And then from there, we,
now maybe I mentioned the remaining milestones.
Milestone 4 is then having several,
logical cubits of about that quality
and to have a gate set, a universal
set of gates,
gate operations
between
those logical cubits.
As we then scale up through milestones,
56,
we get a 100,000
or even a1000000
physical cubits,
allowing us to make,
more and more logical cubits, let's say, with
current technologies or current estimates
would be a 100 or 1000.
Most likely by the time we reach those
milestones,
quantum error correction technologies have improved further. Our
hardware has improved further,
and we get a few more logical cubits
out of a given set of or a
given number of physical cubits.
I see. Okay. And do and do you
have any any sort of feeling for,
the the timescale,
you know, when when you will get to
a situation
where
you can you you can start
connecting up these very good logical cubits. Is
that something that is that like a 5
year thing or a 10 year
plan? More about 5 years. We expect that
we will have feature complete quantum computer with
at least
a 100 logical cubits, hopefully more like 1,000
by the end of this decade.
So, I've been asked this by reporters before,
oh, is quantum computing like nuclear fusion, which
famously is
people quit,
20 years out.
Quantum computing is not like this. We publish
the road map and we pretty much knock
out the milestones as
clockwork. So we are making
good progress. Of course, it's a very ambitious
road map and could we be delayed by
a year or 2? Absolutely, that could happen,
but so far, so good. We have pretty
much stayed to true to the predicted timeline.
I see. And and when you get to
a 1,000 cubits,
logical cubits, I mean, I would have thought
that there were practical things
that you could do
with a 1,000
cubits?
You you know, may maybe not solving universal
problems, but are there are there specific
problems, maybe problems in science that you could
tackle with a 1,000 logical cubit machine?
So with a 1,000 logical qubit machine, we
certainly can do many,
useful things.
So we can start to then,
simulate
processes relevant to drug development.
We
develop algorithms that would help,
with the design of nuclear fusion reactors.
There would be applications
in
making
batteries for electric cars
better in the sense of you can charge
them quicker or they are lighter,
less dangerous to burn. I mean, wouldn't it
be awesome if you could have airplanes
as that operate on ion air batteries? In
principle, that's possible. Those designs
have a higher energy density like kerosene.
But today, they're still too brittle.
You can't reliably put those into airplanes yet.
But with,
a quantum computer,
you can hasten the development of such a
device like,
batteries for airplanes
because you don't today, if there's an
electrochemistry
engineer and she has an idea, oh, I
think this is a better cathode material.
Let's put this in. The only way to
test it today is to build this battery,
take it to her to lab, and measure
it. That's a very slow process.
With a quantum computer, you can
simulate this in silico, so to speak, where
you
now,
simulate,
the properties of this battery and see how
fast it would charge or how quickly,
electrons
diffuse.
And then only the very best designs,
that checked out, you take into the lab,
measure in practice how well this would work.
I see. And and what about,
I suppose,
applications that maybe people would associate
with Google?
Things like, information
processing,
the optimization
of of searches,
and,
I suppose AI
when it comes to dealing with large quantities
of information.
Could a a 1,000,
logical cubit machine be useful for that? Or
would you really need
lots more cubits,
logical cubits to to do those sort of
applications?
Yeah. So what we talked about so far
is this,
application area is referred to as quantum simulation.
And we often refer to this as Richard
Feynman's,
killer app.
Because
it was Feynman
who famously,
realized for the first time
that it's actually his quote. Nature is not
classical, and damn it. And if you wanna
simulate nature, you better make it quantum mechanical.
So that's questions like the dynamics of,
chemical reactions
or
properties of magnetic materials or making very
low
resistance,
materials for
electronics.
And all these
problems
entail
quantum
phenomena or these are systems
where quantum effects are important and simulating those
well is sort of the
baseline killer app for quantum computers. But you're
in quite right. It's not limited to this
at all.
Today, we know about 60,
algorithms
that have a scaling advantage, which means,
as, the problems get larger, the quantum computer
can do it more efficiently. Meaning,
it can do the quantum algorithms, can do
it with fewer, sometimes way fewer steps than
a classical computer.
And way fewer steps, I mean, can be
exponential
reduction or a quadratic,
reduction in the number of steps
you need.
And, for example, with optimization,
which is another killer app because optimization problems
are so pervasive. They are, key in machine
learning.
They're key in engineering.
They're important in finance. There's hardly any
area that doesn't,
require the solution of optimization
problems.
And we have known since the nineties that,
for any optimization problem, you at least get
a quadratic,
speed up in
scaling.
But quadratic is not as good as exponential
and therefore,
it would
put,
quantum enhanced optimization
rather far out.
But to our delight, our team has developed
a new algorithm that's called the DQI,
algorithms, stands for decoded quantum interference.
And this algorithm,
seems
to give us an exponential
speed up
in optimization
for certain classes of optimization
problems.
We don't quite understand yet which classes those
are, but if this were to pan out,
this would be super exciting
because you can think of optimization
as puzzle solving. And let's say if you
wanted to
build an AI,
then, of course, an AI that is better
in puzzle solving, that is the one you
will wanna have. So, therefore,
my prediction is that in the future, if
you have a quantum AI playing
chess or go against an AI, the quantum
AI will win.
I see. And and
because of that exponential,
effect, do do does that mean that you
could conceivably
implement those
algorithms on a a 1,000
cubit machine? You you might not have to
wait until you've got 10,000 logical cubits or
a 1000000 logical cubits. That the it's so
efficient that you could implement it on a
small
quantum computer.
We should definitely
see first compelling examples of optimization problems in
the range of,
1,000 variables.
Of course, more is better if you could
have problems with,
10,000 or,
100,000 variables. That would be better. But but
certain optimization problems are very hard, and you
definitely can find the optimal solutions for a
1,000 variable problem.
And if we could show that the quantum
computer can
find better solutions,
don't wanna mislead,
the audience here.
It is
not known today, and many suspect it is
actually not correct,
that quantum computers
can solve very hard optimization
problems
perfectly
either.
But what they often can do is they
give you a much better approximate solutions than
what is classically attainable. So they solve the
puzzles
not necessarily to full optimality, but they can
solve the puzzles
better than classical
computers.
I see. And I don't think, Hartmut, I
I haven't asked you,
specifically about
the Willow chip. So how many,
physical cubits,
does it integrate?
So the Willow chip has a 105,
physical cubits,
and they have,
very high quality.
The physical
error rates, let's say, for our 2 cubit
gate
gates is just 1 in a 1000.
So this,
allows to run the most complex quantum algorithms
today on the Willow chip.
Well, that's great, Hartmut. Thanks so much for
coming on the podcast.
Oh, most welcome. Pleasure to be on your
show.
You're listening to 1 of 2 podcasts
with our breakthrough of the year winners.
The other features Harvard University's
Mikhail Lukin
and Dolev Blufstein,
and you can find it on the Physics
World website,
or at your favorite podcast provider.
You can also read more about our top
10 breakthroughs of 2024
on the Physics World website.
This served as the shortlist for our breakthrough
of the year award, and it covers a
range of fantastic
research in Physics.
So do check it out.
I'm afraid that's all the time we have
for this week's podcast.
Thanks to Hartmut Nevin for joining me today,
and a special thanks to our producer Fred
Ailes.
Physics World's coverage of the breakthrough of the
year is supported by Reports on Progress in
Physics,
which offers
unparalleled
visibility
for your groundbreaking
research.
You can find the journal at iopscience.org.