Thanks.

As David said Thanks for
having me Spencer Falak I'm representing

the cost of quantum systems of vision
which is right here if you want to.

Ask them any questions after the talk and
I'll be talking about advances in AI and

trap quantum computing and it's no concern
if those words don't mean anything to

you at this point the talk is intended to
be you know soup to nuts introductory.

You know what is quantum
computing what are trap diets and

how do we work with them.

On that note that's a question I just
asked which is what is quantum computing

and how does it compare to classical
computing So for us classical computing

means by an area logic with transistors
the same type of logic that goes

on in your cell phone or
your MacBook Pro or your F.P.G.A.

and I'll talk a little about quantum
computing it's a completely separate

paradigm where we use a new type of bit
called quantum bits to solve different

types of problems that you can't solve on
your own your best classical computer.

I'll talk next about trapped which
are our choices of quantum bit

talk about why we've we use them
what are their advantages and

how do we control them to sort of change
their state in the same way that you might

change the state of a classical computer.

Next I'll talk a bit little
bit about scaling so

how do we go from our current
experiments which are a few

a few quantum bits up to two hundred so
maybe thousands if hope.

One day that will be the case just like
you know there was an exponential speed up

in classical computing and then finally
highlight one of our newest algorithm

demonstrations very briefly just to give
a feel for where the fields that but

also how far we have to go to
really do a powerful computation.

And I just like to point out how
multi-disciplinary this field is and

I know there are a lot of students in here
from different backgrounds and the talk

is sort of split up into thirds where the
first third is more computer science and

then we get into atomic physics and then
sort of what the Nano seminars are more

about electrical engineering which is
using micro fabrication as a tool to

to advance our research and
the goals of the field.

So here's a little bit of the history
of classical computing and

this is something that you might see on
day one of electrical engineering class.

But what's useful for us is to sort
of look at it as a comparison for

quantum computing and to see maybe if
we are following the same track so

classical computing starts out in the
seventeen hundreds with the introduction

of binary logic followed by Boolean
algebra and the eight hundred fifty S.

and then maybe one of the most important
inventions of the twentieth century is

the transistor at Bell Labs by Bardeen
Bradley supervised by Bill Shockley.

And then in one hundred fifty eight
with the first integrated circuit.

From nine hundred sixty five until
now we've seen incredible growth

in classical computing there's
you know companies like Intel and

Moore's Law which say we can double our
device complexity every year not here to

talk about Moore's law there are certainly
skeptics of the future of that but

I'm here to talk about a completely
different paradigm and

maybe something that's separate and
you know today we have incredible

supercomputers that you know have twenty
Petta flops of logic operations that

can do incredible things and
I'd like to compare this waiter at.

To where we're at in
the quantum computing field.

So there are some limits
on the way that we've

approached scaling we use lots of
large clusters operating in parallel

to solve some of the most complex problems
we can add graphical processors or

F.P.G.A. to supplement our cluster but
there are certain problems that aren't

really tractable with this type of scaling
and in one thousand nine hundred one

Richard Feynman propose that there are
some problems that could be solved better

with a quantum bit with
a completely different style.

And that brings us sort of to the history
of quantum computing which you can

sort of make a comparison to
what I just showed you before.

The one thousand eight hundred Max Planck
quantizes light to account for

black body right eight radiation
there's big names like zero and

showed in German Albert Einstein who sort
of formulate modern quantum mechanics and

then eighty years later Richard Feynman
publishes this quantum computing lectures

which talks about in a very abstract way
about how there could be such thing as

a quantum computer and it takes another
fifteen years before this sort of abstract

idea becomes a reality when researchers at
the National Institute of Standards and

Technology perform the first two quantum
bit logic gate and it's for this work that

Dave Wineland who's a physicist at NIST
won the Nobel Prize a few years ago and

you know in the past ten years since that
sort of the field has started to take off

but it hasn't been very quickly it's sort
of the same way that classical computing

take has taken off and we've gone from one
sort of gate and now we can sort of do

algorithms with three to five quantum
bits very small number of bits and

what we're hoping for is the same type
of rapid growth that classical computing

saw from one nine hundred sixty S.
until now where we can scale up from

five bits to thousands hopefully.

So we talked a little about quantum.

Computing in the history but
what is it actually useful for and

right now it's a bit unknown there are a
few algorithms that have been theorized

that will be much more
efficient on a quantum computer

the key algorithm is Shore's factorization
algorithm which I'll talk about a bit

about in the next slide there are some
other applications here the discrete for

a transform simulating quantum systems and
then solving linear systems but

the current currently the quantum speed up
is limited to only a few algorithms and

we expect or we hope that as the field
expands they'll be more algorithms

that sort of bootstrap along with
the development of new technology

that can also surpass their
classical counterparts.

So here's a source factorization
algorithm and this is probably the king

of the quantum algorithms because it's the
one that's most interesting to the federal

government and the reason is that R.S.A.
public private key distribution

assumes private factorizations
computationally hard are saying corruption

is ubiquitous it's used on Internet
security cellphone password things

are not source and so for much of your
bank account password things like that.

And the intelligence community has very
strong interest in an algorithm which

would either render this type of system
useless or maybe come up with a new

way to avoid other countries from
breaking our current encryption code so

here's an example of classical scaling and
quantum scaling for

factoring a prime number and
as you can see as you add more and

more digits the quantum scale is far far
better than the classical scaling it takes

far far fewer number of operations to to
factor a number Quantum with a quantum

computer than it would classically and
this is sort of why this field took off so

so quickly and here's sort of like
a an example you know what's the biggest

number that one can factor in
a reasonable amount of time.

If I sat down with a pen and paper maybe
I could do a three digit number and

your MacBook Pro could maybe do a fifteen
digit number if you wrote a nifty little

algorithm and then the best published
computer could do a two hundred

thirty one digit number and we're hoping
that you know a quantum computer could do

much much larger more complicated
number in a reasonable amount of time.

So we've talked a bit about
quantum computing and

maybe a few of the algorithms that will
surpass a classical counterpart but

what exactly does that mean what is
the quantum bit how does it work.

And there are two key components that I'll
talk about the first is superposition and

the second is entanglement and
these are two.

Key points to describing quantum bits so
what's.

So here's an example of a classical
bit that has a value of zero or

one you can make
the analogy to a switch and

it just turns on and off and quantum
mechanically we have this principle called

superposition which I'll talk
about in the next slide.

But here let's look at an example of a two
bit word which holds for value zero zero

zero one one zero one one one and then.

We can look at a quantum bit and
we describe it with this

state notation side where these are
complex coefficients A zero and a one and

what you'll notice here is that each
quantum bit of this quantum state that

we've written here can store information
about both zero and one simultaneously.

But there's sort of a weird Kaviak
which is if you measure the quantum bit

the state will collapse into zero or one
with probability given by the magnitude

of these coefficients squared and the
analogy I like to use it's not a perfect

analogy because these are quantum
mechanical States but is a weighted coin

a sort of like you have a heads or tails
and when you flip the coin it away and

either heads or tails and
you can change the weighting of that coin.

About how we do that
with quantum bits later.

And then to return to the example
from the previous slide

if we look at it two cubits state it
now requires two to two squared for

complex numbers or two to the third
real coefficients to describe So

this is more information in each quantum
state than that in a classical state and

this is sort of a powerful implication for
computing.

The second key point I'll talk
about is entanglement and

this is the ability to link to
quantum bits so that their states

cannot be described separately and
there really isn't any classical.

And that like comparison to
this in classical computing and

the analogy that I like to use before
which is if you had two coins and

flipped them they would both land heads or
both land tails together and

never opposite from each other and
this weird entanglement is something that

troubled Einstein and other researchers
in quantum mechanics for a long time but

it's it happens to be a very key component
to doing a quantum computation so

let's return back to our outline for

a second I talked a little bit about
what was quantum computing and

now it's sort of what are the key
components of quantum computing but

now all talk about trapped ions and
trapped ions are what we use a teacher or

I for quantum bits and
I'll sort of highlight how we

control the trap Dion's and how we can
generate superposition entanglement to do

interesting algorithms
with these particles.

So just before I dive into trapped
on thought talk a little bit about

the technology that people have used over
the past ten years to as this field has

grown people have tried to make quantum
bits out of polarization States and

photons circuit in superconducting loops
to the electron gases quantum dots and

then.

Energy States in neutral atoms and

here's a picture of a lattice of
neutral atoms and then here's a.

Picture of chain of trapped ions
which is what we have a D.T.I.

this is a picture from our lab and

over the past ten years it's sort of
become clear that superconductors and

trap diamonds are the most promising
candidates they have their

trade offs superconducting cubits
are incredibly fast you can do a quantum

operation in nano seconds whereas in trap
times it's on the order of microsecond but

the coherence time which is really
the ability to to do operations.

Coherently with previous operations and
read them out coherently is far longer for

trap Dion's than it is for superconductors
and sort of as an aside this you know

picture of five or six cubit
superconducting circuit at Santa Barbara

is their team has recently merged
with Google to continue this work so

it sort of gives you an idea of
the hype surrounding this field and

sort of where people think it might go.

So moving into trap Diane's.

Cubits are the species that we're using.

We have calcium terbium and
we're starting work with strontium.

I'll talk mostly about you terbium
that's the chapter on that we've done

the most interesting
algorithm work when recently.

And so
let's get in now to how do you trap and

so here's a really small
picture of an eye and trap and

I'll talk about that in the next slide and
we put the trap into a vacuum and

the reason for that is each eye obviously
doesn't weigh very much and if it

collides with the particle an area could
get knocked out of the trap it's also very

sensitive to magnetic field fluctuations
so we put magnetic field shielding and

biased magnetic fields around the vacuum.

And then what else do we need to trap and
we need laser cooling to cool it down and

then of course we need a source of
neutrally terbium or calcium and

photo ionisation laser kicks
an electron off the neutral source.

To turn it into an island.

So here's a picture in of the trap that I
showed you before this is the classic for

a trap that's pretty popular among groups.

And what's interesting about it is this
radio frequency all RAF electrode pair and

you think why do I need to put radio
frequency to to trap in Iran and

the answer is you can't actually make
a three dimensional potential little so

that's how we trap the UN in
a electric potential mill.

With static electric fields by the you
know some of the fundamental questions

of electrostatics so Wolfgang Pauli
to determine that you could use

radio frequency voltages to create
a ponder motive force which is sort of

a net inward force that's created
by this R F A lecturer and

here's a cool little video of an eye and
bouncing around in.

You know the profile view of that trap and
it's oscillating at

the two different frequencies I'm only
showing the slow frequency here and

then you there you can see sort of
a cartoon picture of laser cooling and

its extent of its oscillations will
reduce over time the mechanical analogy

that Wolfgang Paul uses in his Nobel
paper is that of a ball on a saddle and

the idea that if you put a ball on
the saddle and spin the saddle fast enough

the ball will not slide down
any of the sides of the saddle.

So we just looked at this trap here and
now are looking along the axis of the for

a trap and there are certainly some
disadvantages which when it comes to

scaling a trap like that you can see here
there's only three segmented electrodes

we use these electrodes to move and move
on trap which moves the irons back and

forth left and right there's also some
difficulty in the lining these electrodes

you know they're macro structures maybe
the size of a couple millimeters and

in the mid two thousand
researchers at NIST

determine that you could
actually fold the structure as.

Into a plane or structure and the word
planner probably means something you know

a lot in this building where people spend
a lot of time working on silicon chips and

it is enabled us to take some
of the technologies from.

Silicon chip manufacturing and
utilize them to our benefit so

how do we do that we've this is a cartoon
drawing of one of our chips and

what the planners ation does is
it allows us to make many many

electrodes these short segment electrodes
which we can use to move the on back and

forth and there you see the R.F.
potential that I discussed before and

here you can see sort of the layering
build up of the of the silicon ion trap

chip Here's a colored in picture of one
of the chips that we fabricated here

at the nanotech research center there's
hardly Hayden who's our chief bad guy

who made this trap and here's a zoomed
out picture of it it's one of our

older traps the Gen three trap and
you can see the slot in the center

you can't really see
the small lecture it's here.

In here a lot of capacitors which we use
to filter out noise pick up on these

on these electrodes and
wire bonds out to the external world and

I'll talk a little later about
how we've modified this design

over the years to remove some of these
capacitors or make them less bulky and

then move the wire bombs away so
we have more access for lasers and

other techniques that we've
used to improve this design so

here is a video from two thousand and
eleven two videos which show you

sort of what you can do with these traps
and it's pretty awesome this is a chain

of islands individual islands sort of
getting merge and split back and forth and

therefore wrestling with the cooling laser
light and you know someone who goes and

sees this every single day it kind of
loses its alert maybe a little bit but

if you it's pretty wild to think
about that you're looking at

an individual particle on your screen.

In here getting pushed back and forth and
controlled it's location being controlled

really well and here on the bottom
you can see as we load it chain up

the ions are coming up from the neutral
oven source we talked about for

the ionized and then getting transported
over there to build the chain.

OK so now we have ions in there in
the trap but this talk is about quantum

computation so how do we make
superposition how do we make entanglement

with these individual and so here's
an energy level diagram of you terbium

and there are two relevant transitions
I'll talk a bit about today there's some

added complications as well but the ones
I'll focus on are this transition at three

hundred sixty nine point five nanometers
and this one at twelve point six you get

hurt in the microwave band and here we
have two energy levels of very terbium

mine which correspond towards zero and
one quantum states of our quantum bit.

And this top transition is our detection
transition it's how we flip the coin or

measure what state our quantum bit is in
and then this is our gate transition and

if we shine resonant radiation here we
can switch the state of our quantum

bit from zero to one.

So how do we figure out whether our
quantum bits in the zero one what

we do is we shine through in a sixty
nine then a meter light onto our own and

if it's the atom is projected into the one
state it will scatter photons which we

can collect on a P.M.T.
for the multiplier two and

we'll get twenty photon counts histogram
like that if we repeat it over and

over again on the contrary if
the atoms in the zero state and

we shine the same light this light is no
longer resonant with this transition and

we won't get any photon counts so you can
see how we can easily sort of distinguish

between these two states by saying if
we have more than eight photons or so

on our P.M.T. the Ion was one and
if we had less than a photons and zero.

OK so now we know how to
read out our quantum bit.

So how do we actually.

Change the state of our quantum
bit we use resonant microwaves to

drive transitions so this is a microwave
horn which is a cartoon drawing

at twelve point six gigahertz to drive
the state of our quantum bit back and

forth Alternatively we can use
two laser beams that are detuned

by this twelve point six you get Hertz to
drive the same transition and what will

get here is sort of a signature of quantum
mechanics this is called a Robbie flopping

curve where the the state of the eye and
goes from zero to one and back and forth.

Just like the cartoon.

So what's illustrate a little bit
about superposition which is what we

talked about before so

we here at this point on the curve we know
what are our history to look like it'll

it'll be we'll get almost no counts
because the items in the zero state.

And at the top will get a bunch
of twenty photon counts or

so because I was in the one state but what
about here on the raw beef up in curve and

this is sort of where the power of quantum
computation comes in is half the time

we're going to get of a bright histogram
and half the time going to get a dark

histogram and that's the state
described here where each.

State is each level is described
by this probability coefficient So

that's something that you
really do not get classically.

So let's return to our
trapping out of apparatus

I've replaced the old classic
trap with one of our G.'s here

I had micro fabricated traps
now we're strapping in terbium.

But we've got to add a bunch of
different things now we've got to add

radio frequency electrode for confinement
we've got to add one hundred diddle

digital analog conversions to move ions
back and forth with the outside electrodes

we've got to add a big lens stack
to collect light for detection

to measure what state our quantum bit was
in and we've got to add microwaves or gate

lasers to perform quantum logic so this
picture has gotten a little bit complex a.

And what does it actually look
like in a lab it's pretty wild.

You got optical fibers going
everywhere this is one half of one

room that's filled with tons of equipment
and here if you zoom in on one of our

traps you can see the magnetic bias field
you can see the lens stack to our P.M.T.

you can see the digital analog converters
coming into our vacuum chamber and

the radio frequency electrodes.

So that sort of brings me to the next
point which is scaling and the purpose

of scaling isn't necessarily to shrink
the size of the equipment in the room but

it's to get more quantum bits that we
can control with higher fidelity and

detect faster and
with better detection fidelity and

the technique that we've adopted
at is to build integrated service

a lecture traps which is to take some of
the components I just talked about and

smush them into our service electro trap
and use micro fabrication as a tool for

example we can replace the microwave horn
with integrated waveguides into our micro

into our our trap so we don't need
a horn radiating onto our vacuum instead

we just send microwave lines
into our trap and can do.

Quantum logic with the built in waveguides
and this is something that we did in

two thousand and thirteen so
now highlight three of our most recent

integrated trap chip trap designs some
that we've done in-house some that we've

done in partnership with
Honeywell International and others which

really try to solve some of the scaling
problems and make life easier for

the experimentalists So the first thing
that we've worked on is trying to improve

the detection issue and the the picture I
just showed you you know in real life was

a big one stack going into a mirror in
a P.M.T. or onto a camera which is how we

saw the ions moving back and forth before
so in two thousand and eleven we said Well

we're we're not really collecting very
much light from the atom when we get.

Twenty photon counts in two hundred or
four hundred microseconds it's it takes

quite a while to figure out what state are
I in is in so what if we put a mirror onto

our trap surface we can collect
more light from from the eye and

as we detect it and here's a cool
video of an eye in transporting

over the mirror that we put onto this trap
and you can see as it surpasses the mirror

we start to collect more light that's
actually reflecting off the surface so

this is one idea that we had to use
integration of microfiber cation to maybe

reduce the load and
speed up this process so the integrated.

Trap was it worked well but
there's these funky electro designs and

it's sort of difficult to manufacture so
recently with partners at

the friend Hoffer Institute as well as
a group at the university we decided

to replace the mirror with the fact of
optics on the surface of the track.

And here's a picture of the chap that
I believe it was fabricated here and

the mirrors and the colonnade and
other diffract of optics were patterned in

Germany was yeah so
here's a picture of the track it's

got the key component here the collimated
which collimate some of the light from

the eye and so that we can collect more
this is really the name of the game and

researchers at Griffith University
using the strap were able to actually

adjust the height of the eye and to get
a great diffraction limited limited image

of the eye and so which isn't really
particularly useful but if you but

you could add a couple to the light
from the eye into a fiber but

it's also very interesting
to see that you could.

Collect more light for faster detection.

So the second idea we had was to take.

The hundred digital analog conversion
converters which are really bulky and

this is sort of a picture
of how we wire up wire.

The one hundred channels from that
card onto our trap chip used to

move ions back and forth and this I mean
there's so many wires here trying to get

that all into a vacuum while keeping very
high vacuum qualities a difficult task so

what we decided to do was
to put off the shelf.

Digital analog converters integrated
with the chip integrated with

the ion trap chip into the vacuum and
this way we could reduce the overhead

of the the number of wires here and just
replace it with the serial interface and

you know add two channels you get forty
more digital analog converters and instead

of using a ninety six vacuum few things
are only using nine vacuum Peters now.

So here's a picture of some of the work
done in Honeywell International where they

took some off the shelf
digital analog converters and

actually DI capsulated them with acids to.

Put onto a Rogers P.C.B. board and this is
one side of the P.C.B. board on the other

side is our trap chip where you can
see in a nice compact package here and

this is sort of another way that we've
used this is necessary microfiber cation

But you know try to scale up and
reduce the overhead

on the experimentalist and
make things much more easy to work with.

In the final trap that I'll talk a bit
about today is called a ball grid or

a trap and the purpose of this is to
reduce the area of the trap itself.

Instead of using those planar capacitors
which took up a whole large portion of

the trap here we can replace them instead
with through substrate vias and capacitors

there so now we move to wire bonds for
a laser axis way down to interpose or

in the trap chip area takes up a far
smaller amount than it did before and

this allows for tight laser focusing so
you can hit one at a time do Gates on one

at a time I've actually got a bum
version of the strap here we can pass it

around it's got some additional wire bond
scratches on it but you can see what it.

It's like in real life.

And here's a picture of the trap here you
can see the radio frequency electrode

coming in here and here is a picture
of some of the trench capacitors and

the through substrate vias that we've used
and the key takeaway here is we've taken

this whole surface and reduced it
down to a very small area here so

that we can focus lasers more tightly and
address Adams individually.

So what we want to do with all
this I mean this isn't just for

the sake of what it is for
the sake of making life easier but

it's also about making
a complex quantum system and

the idea here is to smush all of these
things together that we've built so

we would add the gates region where we
have tightly focused laser beams that I

just discussed we have a detection reason
we read out our quantum bits with diffract

of optics we could have junctions which is
a trap that we built in two thousand and

thirteen here at Georgia Tech where
you could rearrange your eye and

chain and move them back and
forth sort of like a register.

And then if you really wanted you
could integrate optics you know

into the trap surface to do gates and
we haven't really worked with that yet.

OK So back to the outline.

What we talked about thus far we talked
about a little bit about quantum computing

we talked about trap Dianne's and
we talked about scaling but

now I'd like to give you guys a and
feel for where we're at in the field.

You know it's going to take
a large number of quantum

bits to do an interesting algorithm.

Unfortunately we're not quite there yet
but

we're we've been pushing the frontier
doing cutting edge research

on basic quantum algorithms and
that sort of where we're at right now and

here's sort of a path to where
we've come from two thousand and

eight all the way to now we started in two
thousand and eight doing trap development

here's a picture of the same junction
it was one of our older ones yeah.

Then we started doing single
cubit control in two thousand and

twelve two thousand and
thirteen and then we started.

Making scalable traps with
integrated features and

simultaneously working out a bit control
three four five quantum bits and

then now we're working on simple
algorithms with two or three trapped ions.

So the algorithm we've been working on
lately is called the Bernstein Vasser Ani

algorithm and
how it works is sort of a guessing game or

trying to figure out
an unknown bit string ass and

there's a black box Oracle and
the Oracle gives you.

Two So you were X. is the string that you
input so you say to the Oracle I here's

my bit string X.
will you give me this this answer out X.

two and the goal is to figure out
what the hidden string is here so

for example you could Querrey your
Oracle with one zero zero and

the if asked for a one one zero you
would get out one and to determine S.

precisely you need to run this three
times once with one zero zero and

then zero one zero zero zero one So
it takes three runs of the algorithm

to figure out a three bit
string as a hidden string.

Following this quantum circuit which I'm
not really going to describe you can

determine S. in a single query so
it's a simple algorithm not really

factoring large numbers but what it's
doing is trying to figure out a show

a quantum speed up on a small scale system
one that we can hopefully expand later.

So here's a picture in a paper
that we published recently

showing all of the gate laser pulses that
is required to do this algorithm and

each one of these purple bars represents
a single cubit rotations seventeen of them

this is an untangling
operation in orange and

it takes six hundred fifty gate laser
pulses nine point eight milliseconds and

nineteen transport operations
to do this simple algorithm so

what's my point here this is it we're
working with three bits here but

it's it's really difficult research
there's lots of control that has to go in.

There's lots of you know stabilization
keeping the environment outside of our

quantum bits and classically from the from
this algorithm you gain one point

one bit of information every time you run
it quantum mechanically we showed that we

can get one point one bits of information
so a slight improvement over the classical

or the right what's my point here is
that this research is cutting edge but

we still have a lot a lot of work to do
to do really powerful calculations and

that's something that we hope
to be at the forefront of.

So in summary.

We've got quantum computers which can
supplement classical computers to solve

certain key algorithms the main one to
take away one is factoring which is

important to the intelligence community
entanglement in superposition are the key

components of a quantum computer
which enable this type of speed up.

A G.'s your I we use trapped.

Their strong choice of cubit but
scaling is difficult we've

just started to work with short
chains of three to five ons and

to sort of scale up our focus has been on
building integrated chip traps using micro

fabrication to our advantage to bring
things into the vacuum system itself and

finally basic quantum algorithms with a
few quantum bits are within our reach now.

The speed up is nothing to write home
about but hopefully within the next few

years we can start expanding our systems
and do really cool calculations.

This is our team here I'd like to thank
particularly Jason to me Adam Meyer and

to Merrill who made some of the slides
in this talk and thank you.

Two mature superconductors in the.

Simple energy level structures that
resemble hydrogen when you take one

electron away is sort of a criteria for
us and what a good two level

system isolated to level system that can
represent a quantum bit well as your own

one.

Where you put the neutral
the terbium are cast and.

Yet it's unlike a metal of unsourced
which we heat up with current to

spew off as as a gas.

It's not on the chip No it's below
the chip and it comes through a hole.

That gas comes through
a hole into the yeah.

Yeah.

I didn't talk a bit about that it's
a little bit more complicated there's

a procedure called a moment Sorenson to
keep the gate which we like to use but

we actually use the motion
of the atoms as a bus to

to connect their internal logic states.

And I can talk a little bit more
about that later if you're interested

in.

Yeah.

They can decode here there's

heating which occurs on the motional say
such a heat up if they're not cooled and

they can actually leave the trap it's
also just hard to control you know and

laser beams that are directed at all
the different islands have to have very

precise frequency very precise phase
to do quantum logic gates as have many

other things you want to add to the list
goes on and on to be ousted him.

Yeah.

Right so that's the sort of the the
question it's not really a question it's

the trade off in and doing these types
of computation is that you can actually

propagate your input which represents
many states at once so you can so

you can run many calculations in parallel
but at the end it's going to collapse into

one state and the trick to
creating smart quantum algorithms

that few people have determined is how to
trade off these two things so that you can

propagate through the calculation in
parallel and still learn something about

interesting about your your system
with the collapsed measurement to some

extent.

Yeah for us decoherence is
actually not a big problem.

It's we have coherence times
on the order of a second or

two the superconducting cubits are the
ones that have the coherence problems they

do go here in there so
our problem is more about

getting lasers that can do
coherent interactions and

maintaining a static environment
without magnetic field fluctuations.

Yes' or anything else because well yeah.

Sure.

Yeah they superconductors tend to be much
faster they can do their gates in seconds

whereas it takes us microseconds or a few
microseconds However they're decoherence

is much more quick but if you look at the
ratio of the number of coherent operations

you can do to the coherence time it's
similar for the two technologies.