Hello, and welcome to the Physics World Weekly
podcast. I'm Hamish Johnston.
This week, we're celebrating the winners of the
Physics World
breakthrough of the year award for 2024,
and we have 2 podcasts
for your listening pleasure.
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 are honoring 2 independent
teams.
I've spoken to the lead researchers
of both groups, and we're presenting those conversations
in 2 different episodes.
In this podcast, I'm in conversation with Mikhail
Lukin and Dolev Blufstein
at Harvard University,
who, along with their colleagues, have implemented
quantum error correction
on an array of trapped
atomic cubits.
In a second podcast,
I chat with Google's
Hartmut Nevin,
who leads a team that has made a
major breakthrough
in implementing
quantum error correction
in a processor
that uses
superconducting
qubits.
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.
This year, we awarded half
of the 2024
breakthrough of the year to Mikhail Lukin and
Dolev
Blufstein,
and colleagues at Harvard University,
the Massachusetts
Institute of Technology,
and CUERA Computing.
And that's for demonstrating
quantum error correction
on an atomic processor
with 48
logical cubits.
I'm very pleased to have Dolev and Mikhail
on the line from Cambridge, Massachusetts.
Welcome to the podcast, and congratulations
on achieving
a remarkable
breakthrough in quantum computing.
Hello.
So, Mikhail, I think my first question is
for you.
What is quantum error correction,
and why is it necessary?
Thank you, for having us.
So
I have to answer this question, I will
maybe go back a little bit to a
history of, quantum computing and quantum information,
which,
kind of the early ideas are now by
now, maybe 40 years old or so.
And, when people started realizing that,
you can use the ideas of quantum superposition
and quantum entanglement to build new
quantum information processing systems,
From the very beginning, there was a concern
whether you can actually realize such systems in
in practice.
And
in particular,
maybe
around, like, 30 years ago where
some of these early ideas started solid to
solidify, for example, ideas of quantum simulations,
the ideas of Shor's algorithms were put forward,
people really started asking, you know, can quantum
computers be built practical quantum computers be built
be built and how?
And already at that time, it was very
clear. It will be a very challenging task.
And,
in particular, one
challenge, I would say, is conceptual is that,
it's very hard
to put,
a big system in a quantum superposition state.
So at the kind of microscopic level, like
single electrons,
you know, single, you know, spins, you know,
can be put routinely in a superposition state.
But the big objects around us, you know,
like, you know, this this span or this
table, you know, while they're composed from quantum
mechanical particles, you know, there is nothing quantum
about them. Right? So big systems lose quantum
character, and this is really fundamental. So
in the,
field of quantum computation, this specific example,
this,
issue shows up is that if you start
building, for example, quantum computer and you build
it from some kind of
quantum logic operations,
inevitably, there will be a small errors that,
you know, will happen during this quantum logic
operations.
And these small errors eventually accumulate to make
this, you know, the output completely classical.
So it basically loses all quantum features.
And, because of that, kind of early on,
there were,
kind of,
there was a lot of excitement on one
hand about quantum computers, but there was also
a lot of skepticism.
Right? And so and in particular, if you
start looking at what type of error rates
you need to really implement, you know,
some, you know, interesting quantum algorithms at scale.
These error rates are, you know, extremely low.
You know?
Well below 1 part per billion. You know?
So
and,
for this reason,
I mean, there was a little you know,
some of the skeptics were actually very kind
of prominent, you know, I would say, leaders
on the field. We want you know, thought
that, you know, while these quantum
computers is really a furious dream, it's experimentally's
nightmare. You know, it's really literally kind of
impossible. It's impossible to reach such low error
rates. And,
and, already early on, there was an idea.
Oh, okay. So in classical,
computers or quantum classical information processing systems, you
can often use
some redundancy
to,
protect quantum information. And, you know, people started
asking a question. So can you use this
redundancy to
basically preserve quantum information?
And the answer, even conceptually at the time,
was really,
non,
obvious,
because,
quantum information
cannot be copied.
Also, if you measure so to, like, utilize
it done to see classically, you basically need
to,
measure state
to verify where errors happen or not.
In quantum mechanics, you basically cannot
measure the state without collapsing this.
And, you know, for this reason, it kind
of seemed challenging that even in principle, you
could use
error correction to protect quantum information.
Nevertheless,
about,
you know, 25,
30 years ago,
there was theoretically
shown that you can actually use,
this kind of redundancy. You can actually use
entanglement
to,
store and protect quantum information.
And
I would say it was really this breakthrough
that really
kind of jump started
this field as we know it now.
I see. And, Dolev,
an idea that's central
to quantum error correction
are the concepts of a physical
and a logical
cubit,
and and that's something that was important in
your research. Can you can you explain why
quantum error correction
considers
physical
and logical cubits? And and what are they?
What what are the differences between the 2?
Error correction is this really remarkable process,
and, it uses physical cubits. And when we
say physical cubits, we mean things like ions
or superconductors
or,
you know, defects or spins, things that have
two levels
that are like a quantum two level system.
And we have been studying these for roughly,
like, almost a 100 years since the beginning
of NMR,
where people were using these for, you know,
various different applications.
And,
the invention Misha mentioned from 30 years ago
was the fact that you can actually put
these together to make a logical qubit.
And,
the way that it works because you can't
copy quantum information
is what you do is you take this
abstract unit of information,
which is our logical qubit,
and you use entanglement,
these, you know, funny quantum correlations between particles
to take this one logical cubit degree of
freedom and delocalize it. You spread it across,
you know, a large array of physical cubits.
And now what happens is that this delocalized
information
is protected.
Now, you know, if the environment comes in
and tries to measure
part of the system, if it looks at
just one of the physical cubits in the
system, it actually will not learn anything about
the underlying stored delocalized state.
And so
that is what the physical mechanism is behind
logical cubits, and that's how we take we
use entanglement between physical cubits to make robust
logical cubits, and it is truly remarkable that
it is, you know, physically possible.
That being said, it is clearly a completely
different object
than these physical cubits are. These physical cubits
are these two level systems we've been working
with for, you know,
almost a century. These logical cubits are these,
you know, highly entangled states,
and is in in a sense an abstract
unit of information. And now that we're starting
to work with them in the lab, we
see that there's many, many differences to the
physical cubits that we're used to working with.
And,
perhaps one of the, you know, most
key features there in terms of the difference
between physical and logical cubits is that
in a physical cubit, it's a quantum two
level system.
You can take this cubit and it can
be in you know, you can just
rotate it and put it in any state
that you want. And that is, you know,
for example, what people do when they do
NMR is you have, you know, a spin
and the spin can process, and it can
be in any, you know, point of this,
you know, qubit's
phase space.
But with logical cubits, it's very different. You
can't do things like arbitrary
rotations like we do in NMR.
You can only do digital operations
where the logical cubit and the still localized
information can only do things that are exactly
the operations that are allowed by this logical
qubit. And in a sense, it starts to
make quantum information processing more digital.
And this also should parallel, you know, what
we do with our classical computers,
with classical computers, all of our information processing,
the reason it's so robust is because it's
digital. It works on bits, and the bits
do, you know, precise logic operations that are
insensitive to, for example, voltage fluctuations
in your computer.
And we have analog classical computers as well
that are, you know, can do arbitrary rotations,
and these things are much more similar to
the physical cubits that we've been working with
in the field,
before.
But now that we're working with logical cubits,
we're really starting to explore digital processing, very
similar to how we have digital processing with
our classical computers. So there's various, you know,
very important key differences,
between these physical and logical cubits that we're
starting to explore now.
I see. And, Mikhail, can you can you
explain
or describe the the physical qubits
that you use in your lab? Well, I
suppose, in this specific bit of research that
you and Dovlev have done. What what does
the, what does the physical
cubic comprise?
These experiments
utilize,
isolated,
trapped, neutral atoms, actually, rubidium atoms,
which are individually trapped
and held,
in optical tweezers and tightly focused beams of
beams of light.
So
to kind of take a step back, you
know, this, of course, one of the many
platforms that people are now exploring.
So the reason why,
neutral atoms is a promising platform is because,
first of all, they
have excellent coherence properties. So while isolated and
held in tweezers,
you can basically store quantum information for very
long time for tens of seconds or kind
of minutes. It's kind of almost like unlimited,
you know,
in increase in principle.
And that's one important,
feature. For example,
atomic
clocks, you know, some of the
most precise
precise instruments,
scientific instruments that humankind ever built, you know,
utilize now
neutral atoms, trapped neutral atoms.
So and the second thing which is also
very important is that
in print in principle and also in practice,
you know, you can,
create
a very large,
number of this kind of, you know,
of the,
atoms. And, basically,
I should also point out that our experiments
make use of this so called laser cooling
and trapping techniques. So they're actually room temperature
systems, but their atomic motion is slowed down
by essentially buffing the atoms
in a beam of,
beams of light of certain color. And as
a result, they basically come to the standstill.
So each of our experiments starts with a
cloud which contains, you know, basically many millions
of tens of millions of
motionless atoms, which are, of course, fantastic, you
know, initial,
step, you know, to create,
a lot of qubits.
But the challenge
now is not just to create these qubits,
but also to control them. And that's why
we use this kind of optical techniques, the
techniques from holography,
to basically create a large,
number of
tweezers and trap the atoms there. There is
one other challenge,
and that is the atoms in the gas
phase
basically don't interact with each other, don't talk
to each other. So in order to entangle
them, to make quantum logic,
we actually use lasers
to promote the atoms into the so called
Rydberg states. So the Rydberg states are the
states where, electrons orbit, you know, very far
away from the nuclear. So so the atoms,
in this state, say, basically, can be thought
of having kind of a large size.
And then as a result of that, they
really start interacting very strongly to each other.
And
in particular, to entangle these atoms,
and,
do quantum logic, we utilize something which is
called Rydberg blockade. So that's the idea, which
is,
by now almost 25 years old.
But it kind of, in the recent years,
really proved to be kind of remarkably fruitful.
And,
and the the key idea of this Lydberg
blockade is that
you can basically
consider 2 atoms,
which you excite to the Rydberg states. If
they sit far away, then, essentially, you can
excite them independently. But when you bring them
close to each other, then what happens is
if one of the atoms is excited to
the Rydberg state, the excitation for the second
atom is blocked.
And what this mechanism does, it basically makes
the interaction of the atoms between the atoms
almost digital. So if they're far away, they
don't talk to each other. If they're close
to each other, the interaction is nearly infinity.
And that actually allows us
to,
entangle,
the atoms,
with very low errors.
And in particular, it also enables us to
entangle many pairs of atoms in parallel. So
that is something that we will maybe talk
about a little bit later. So,
basically, these optical tools combined with this Littburg
blockade
is what enables, you know, high fidelity
or in other words, low error rate, you
know, parallel,
control and entanglement
of 100 of atoms at a time. You
know? And this is an essential
ingredient which actually allows us to do this
kind of logical you know, build these logical
processors kind of in a very efficient,
way.
And can I just ask you,
Mikael,
how how big is this
cloud
of atoms? Just to give our listeners an
idea of the size of
of, I mean, I know you've probably got
a huge vacuum chamber, etcetera,
but the actual cloud.
The actual you know, the size of the
processor,
you know, which traps these atoms, you know,
and basically allows us to kind of manipulate,
the this
this atoms is a couple of 100 microns.
You know, it's actually, you know, relatively small.
But even this even this,
zone is actually kind of divided into small
parts,
smaller parts, and one of them is like
a storage zone. Another one is the zone
where we do logic and entangling zone, and
another one is on the zone where we
do kind of readout.
And, actually, one other
innovation now going to 2020, which actually really
fueled with this development,
was actually led by the left,
was to
realize
what we call,
the configurable,
architecture.
So and to explain it, you know,
like, let's think about how
the conventional,
you know, chips, you know, semiconductor chips. So,
you know, you what you do, you know,
you design this chip, you design this, you
know, transistors, you design that, you know, connectivity,
and then you basically, you know, send it
to,
you know, to to the, you know, factory,
you know, where basically,
the state of the art technique uses optical
lithography. Basically uses optical tools to define where
this chip
these transistors are going to be.
And then eventually, this,
this,
you know,
things,
are made. But, basically, the point what I
want to make is that
the connectivity,
the architecture
of this chip
is fixed at a design stage.
So what happens is that using optical tweezers,
we can actually move atoms around,
and we move them while preserving the coherence,
while preserving the stored qubit. So this is
done by encoding qubits into so called hyperfine
states, basically spin states of atoms where they
can live for a very long time. And
most importantly,
you know, by moving the atoms around, you
can basically
create the,
the architecture
where the connectivity
is like a living organism. It changes during
the computation itself. And it, for example, allows
us to move atoms between these different zones.
It allows us to,
you know, entangle atoms in parallel. You know?
And it allows us to basically implement all
necessary ingredients,
for the logical cubits.
So, Dov, in in your work, you created
48 logical cubits using these physical cubits that
Mikhail has,
described.
How
how did you do this? I mean, is
it possible to to describe it in simple
terms? How how you take these physical cubits
and
sort of blend them together to make logical
cubits?
Yes. Absolutely. So maybe there's 2 two stages
of answering your question. So one is, like,
on a physical level, how does this even
happen,
like, on a quantum mechanical level? And then
the other one is, how did we
make how are we able to, you know,
really simplify the problem to make it much
easier than it has been historically in the
field?
And,
the, to answer the first one,
it's we're we're leveraging the fact that we
can entangle particles
by moving them around entangle these atomic cubits
by moving them around and zapping them with
the laser pulses when they're next to each
other to entangle them.
And,
to create these,
logical cubit states,
similar to what I was describing earlier, we
take this, you know, like, one qubit, for
example,
or a collection of qubits, and then we
entangle it with its surrounding
atomic qubits
in a very structured way
that spreads out this information.
That creates this logical cubit once you create
this entangled state. Now you have to do
multiple important things to it that we were
able to explore in our work. One is
that you, you know, can now do logic
operations
between these logical cubits.
And,
that's actually one of the things that's the
hardest to do.
When we take,
you know, this logical cubit and then spread
it out across an array of physical cubits,
And we might do this now on 2
different blocks of qubits, and we'll have 2
different, you know,
delocalized,
degrees of freedom.
They're now protected from their environment because now
the environment can't come in and measure this
underlying state. But now it's also very hard
to get them to interact.
And actually in the field before, people had
done quite, you know, nice work in creating
logical cubits.
But the really huge challenge was always getting
them to interact Because now there are these,
you know, just completely delocalized degrees of freedom.
And for example, imagine that you have these
2 delocalized degrees of freedom, and they're stuck
next to each other on a chip or
something with a fixed 2 d connectivity.
But now it's very hard to get them
to interact because they can only interact through
some boundary, whereas they're, like, delocalized over space.
So with our ability to move cubits around,
what we can now do is we can
actually pick up
the 2 logical degrees of freedom,
put them right on top of each other
by interlacing
the 2 grids of atomic cubits,
And then by entangling all of the pairs
of the, you know, underlying logical blocks, that
realizes, like, a logical entangling operation.
So
that is how we do both the creation
of the logical cubits as well as their
operations.
And,
that is something that it was an extreme
simplification.
So one is it now by doing these
gates in this way that we call transversal,
where we can take these 2 degrees of
freedom and interact them directly. It's an
native logical operation that can just be directly
done.
The other thing that's really special that is
related to what Misha said is it now
allows us to start controlling things in much
simpler ways.
And in particular,
you know, modern quantum processors have almost exclusively
been built in this way where you have
several cubits and you just add more and
more cubits and you add more and more
controls to control each cubit. But one of
the big innovations in this work that made
this so much simpler
is that once we're starting to do error
correction,
all of the physical cubits within a logical
cubit
just need to do the exact same operation
in order to realize a logical operation.
So now in this zoned architecture
that we're describing, we can also work with
all these logical cubit blocks as if there's
just one big atom essentially.
And we take this one, you know, big
atom and put it next to one other
big atom and do this entangling
logical operation in a single parallel step, and
then can go and move these, you
know, big qubits and interact them with other
big qubits. And that was one of the
things that was really central
for us to be able to create such
a large number of logical cubits and explore
different types of interesting algorithms,
with them. We were able to create 48
of these small logical cubits using these approaches
and do 100 of logical operations, whereas in
the field, people had previously only done 1
or 2. Before this work, we were also
able to study things such as improving logic
operations as we increase
the size of the error correcting code and
study a lot of really key features of
what does error corrected quantum computation look like
due to the fact that we can, you
know, do this abstracted,
control where we're working with logical cubits as
the fundamental units of this processor.
I see. And and, Mikhail, you you've got
these 48
logical cubits. Are you able to to actually
do practical calculations
with your system? Is there are there problems,
you know, maybe even trivial
computational problems that you can solve using it?
Or is it very much a sort of
proof of principle
system?
So maybe we'll answer this question in 2
parts. I will start, and then I'll let
Alef complete my answer. So,
and, you know, to answer it, I maybe
want to make a step back
and, you know,
mention that in addition
to kind of building, you know, large scale
quantum computer, another big challenge in the field
is to identify what can we use these,
you know, devices for. You know? How can
they really help humankind?
And you could say, well, I mean, it's
kind of a, you know,
funny question to ask for this field. So
active and so on, but this is not
unusual. So when I renew some new tool
comes, you know, into play, you know, people
often, you know, have hard time anticipating where
it's, you know, going to be most,
useful.
But there is one area where it is
very clear that this quantum computers,
and quantum simulators,
will have tremendous value,
And that is in modeling and simulating,
systems which have high degree of entanglement.
And these type of,
you know, systems
occur in various areas of science, of physics
in particular.
Certainly, you know,
many of the condensed matter models,
feature, you know, so called strongly correlated systems.
You know?
You know,
feature, you know, high degree of an or
expect I expect it to feature feature high
degree of entanglement.
Another area which is actually also very exciting,
is,
involves,
simulating system where you can build entanglement very
quickly.
And,
this is actually,
very interestingly connected to the physics of black
holes. You know? So people believe that black
holes, at least, you know, a kind of
their quantum description, you know,
involves
this process, involve, you know, fast scrambling. You
know? This is where you basically, you know,
create entanglement kind of in the fastest way
possible.
And so one of the experiments we have
done is actually exploring this kind of fast
scrambling.
And maybe I'll let Daleyf add because, you
know, he's he was a mastermind of this,
you know, specific. You know?
Yeah. So following up on that. So maybe
I will also take a step back about
this quantum scrambling and say that one of
the biggest open challenges in physics is we
do not understand
how quantum mechanics and gravity combine.
It is, in my view as a physicist,
one of the most interesting open questions of
our time.
And that is actually one of the places
where quantum computers
can almost certainly be very useful.
And
remarkably, we don't fully understand how quantum mechanics
and gravity combine, but one of our best
guesses in terms of how this might arise
in our universe
is that there's entanglement
on some boundary
in our universe,
and the entanglement on this boundary gives an
emergent gravitational
description of the universe, which is amazing.
And,
however, it's really hard to make progress on
some of these types of really complex quantum
questions
without a quantum calculator. We only have classical
calculators, and we're trying to calculate these extremely
complex
things about the universe.
So what we did here is a really,
you know, like, toy
study of those types of things, but we
were able to, you know, study this complex
scrambling.
In particular, we entangle everything on hypercubes.
One of the things that's special about hypercubes
is they're very, very connected and that scrambles
information very rapidly, very similar to a black
hole.
And, we didn't learn anything here about, you
know, emergence of gravity
from complex entangled systems, but it does start
to give us a bit of a hint
in terms of, you know, how we can,
you know, what types of systems can we
simulate with these logical qubits.
And,
if I can just nerd out for a
second, one of the things that we did,
which was really, you know,
very tailored to this logical cubit processor here,
is that once we're so we've we've had,
you know, several decades of exploring,
you know, processing with physical qubits.
And
physical qubits have very particular rules that we're
used to following.
And one of them is that, you know,
it's very easy to do arbitrary rotations of
cubits like we do in NMR, and it's
very hard to do entanglement.
In these, you know, first error corrected algorithms
that we were doing with these logical cubits,
one of the things that we saw is
it's very hard to do arbitrary rotation,
but it's very easy to create entanglement. So
it actually very well suited to something
like this black hole scrambling.
And this was just one example of us
doing quantum simulation with these air corrected cubits,
but it starts to open a new scientific
frontier
of exploring how to do quantum simulations and
quantum computations,
in ways that are highly tailored to these,
you know, new set of weird rules that
we have to work with with logical cubits.
And so we're not yet doing any practical
calculations that are, you know, curing cancer or
completely changing things like that. But what it
is very clear is that in the near
term
and in all already what we've done and
also in the near term,
there will be a real value in just
learning what these quantum computers can do from
these types of experiments. So it's practical in
that sense, but, yeah.
Oh, that's great. I mean, I have to
say, I wasn't expecting that you'd say black
holes.
So that's that's great. You learn learn something
new every day. So what's what's next,
for you guys?
Mikhail, what what what do you have planned
for the future? Are you are you going
to try to create
more
logical cubits in your system? Or is there
some other avenue that you can pursue to,
sort of, to to gain your understanding
of, of this quantum computer system?
Yes. Certainly
scaling up
this,
you know, quantum, you know, computation is definitely
very much in our agenda.
And,
I would say that, you know, definitely one
would like to have more logical qubits. You
know? But most more importantly or equally important,
one would like to actually improve these logical
qubits. Right? Because, you know, even if we
encode information,
you know, at least up to now, what
we and others have done is encoding offers
some protection, but it's not, you know, I
mean, it's not perfect. So we would like
to actually,
you know, make this logical qubit better,
and then reduce error rates. And the key
goal here is really to start,
doing computation, which have
computations, which have deeper circuits.
So,
I would say,
you know, over last year, you know, in
addition to the work that we have done,
there was also some very nice experiments from
across several different platforms. So for example,
the very recent work of by Google,
actually,
demonstrated just one logical qubit, but what they
have done is they basically,
did experiments which involve multiple cycles of error
correction. Alright? And it's kind of it's a
little bit like scaling up if you want
them on different axis as compared to what
we have done. So but, but in reality,
what one needs to do is one needs
to really combine these two things, you know,
to basically start implementing
kind of, you know, deep circuits involving large
number of of logical qubits and eventually try
to, you know, figure out, you know, how
to answer, you know,
your previous question in different ways. You know?
So the question is, what can we do
with this kind of systems, basically?
And, what emerged from our work
is that
answering this question would really real has to
rely on this idea, which we sometimes call
codesign. So if you have if you want
to solve some specific problem. So what you
like to do
or you would need to do, basically,
you need to think about algorithm
to solve this problem,
kind of codesigned with first
error correcting code, which really fits this problem
very well
together with decoder,
with compiler, and eventually with your hardware system.
So this is it's very clear to us
that for the next, you know, 5 years,
maybe in the next decade, this is a
way to make progress.
And we are really excited about kind of
starting to put these things together to really
kind of, you know, you know, build,
you know,
systems and and
come up with more examples where you can
really, you know, enable, like, deep circuit kind
of useful,
quantum computation with, you know, large number of
of qubits. Maybe I'll let Dolesv.
Do you have anything to add to that,
Dolesv?
Yeah. Absolutely. So, yeah, I would say that
last year, we learned a lot about how
to do error corrected algorithms. Now, you know,
it's a really important
frontier to learn how to do deeper error
corrected algorithms,
and improve the performance.
There is really exciting progress happening across the
field, both, you know, between, you know, neutral
atoms, trapped ions, who are gonna think you,
but people are really starting to experiment with
these systems. And I think one of the
things that
has become
extremely clear in 2024
is that error correction is,
you know, definitely works.
And also, it is currently a real inflection
point
in the sense of you really start to
get below characteristic thresholds in the system. You
really start to come up with creative ways
to do logic operations.
And it really the field is now going
to start transitioning toward doing algorithms and computations
and simulations with error correction. And that is
going to be a, I think, a very
dramatic inflection.
And so
that, in my view, is extremely exciting. I
mean, even just in the past few months
alone across many different systems, there's been really
remarkable error correction progress.
But there is, however, one pretty huge elephant
in the room, which is that
for a lot of the computations that we
have in mind, we need things at the
scale of tens of millions of cubits.
And we are working to reduce that number,
but the main thing is that we are
not yet close to that.
We do have ideas in terms of how
to get there.
But currently we're working with systems that have
hundreds of cubits at the most.
And so,
there will be many challenges in trying to
get to these much larger systems.
But I do think that the field is
going to develop in a very different way
than it has in the past.
And I would say it's for 2 key
reasons that are emerging now.
One is that error correction
clearly works, and I think that is really
starting to be at an inflection point that
it was not nearly at the same level
2 years ago. And 2 is that when
we build error corrected processors, we can build
them differently than we're used to building physical
CUDA processors.
And so we are not yet close to
our end goal.
Although, of course, the goal will always evolve.
Although we can, you know, start to explore
interesting science in the meantime. But I also
think that things are going to start developing
across all these very various different systems
more rapidly than is being expected because of
these two key changes that I think will
both be key inflections.
So, yeah, many challenges, but also very exciting.
Oh, well, that's great. Well, thanks. Thanks, for
coming on the podcast.
This is one of 2 podcasts with our
breakthrough of the year winners.
The other features Google's Hartmut Kniven,
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, 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 Mikhail Lukin and Dolev Blufstein
for joining me today,
and a special thanks to our producer Fred
Iles.
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.i0p.org.
We'll be back again next week.