- So as an example of how
science and society move together
I think it's important
to think about at first
how we do regular computation.
I think about this as
kind of a choice of bits,
what are the underlying
units of computation?
So underlying units of
computation were originally gears
because humans were really
good at building gears.
Their applications changed
in time so these gears
from the ancient Greeks were
used basically for astrology.
They wanted to better
compute locations of planets
and then some stars to
better advise their leaders.
So for quantum computing
it's kind of the same thing.
We actually have a lot of quantum bits.
Some of the initial experiments
in quantum computing
were done in a nuclear
magnetic resonance machine.
I know some of you or you
know someone in your family
who's gone to have an MRI done.
An MRI is a nuclear
magnetic resonance measure
but they knew that patients wouldn't like
to hear the word nuclear so
they just struck the "n" off
and made it an MRI.
It's basically a big
magnet and what it looks at
is it actually looks at quantum
states of hydrogen spins
in your body and that's
how it makes its image
which is kind of amazing.
Now because MRIs are
good for so many things,
they already had all of
the control infrastructure
to do quite complicated
quantum experiments
which is why at the beginning
of quantum computing
experimentally a lot of
work was done in an MRI.
Nowadays it's spread on
to thinking about ways
to put photons together.
These are each individual
atoms that can be controlled.
You could think about
electrons controlled inside
of solid state quantum dots, atomic ions
and superconducting qubits.
At the moment I would
say that atomic ions,
which I work on, and
superconducting qubits,
which Google and IBM and Intel work on,
are the leading candidates
for how to build
a quantum computer right now.
All right, so just briefly
I want to talk a little
bit about the difference.
Classical computation, the key thing is
it's just basically a bunch of answers
to yes or no questions.
So you have the bits storing a zero/one,
things are true or false.
If I do a computation, in
this case I have a bitstream
and I'm adding one.
I map a bitstream to a bitstream.
So everything you do in your computer
whether you're on YouTube or social media
or working on your MATLAB
or writing your essay
all it's doing is mapping zeroes and ones
to zeroes and ones.
That's all it does, the fundamentals of it.
If we measure these
strings of zeroes and ones
nothing happens, you
get to keep that string
and use it later.
And then most importantly, we can copy it.
So you know, you should
always back up your data
any way you can so that
if one device goes down
you can get that information
from another device.
In a quantum computer information
is stored in a quantum bit
and what it means is that
you can have sort of like
you have technically a super
position of zero and one
and then a and b can
actually be complex numbers
and that complex number is
what you should think about
it is it means it's a wave.
So I like to think
so classical computing is
like a set of information
quantum computing is effectively
a wave of information.
So now when we do this
computation, what we do
is we map superpositions of bit strings
to superpositions of bit strings.
So in this case I can have
these two different numbers
and when I add one, I
add one to both numbers
at exactly the same time.
So in the popular press, you often read
that quantum computers are very powerful
because they can do infinitely
number of computations
in parallel and this is
what they're talking about.
What's, as a specialist,
what's frustrating
with the popular press
is how to get from that
to an answer is actually quite hard
and that's actually the
challenge of building
really good quantum algorithms.
And the reason why it's hard is that here,
if I measure, okay, what output did I get?
I either get this number, which is four,
or this number, which
is five and that's it.
I don't get four and five.
So that's a challenge.
And then the second thing is
the state cannot be copied.
So within the middle of your algorithm
you're like "ah, I'm not
sure what I want to do next,
so I'll just copy the state"
and maybe try a bunch of things.
You actually, it's forbidden
by quantum computing,
quantum mechanics. And so
these are kind of limits,
I would say.
And now, of course, the
quantum computers totally
subsumes the classic computer
because as long as I don't
make the superpositions
of bits, I can do everything on this side
but this is why it's
challenging think in algorithms.
So when I think about
kind of what you need
to build a quantum
computer, you need waves
to control bits and qubits,
you need waves to measure their outcomes
and then you need some way to scale
the many, many possible quantum bits.
The reason, the challenge
I would say right now
is primarily scalability for
ions and superconductors.
I think for some of
those other technologies
that I mentioned, they still have trouble
with saying they're
two cubic gates or not,
it's as good as I'd like to see.
So what makes it hard to build?
So the problem is, you know,
humans in a, basically,
humans didn't find quantum mechanics
until the beginning of the 20th century.
Why not?
What were we doing for those
hundreds of thousands of years?
Well, the problem is
day-to-day, everything seems
really classical and the
reason it seems really
classical is that these quantum
states are very fragile.
So just a little bit of
noise really ruins it.
So what I wanted to try
to get across in the next
couple of slides is why now
are all these companies
and everybody so excited?
What has happened?
So quantum computing, basically,
there were some early ideas in the 80s.
It became really, somehow important
when in 1994 Peter Shor
showed that you could
factor really large
numbers very efficiently
with a quantum computer
and that's important
because a lot of our internet security
relies on the hardness
of factoring a really
large number into its primes.
So if you had a quantum computer,
mathematically we know
it would work quite fast.
But we also already knew
that things were noisy
and so, actually, Peter Shor, Steane
and then Robert Calderbank
is a professor here at Duke
in the 90s showed how you
could make arbitrarily long
computations by using
error-correcting codes
and then Daniel Gottesman
really formalized this
in his PhD thesis nicely.
And so what's great is we
had a way where you could
take classical codes that we knew about
and generate the quantum
code which could fix
against all of these
problems we could have.
Now, classically when we
think about the storage
of information, we can
also think about storing
information in materials
that are very resilient.
So my favorite example is always
Mesopotamians versus the
Egyptians. So the Egyptians
put everything on paper. We
don't know as much as we should.
Mesopotamians wrote everything on stone.
We know-- we had, like,
crazy stories of parents
divorcing their children,
which I guess you could do
in Mesopotamia, which we only know because
they wrote it in stone.
Interesting, it's very interesting to me.
So one way to store digital
information is magnetically.
Very soon, I mean this is almost already
an antiquarian slide since nobody has
a magnetic hard drive anymore, right?
But people were curious,
could you make kind of
the equivalent of a magnetic hard drive
for storing more information?
And the answer was kind of disappointing
which is they found that it had to be
a four-dimensional
object and since we live
in three-dimensional space,
it is a bit of a challenge.
(audience chuckling)
So what's amazing to me and
I think, like a great example
of basic science, this, this
idea of these four-dimensional
stable memories and then a very weird idea
which is, what if
there's a type of material
that if you could make it
someone could do computation,
quantum computation,
only by measuring it.
You don't do any operations.
The material itself,
somehow, already has all
the correlations for all
possible computations.
So two guys, Raussendorf and Harrington
put those two ideas
together and they realized
that if I had this kind of normal system
of qubits in two-dimensions,
I could make something
like a quantum hard drive
as long as I had feedback
and what's great is,
like previously, instead
of having to have gates that
were good to part per million,
you only had to have gates
that were good to about
one percent. And the way
they did operations,
it's all really strange.
There's holes in this paper
and they twist things around each other
and that's how the computation happens.
But this was, I think,
a really seminal result
because before that only
crazy optimists like me
were interested in quantum computing
'cause I was like, "we can get
to part per million, why not?"'
Now at one percent, anyone could do it.
Shortly after that, kind
of two things happened.
So on the superconducting
qubit side of things
their gates and ion shut gates both got
to better than one percent.
So because of the previous result we knew
getting to one percent,
everything should work.
So right now we're in this
huge growth of industry.
Here, at Duke, I'm really interested
in ion trap quantum computing.
We have this National
Science Foundation grant
which we just call STAQ.
These ions, each of these
ions are individual atoms.
They live above these surface trap chips
and we have prototypes
already here at Duke
and we're working at building these
up to roughly 30 to 60 qubits.
So then I just want to end
with a couple of news stories.
That's too red.
It's nice to have the Herald Sun come by
and say that we were
actually taking on IBM,
that was pleasant, and so
Jungsang Kim and I have,
this is our ion trap lab and Iman Marvian
just started as an Assistant Professor
in Electrical and Computer
Engineering Physics
and then, you know, this WIRED article
which came out this summer.
I really like this quote.
"Of all America's issues right
now, it was quantum computing
that brought Democrats and
Republicans together this summer."
And so if I can reduce partisan rancor
(audience chuckling)
I feel like my job here is done.
Thank you.
(applause)
