- So I've been working in this field for 25 years.
And a lot of the things that we worked on
already two decades ago,
we're just getting to the point
where we're starting to see those ideas realized
in real physical devices.
So evidently quantum computing is really hard to do.
(bright music)
- John Preskill is a theoretical physicist
and one of the world's leading experts
in quantum computing and an Amazon scholar here at AWS.
John has been theorizing about quantum computers
since the early '80s, even before modern computers
as we know them today were fully realized.
And there was a time not too long ago
when people actually weren't sure
that quantum computers could even exist,
that the technology may not be possible.
But today we are starting to see some instances
of quantum computers in action.
This technology has the potential
to drive transformative advances
in areas like data security,
machine learning, medicine development, sustainability,
and much, much more.
I was curious to learn about the kind of problems
that quantum computers might actually be able to solve.
So I met with John to dive deeper
on this technology and to learn more
about the state of quantum technology today.
Now, if you're not familiar, you might be wondering
what makes quantum computing different
from the traditional computing, and how does it work?
- These quantum systems,
they sort of have their own language.
It's different from the language of bits that we would use
to express classical information.
- To put it in very simple terms,
in traditional computing you have a bit
which can be zero or one.
With quantum computing, you have a qubit, a quantum bit,
which is in super position of zero and one,
meaning it's a combination of both at the same time.
And quantum computers also use entanglement,
which means that they can correlate
their measurements results
among the parts of a quantum system.
The important thing to know about quantum computing is
that it's not necessarily intended to solve problems
that are unsolvable by traditional computing,
but that it allows us to do it in a more efficient way.
So something that might take hundreds of years
on classical computing might only take hours
with quantum computing.
- Yeah, it's an important point
that quantum computers compute things
in a very different way than ordinary classical computers,
but they don't change the notion
of what is in principle computable.
Anything that I can compute
with a quantum computer, in principle,
I can compute with an ordinary computer
with a Turing machine,
but perhaps with a very, very drastic slowdown.
So it's really all about the efficiency.
That's why quantum computers are exciting.
- What John describes here
is worth spending some time considering.
Quantum doesn't change whether it is computable or not,
but what it does change is the time horizon
of the computation.
So what is the current state of quantum computing?
- We've gotten to kind of a pivotal stage
where at least we have devices that are powerful enough
that, in principle, they're capable of doing things
which surpass what are most powerful classical computers
can do today.
When it comes to quantum computers
with the potential to change the world,
those could still be decades away
because we're going to need
much more powerful quantum computers to solve problems
that can have an impact on people's everyday lives.
And the time scale to get there I think is very uncertain.
They're really big technical problems we have to solve.
I can't overestimate what a big challenge it is.
I think it's one of the great challenges
facing 21st century science and technology,
to scale up quantum computers
so they can solve very hard problems
that are beyond the reach of what we can solve
with ordinary computers.
You can find challenging problems to work on
and make progress year by year,
and have a lot of fun doing it.
- As technologist, whenever I hear this type of challenge,
I'm always curious about how we might be able to solve it.
Is it a software issue or hardware limitation?
Quantum computing is uniquely interesting and challenging
because there are many layers of complexity.
The first is actually trying to figure out
the kind of problems
that quantum computers can solve by design.
- So they can solve some problems much, much faster
than we can solve them
with ordinary digital computers, but not all problems.
Only problems that have the right structure.
And we have only a limited understanding
of what the problems are
that are too hard to solve classically
and can be solved efficiently quantumly.
So we need to advance that understanding.
- You might say what we don't know what we don't know.
We're still working to understand the types of problems
that can be solved with quantum computing.
This means there are vast opportunities
to test and explore different ideas,
but it also means we have to determine the right approach
to the physical hardware in order to solve these problems.
So how does this limited understanding
have an impact on early quantum hardware design?
- Well, of course, any quantum computer
has to be realized physically
by encoding the quantum information
in some particular physical system.
And right now
there are a number of different hardware approaches
that people are pursuing,
and we don't really have a clear understanding
of which of those has the best long-term prospects
for scaling up
to computers that can solve really hard problems,
or it might be something that we're not even pursuing now.
So that's, I think, a very open, basic research question.
And then the third challenge is
the challenge of how to control noise.
Quantum computers are intrinsically very noisy.
- Noise is something that comes up frequently
when talking about quantum.
It's an interesting phenomena
given the uniqueness of the hardware approach itself.
What actually is noise when talking about quantum computers?
- Well, really, I mean that the quantum hardware
doesn't perform according to its ideal specifications.
Well, there are a couple of different things.
One is the interactions with the environment
that we were talking about before.
That's one source of noise
and a particularly a pervasive one, which is hard to combat.
Essentially the quantum hardware
becomes entangled with the environment.
Information is leaking to the environment and that means
the device doesn't behave the way it's supposed to,
the gates won't be exactly right.
But even if we did isolate the device perfectly
from the environment, there would still be issues
because if we do a quantum operation,
it's not as robust as the kinds of operations
that we do on bits where you either flip a bit or you don't.
When you perform an operation on a quantum computer,
you can, in a sense, rotate the qubit a little bit too far
or not quite far enough,
and that gives rise to imperfections as well.
So you need very precise control.
If you perform operations on pairs of qubits,
then in the best hardware we have now,
either the trapped ion quantum computers,
which encode the information in atoms,
or the superconducting circuit quantum computers,
the probability of making an error per operation
is about 1%, maybe a little bit better than that.
And that's really not very good.
So it means if you try to do a circuit
with more than a few hundred of those two-qubit operations,
the signal will be overwhelmed by the noise,
and that's a very severe limitation now
on how far we can go.
We need to make the hardware better.
It's much harder to make a quantum processor reliable
than a classical processor.
Maybe sometime in the far future
that will no longer be true.
And I think it's an important goal for research
to come up with better ways of encoding
and processing quantum information
that can be much more reliable
than the methods that we're using now.
The hardware will presumably improve
but noise is still gonna be a limitation,
and so we have to understand
how to do error correction efficiently
to be able to compute reliably
in spite of the noise is in the hardware.
- Now there's a lot of hype around quantum computing
and people are excited that it will be the next big thing.
And I think a lot of the excitement comes from the idea
of being able to solve problems
that today aren't feasible with classic computers.
But as John says, we still got a long way to go
in developing reliable, scalable quantum computers.
And it's going to take a lot of iteration
and improvement over time
before we really start to see these kind of results.
So what does the road ahead look like for quantum computing?
- Well, in a way, the hype is understandable at some level
because everyone understands that information technology
has a huge impact on the world,
and this is a completely different way
of processing information, which we know,
at least for some purposes, has big advantages.
Well, I think it's helpful for someone who's interested
in developing new quantum algorithms
to have a better grasp of the computational problems
that people can't solve now.
You have to probe a little bit to find out what they are
because people can't solve them.
They're not trying to solve them necessarily
and they may not come foremost to mind
when you think about how to use computational resources.
So I think there really is a dialogue needed
to get a better understanding
of what are the big computational challenges
that could be impactful for customers,
and that can help to steer the theoretical work.
- I remember back to my time in college,
one of my mentors said to me that computer science
wasn't actually really a science.
Physics, biology, they are observed.
But when it comes to computers,
we are the ones that made those.
We created the hardware and the software.
So computer science can't really exist
without the computer industry
'cause the two are interlinked.
But in quantum, there seems to be
a much more fundamental part to this,
where it relates back to fundamentals of nature,
of physics and science.
- First of all, I mean,
I think from a fundamental point of view,
computer science is
investigating consequences of the laws of physics,
of a model of computation
is really a model of something we can do
in the physical world.
And it shakes up those foundations
to realize that if we take advantage
of the quantum properties of our hardware,
that changes the answer to the question,
"What are the hard problems
"and what are the easy problems?"
So that's very fundamental
from a physics point of view, I think,
but also from a computer science point of view.
It's kind of a matter of degree.
The practical side which is trying to get algorithms say
to compile or to run more efficiently and so on
is just a refining of our understanding
of what are the easy things to do
and what are the hard things to do in the physical world.
- As some of you may know,
last year we launched our own quantum computing service,
Amazon Braket, which is a fully managed service
that helps you get started with quantum computing.
You can design and build quantum algorithms
and run your code on actual quantum hardware.
You can choose between different quantum technologies
from multiple providers
to find out which quantum computing technology
is the best fit for your application.
We also announced the AWS Center for Quantum Computing
which is located on the campus of Caltech.
This center aims to develop
more powerful quantum computing hardware and software
and to identify new applications for quantum technologies.
John has joined this research effort
alongside with a team of other scholars.
For a theoretical physicist,
what's the appeal on working on a project like this?
- I think that we're a long way away from having hardware
that's capable of solving really hard problems
with quantum computers.
And I saw this
as an opportunity to focus on solving
the really challenging problems
that might not have a practical impact
in five years or even 10,
but in the long run, we're going to have to develop
much better hardware approaches as well as new applications.
And this seems to be a good fit
to the culture of AWS, where they're accustomed
to thinking long term and to taking on challenges.
And I feel AWS has an opportunity to have a special niche
in the quantum ecosystem
by being the company that talks straight
about the status of the field and how challenging it is.
And I don't think we will hear from the AWS leadership
highly exaggerated claims
about the time scales for big impact
because that's just not the way they act or think.
In the long run, we can anticipate quantum computers
will have a big impact on human health
and energy production and agriculture and so on
just through allowing us to search more efficiently
for new types of compounds, new types of materials
that have novel applications, things like that.
We don't know how far away those things are
but I think there will be a big societal impact from that.
So that's part of what we are trying to pursue
at the Center for Quantum Computing
that AWS has established in partnership with Caltech.
We're thinking about what approach might give us
much better hardware,
and that's important to pursue, but it's a big challenge
and we don't know the timescale for success
or what approach is going to turn out to be
the most successful.
So we got a lot of hard work to do
and we don't know what a realistic timescale is.
I think in the long run
we're going to see a very substantial impact on society
from quantum information processing.
It's important to go there.
It's going to take a sustained effort
and a lot of innovation
and a lot of investment over a sustained period.
So let's not give up.
Let's keep at it.
- I'd like to thank John for joining us
and feeding my curiosity and sharing his insight
on the future of this exciting technology.
If you'd like to learn more about our guest,
you can follow John on Twitter at @Preskill.
And if you'd like to learn more
about quantum computing and our partnership with Caltech,
we've included links in the description below.
We'll see you next time. Now go build!
(bright music)