Thanks, everybody, for coming from the remote sites

to attend the talk by John Martinis about the design

of a superconducting quantum computer.

And we're very pleased to have John

here with us, just a short ride from UC Santa Barbara.

And the reason we are excited is John

is considered one of the world, if not THE world

authority, on superconducting qubits.

So since the current machine we're working on

is based on superconducting qubits, of course,

his opinion and advice would be very important

for the guidance of our project.

So John got his PhD in physics in 1987

from UC Berkeley in California.

But then went to France to the Commisiariat Energie

Atomic in Saclay.

Afterwards, he worked in NIST in Boulder.

And then in 2004, he settled where he is right now,

being full professor at UC Santa Barbara.

And then in 2010, nice achievement,

getting the AAAS Science Breakthrough of the Year

award for his work on a quantum mechanic oscillator.

So we are very curious to hear your--

JOHN MARTINIS: OK.

Thank you very much.

MALE SPEAKER: Oh, one last thing I should say

is you remote sites, when the talks over, at this time

you guys will be able to ummute, and then you

can ask questions remotely.

Thank you.

JOHN MARTINIS: Thank you very much for the kind invitation

to come here.

I have a son who's a computer science major at UC Berkeley.

And I don't know if you have kids.

When you have kids and they're young,

the parents can do no wrong.

And then they turn into teenagers,

and their esteem of you goes down.

And then, as they get into the real world,

you suddenly become more and more intelligent

for some reason.

So coming to Google, for my son, is totally cool.

Makes me totally cool.

So I'm at a much higher esteem today after doing this.

I want to talk about our project now

to work on superconducting qubits.

And to talk about some recent, kind of amazing results here.

This is maybe one of the first times

we're talking about these results.

The ideas of quantum computing have been around

for 20, 25 years or so.

The idea here is you can do some kind of calculations

maybe much, much more powerfully than you can ever

do with a classical computer, taking

advantage of quantum states.

But it's been 20 years or so.

And you might ask, well, is it really

possible to actually build a quantum computer?

It's maybe a theorist's dream.

Or I've heard one paper call it a physics nightmare

to build a quantum computer.

It's really hard.

We've been going at it for 20 years.

Are we really going to get there?

Is it possible?

And what I want to do is talk today

about some theoretical understandings

in the last few years, and some recent results

in the last year.

Really coming up to data-- I'm going

to show data we've taken in the last few weeks.

Where we really think we can build a fault-tolerant quantum

computer.

And we can start down a road to really harvest,

to take advantage of the power of quantum computation.

So I'm going to talk about the theory.

I'm going to talk about our new superconducting qubits.

Basically, here, with the theory for fault-tolerant quantum

computer, you have to make your qubits well,

with an error per step of about 1%.

Then you can start building a quantum computer.

I'm going to show here that, in fact, we've done that.

To motivate this, I want to talk a little bit about D-Wave,

because people at Google and elsewhere

are thinking about that.

And exponential computing power.

And then a little bit more about the need

for fault-tolerant computer computation to do this.

So let's just start with the D-Wave Here's their machine.

Beautiful blue picture here.

They've been very clever in their market

to solve optimization problems, essentially

mapping it to physics of what's called a spin glass.

And one of the big conjectures of the D-Wave machine

is, because they're doing this energy minimization

optimization, mapping it to this physics problem,

maybe you don't have to build a quantum

computer with much coherence at all.

And in fact, their machine has about 10,000 times less

coherence then the kind of devices we're talking here.

So it's a different way of looking at it.

And the nice thing is, once you make that conjecture

and assumption, it's not too hard to go ahead and use

standard Josephson junction fabrication

and build a device to try to do that.

So it's an interesting conjecture.

The machine has superb engineering.

It really is a very, very nice piece of work,

with the low-temperature physics involved in all that.

The problem is, well, although they

think they could be useful, a lot of physicists

are very skeptical of whether it will have exponential computing

power.

And I've been enjoying talking to people here at Google

and other places, because they've said, well,

what does nature have to say in this?

So they've actually taken the machine

and done some experiments.

And I'm just going to review the experiments here.

And this is basically the system size versus the time

that it takes for the D-Wave machine

to anneal to, effectively, the ground state.

You're doing the spin glass problem

with random couplings between the spins.

And they're plotting a typical mean execution time.

And with the D-Wave machine, initially

for small numbers up to maybe 100, it was pretty flat.

But now the latest results, up to 512.

It's starting to grow exponentially.

This exponential growth is actually

matched by some quantum-simulated annealing--

both to stimulated, classical annealing and other methods.

So the preliminary results here, maybe for this particular class

of problems, it's no faster than classical code.

Although people are looking at it.

That's not a firm conclusion yet.

And one has to do more work to see exactly what's going on

in the D-Wave and can you use it.

We're going to take an approach that's

very, very different than this D-Wave machine.

It's the conventional, classical approach where physicists

have proved theoretically-- it's still only theory--

but they have a very strong belief

they should be able to build a computer

with exponential power.

Let me just explain that briefly.

It's easy to understand.

You take a regular computer, and the classical computer

scales linearly with, say, the speed of the processor

or the number of processors.

It's very well understood.

The beauty of CMOS is that the growth of this power

actually goes exponentially in time because of the technology

improvements.

But it's linearly with, say, speed or processor number.

However, in the quantum computer,

this power grows exponentially.

And the basic way to see this is, in a quantum computer,

it's not just a 0 or a 1 state.

You can put it in a superposition of a 0 and 1

state.

Just like you say that the electron is orbiting

around an atom, and it can be on one side

of the atom or the other.

There's an electron cloud.

At the same time, you can have these quantum bit states

that are both 0 and 1 at the same time.

So here, for example, we take three quantum bits, put it

as a superposition, a 0 and 1.

You write that out.

You have 8 possible states that the initial state can be in.

And you're in a quantum linear superposition

of all those states.

And the idea is you take this one state,

you run it through your quantum computer,

and that's basically taking all the 8 possible input states

and parallel processing them in one operation

through the computer.

So the quantum computer allows you

to do amazing parallel processing here

as 2 to the 3, 8 states, or in general 2 to the n states.

So if you have some quantum computer with 64 bits,

you're processing 2 to the 64 states at once.

To get a doubling in power, what do you do?

Here you would you double the size of it.

Here, to double the power with a quantum computer,

you just add 1 more bit.

And you've just double the parallel processing power.

And by the time you get something

like the 200 quantum bit quantum computer, the parallelization

that you're doing is greater than 2 to the 200,

is greater than the number of atoms in the universe.

So you're clearly doing something there that's amazing.

The problem, however, is that you're

doing this parallel processing.

But you only get, when you measure

the system, end bits of information.

And you have to encode the problem

and only can solve certain problems

to take advantage of that kind of optimization.

So I'm not going to go into that very much here.

But I am going to talk about one application of this.

It turns out the government's interested in this.

And that is factoring a large number

into its component primes.

For example, take the idea of factoring a 2000-bit number.

The algorithms for doing that scales exponentially.

And right now, if you take a 640-bit number,

that takes about 30 CPU years to factor that

into the composite primes.

And then, if you say, OK.

You take this and you exponentially

scale up to some number like 2000-bit,

which is something you might think of doing,

what do you have to do to get there?

So what I've drawn here is I think

this is some Google supercomputer here.

I put this especially for this talk.

What would you have to build to factor a 2000-bit number?

You would have to basically build

a computer farm almost the size of North America.

And you see, I put it up in Canada.

You get natural cooling.

Not too many people there.

I think the polar bears would be happy for that to be there,

because there'd be a lot of people to eat

and that'd be good for them.

But with that size, if you built something that size,

you could do this problem in a 10-year run time.

It's possible with that size.

However, that's maybe 10 to the 6 trillion dollars.

Which, even with quantitative easing,

I don't know if the federal government could even do that.

It takes about 10 to the 5 times the world's power consumption,

and it would consume all of the Earth's energy in one day.

So I know Google, you like to think big.

But I'm going to just conjecture you're not

going to want to do this.

This is not practical.

I get this example because I want to show you just

how reasonable a quantum computer might look.

And we don't quite know how to build that now.

We have a general idea.

We need about 200 million physical qubits.

100,000 let's call logical qubits.

You could probably put this in some building size.

Maybe even fit in this particular lecture room,