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  • MALE SPEAKER: Good afternoon.

  • 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