Well, hello and welcome back to Impact Quantum, the podcast for the quantum

curious. We. We firmly

believe you don't need to be a PhD, although it certainly helps

to participate in this emerging field. And with me is the most quantum

curious person I know, Candice Gahooley. How's it going, Candice?

It's great. Thank you so much, Frank. You know, today

it's crazy. This week actually we got a massive, massive snowstorm.

And although where I am in Montreal, Quebec, we always see the

first flakes for Halloween, we normally don't have such

a deluge. I probably have nine inches outside.

It's, it's just crazy. So I'm all looking at the beautiful

snow, but I'm getting my, my head all in gear to have a great conversation

today. I'm really excited about our guest. Yeah, awesome.

It is chilly down here. We didn't get any snow, although I think in Western

Maryland they did get some snow, but it is chilly here

and I may have to fire up the GGX to do some fine tuning

to heat up my office as well as turn on some of these monitors.

So who do we have talking to us today? Candace. Right, so today we're talking

to Mahmoud Sabuni. He is the lead

quantum processor engineer at

oqd. Very cool. Hi, how are you today?

Okay. Yeah, thanks for having me here.

Yeah. Here also we have a little bit of snow, like last couple

of days still like leaves on the trees, but you could

see the snow and then snow and then after that you see the

leaves on top of the snow. That's kind of interesting feature that

you could see. So you're also in Canada, right? Yeah, yeah, in

Waterloo, Canada. Okay, very cool. For the people who know, don't know,

like Waterloo, where is it? Like close to the Toronto, like 70 or 80

kilometers south. Yes, that's.

I'm here from probably almost like 10 years.

Start by like some postdoc and some other activity at

Google as the optical engineer and then back

to this open quantum design startup

in Waterloo. Oh, very cool, very cool. Your

LinkedIn is very impressive. So I have plenty of questions around

that. Open quantum design.

Tell me about that. That's an interesting.

What does open quantum design do? Or. Yes, that's.

Yeah, that's probably the core of the Discussion that we can go through.

First of all I'm more like a hardware person. Like I worked more on the

hardware side on like quantum information since my

PhD which was in in Sweden in Europe

and also my master there around the quantum

information technology and like storage

basically quantum memory during my PhD and after after

that I came to the like quantum computing parts

and as a postdoc here at iqc.

And yeah I just put some gap for the

Google time which was more classical optics and hardware. But later

like there is some three PI here in Waterloo.

Two of them Crystal Senko and Raj Wolselm working on the

Ion Trap machine and one Roger Molko

working more on on more AI and software side.

They decided to start making like an

iron trap based full stack quantum computer.

This company start from like a February 24th and

I joined them at the at the same time then we

the idea is to have a full stack quantum

computer based on iron trap available for anyone who

wants to rebuild it. Like the

way that we do is that we have like some software team

and some hardware team. I'm more on the hardware side and we are

developing like some prototype and at the same time we

put all our designs on GitHub available

for like anyone who wants to rebuild or use

that kind of module for for his setup in future.

And the the big picture is to like do the same thing

that happens for either software or some even hardware

in the classical computers in the quantum

computers. Like there are some like you can explain more details like

what kind of reasonings is behind this will be successful or not

that. Yeah we don't know but we're pushing that. And during

last year we could reach to some good milestones.

We could get some collaborators like

partners. We have like five different companies already

putting money or full time employee on this open source

activity and we are pushing towards

getting our first aspect of the machine out soon.

Hope like January 26th that shows okay this machine

is already live and available and yeah

looking for more people to come and contribute to see

how we can push it forward. That's the more like

a central activity of the open quantum design.

So in practice

what does open quantum hardware design mean

in practice and how is it different from traditional

closed or proprietary approaches in physics

research? Yes like

if we want to make very similar example in the

classical world I could bring example of the

RISC V company that's a company

that actually we have some people from there also like

with the same idea came to the one quantum design as a board member

to Push the idea here also in 80s and

90s there was like discussion about the

designing of the CPUs. Like different

companies like closed or open just start

to work on that kind of architectures. And then

at some point RISC V as an open source company

start building the standards or

designing chips. And these days like that

the RISC V it has already

70 plus members. And then most

of the CPU design in Intel AMD anywhere else

it's using those standards that's built on the open

source activities. The same idea

here also could be in principle

implemented. Like for example

benchmarking of the quantum computer is something

which is very tricky. Like different company. Like

+10 different companies already announcing different

like machine. And then we don't have still

fully standard benchmark system to do

the benchmarking on different machine. And

one of the reason is that those machines are closed and that

no one have access to those machines to test and

benchmark them. Then if they have some machine at

least in some of the architectures like Ion Trap

or in future could be other others also you

can run your algorithm on that machine and

tested machine and then test your standards and then write the standards

based on those open access machine for anyone around the world.

And then the rest of the people can also use that. This

is not against any like a

IP based company also like they could also get benefit of this

open source and build the standard around that.

Because around this like a point people can come

and exchange idea and also develop

whatever that they have done so far. And then

integration of those like multi

idea people can

in principle maybe outperform

the flow system.

That's the idea behind this. But there is some other

feature also would be interesting from hardware point of view. Because from

software point of view you can see a lot of comparison

around the board. For example Linux versus Windows

like maybe the top

like the most important project around the world

running Linux, not Windows like Linux is the open source

that's in the software. It's very clear that

that's a path that can be very successful

in the hardware regime. Especially in

quantum computation.

Very expensive hardware you need to develop. And then the open

source community will have difficulty to

gather those together and then test whatever they

want to do on that hardware. The idea on OQD

is to make that kind of test beds for

anyone who want to work to single

details of the machine. Like

other machine that is available already mainly is cloud based.

You can run some algorithm on those but

you don't have access to full ingredient of that

machine. Yeah, that's simply because of the IP reason.

But here everything is transparent. You can see

each single module.

For that reason it will be easier to

first benchmark it second also learn it

from this hardware available. A lot of

people like a software oriented that they are interested

to run some calibration on some real quantum machine.

They can do that and

that's a big benefit on the learning curve.

Like build some better workforce development.

Or even for closed system they can use this kind of people

who know it's capable of doing

something which was not possible without access to the hardware.

That's one big plus. And also

like for example colleges that they

want to train some quantum engineer at the bachelor level that

they want to handle quantum machine.

They can use this kind of system. Like they cannot

go and buy some quantum computer from like big IP based

company and then have access to all full details because

it's IP based. But here they can have

machine and then run it and learn it

and improve it for whatever

purpose that they have. This machine is not necessary to be the top best

machine in the world. It's just need to run some simple

functionality. And

because of the reason that I mentioned like benchmarking, you can do it on a

simple machine learning, you can do it on the simple machine. And

also workforce development also you can. Do

on this machine with the open design

transparency, open source. Do you find that this

accelerates innovation? That sharing

these designs actually helps solve complex engineering

problems, perhaps faster? Yeah, that's the idea.

If you want to compare it in physics person. And then I

was comparing to some phenomena in. In nature, like

comparing the light bulb to the laser. Like the laser

happened whenever that you have phase coherences of the

photons on top of each other and they will

amplify the the final result here also

that could be a. Yeah, it's. It's difficult to prove

it, but there are some example as I mentioned, like

software developmentally and also hardware like this

RISC V versus ARM or Intel

that shows that was successful here also there's a good

chance that can be successful and this comes

from like University of

Waterloo. Anyway they spend time on

development different sections and different modules.

And that would be good to share it with the academia first for

sure. And then why not share it with everyone

and then try to. Also we

also could benefit a lot. Like for example we have

some locking mechanism under our laser

that we don't have much and bandwidth of the people

to develop that. But someone else could come and say

oh, I did this with all single details. Not

just a paper, not the published paper or archive version, just with the

details of the electronics, mechanics, optics,

diagrams and share those information and then

we can build that system in here. On the other hand,

the other partner also can benefit from our whatever

for example our optical circuit board design which is

transparent, everything available and use that as

like resources to build its own system

in different country completely.

Yeah, we have some collaborators like from India,

from like Brazil, from a lot of countries

in the south hemisphere like coming

for this kind of options.

And of course there will be also some difficulty in terms

of politics that will be challenging to

how, when and where you want to distinguish between

these open and close and IP base. That's. That's the

still open question that yeah, there is some resistance

sometimes. So there's a lot to unpack

there. But one of the things I saw on

your website for OQD was

you referenced the term full stack quantum computer. Yes.

What is a full stack quantum computer?

Full stack means like you can.

The whole quantum computer is several

layer of different information

from your classical computer code

and then that will convert to some mid layer and

then the mid layer to very low level in the. We call it like a

meta layer that you want to talk to the. In our case to

atoms and there will be a lot of like exchange

in between. In at open quantum

design we have some partner that works on the

software like for example Xanadu as a IP

based company they provide some agreement to

OQD and provide some full time employee they can

adopt their like a software which is open source

to our hardware which also is open source.

Like we are working on OQD Aintrap using

the Arctic software which is the open source hardware

developed by. I think

it's from Maryland actually. And

these two combination could be as a full stack.

Like you can run your code high level and then it will convert everything

down to the. To the pulse level. Talking to atoms. Take the

data and then plot the data, extract the information

and use it. That's the meaning of the full stack. Like

from high level to bare metal layer

and vice versa,

open to everyone. The software

in most cases are available

like in superconducting qubit in

photonics and ion trap. It's a

little bit lagging. Like we need some software at the high

level. That's this kind of project trying to fill out that

gap. And

yeah, that's the meaning of the full stack.

Interesting. Full stack is one of those terms you hear a lot in technology

and it means different things to different people and

it Means different things in different contexts too. So that's why when I saw that

usually my reaction to the the term full stack it triggers me to

flashbacks of conversations I had with recruiters throughout the years.

So I'm like no, so

so. So I like your definition better. So okay. Yeah, it is here is

like a. Like a simpler version that.

Yes, that's more reasonable. Version two.

Yeah, yeah. Of course for like using the

quantum computer you cannot directly go to the

bare metal. You need some middle layer which is very

crucial and still needs a lot of development

connecting those hardware in terms of. Because one of

the enemies here that we have in like. Okay. Whatever

properties that it will make a lot of difficulty for

us is the phase. Phase means

like whenever that you have a wave you need to

predict exactly at. In future time what is

your amplitude. And this could jitter a little bit.

And in our hardware side

this will be crucial to control this phase.

And in the radio frequency

hardware that's It's a challenging.

That's not in our expertise like

a bandwidth in open quantum design. And we are trying to

get help from like some expert in the rf.

There is nothing available I could say

reliable for special

for ion trap that you can use it

easily and control your. Your ions or your atoms.

And even the. In

the. In the closed system also which is very expensive.

It's not fully functional to control the ions yet.

And yeah we have some gap there that we need to

fill out. Controlling the phase reliably

and in. In an open source community which is as I said is

based on the Arctic and Cyanura

blocks. It's a company for ion trap. The same like a

company like other names coming to the superconducting

qubits. Also they're a little bit ahead than

ion traps if you want to compare it in that area.

Okay. Ion traps you don't really hear.

I haven't heard a lot about that lately. What exactly is is an

ion trap versus.

Superconducting for superconducting? Like what. How does that relate. I

know it's one of the hardware kind of families. Yes. Like

for any quantum computation you need some

species. Some people using photon

which is like a photonic based quantum computing like a

Xanadu or Psi quantum. They are using photons.

Some people using a atom

directly single atoms. That's like a neutral atoms

like a Q era for example. Using this kind of

species to encode the information.

And another type of

quantum computer that using species of ion

means like you have atoms and then you

shoot out one of the electrons ionized.

Why you make it ionized? Because it will

be easier and deeper to trap it.

What the trap means like you need to have like

atoms and these atoms, single

atoms, no interaction with the environment and then

levitate in whatever area that you have. We have a

vacuum system. We push atoms inside like

evaporate atoms to the vacuum system and

then shoot the laser to the atom. A bunch of Atom 10

to 2010 to power 20 like and then ionize

atoms send electron out will be ion. And the

ion, because it has a charge, you

can trap it with the DC and RF voltage. And

then that trap is very deep. Compared to the neutral

atoms. Neutral atoms is shallower. Like if

you some other hydrogen atom inside your vacuum

system come and hit the atom can kick

it out. But in ion trap is deeper. Like you

need more energy to kick it out than if you trap

like one ion in your vacuum system. You can have it

quite long for a day or two and then do the experiment with it.

Compared to neutral atom which is like quickly will

escape and you need to trap it again and again. But

in terms of the physics they are quite similar. Like the way that we

trap is different. But in terms of physics you can find a lot of

similarity. Yeah. Compared to the

superconducting cube. Superconducting qubits are circuits.

Like in principles are electronic circuits

in the quantum level. Like they have some non linear component.

Like in the normal electric circuit we have

rcl. But they have some component which is

non linear. And they can work as the source of

making the energy differences and make a qubit. They

are handmade compared to the atoms which is

natural. Like it's very

difficult to make 10 quantum bit in

superconducting qubits similar. Exactly. Because

it's handling. But in in atomic word they

are similar in nature. In. In that sense it's easier

to to start with. But the

difficulty will come afterwards when the engineering comes. Like

the electronics supports a lot the

superconducting cubits back in AD 19. Then

there's a huge engineer development there which is

missing in atoms and ion community.

If we could fill that gap, they could

outperform the superconducting qubit because

naturally their properties are better.

That's the differences between these two. Like a

way of or three photons, superconducting

qubits and atom or ion base.

Okay. So there's a lot of precision that goes behind

the ion traps. It's very exciting. You get lasers,

vacuum chambers, electromagnetic fields,

all these mind blowing ideas all working

in Harmony. What's the most challenging part

of building or maintaining an ion trap system

at the moment? Scalability. That's the,

that's the, that's the challenging part. The

rest is already is manageable. Like you can have

recently Quantinium published like a 98 qubit.

And with the benchmarking, when I say

there is some spec of your machine that will define

how it's working in terms

of the element level and in terms of system level.

There's a huge people that working on this benchmarking

different machine. And

what was, what was the question? I forgot I was saying what was the most

difficult. The difficult like the scalability. Then you want to increase

this like a 98 qubit to 500 or thousand

or 10,000 or 30,000. How you want to do that?

That's challenging. Like different

architectures already like is

under investigation to see how we can do that.

Yeah, two main way to do for the ion trap

is either do it like a

node base, like have 100 qubit here,

100 qubit somewhere else like within like

acceptable range and then connect them with photons. Then that

way you can extend and escape.

That's one approach that IonQ and IX or

Linux recently starting that kind of approach. And

also another approach is it's called qccd.

You have core center and then you transfer

physically or ion to somewhere else and do something and bring it

back like transferring between different nodes

that you have. That's also another approach that

some company is using that approach to

reach to scalability. But still it's very difficult challenge

to overcome. And like a company put

like a 32,000, 2030 or

2035 to reach to some level of 30K

level of scalability like number

of the qubit that you have plus the rate of error that you

have each single qubit. So do you think the

trapped ion systems will eventually power

commercial quantum computers or are they more likely to

remain like the gold standard for scientific

precision and benchmarking? Yeah, it's a

difficult question, but the paper publication that

you could already see in

terms of system benchmarking.

Like system benchmarking means that you have some specific

algorithm and you give it to me, I will run it on

ion trap machine. You give it to the second ion trap

machine, you give it to the neutral atoms machine, you give it to the

photonics machine, superconducting machine and compare the result

in terms of accuracy and the time that it takes

to get back the data.

Like iron trap, it's top now like with the

error rate and Even with the lower number of the

qubit compared to, for example, superconducting. But

in terms of the overall, like a benchmarking is still. Yeah, it's

better than superconducting.

But this question. Yeah, it's very difficult to

answer. There's like some other competitor, like a photonic base.

They're claiming for a million qubit, but it's not out there

yet. Right, but it could come, like, who knows?

Yeah, so. So are sheer number of

qubits going to matter or is it logical qubits and there's physical qubits.

Like is. Is there? I think what I really want to know is like, what

trapped ions, you know, the dealing is good for one type of problem

photonics are good for. Where does trapped ion really, like,

shine? Like, the original question that

you mentioned was about the logical and physical qubit. Well, yeah, I know.

I. Sorry, I had way too much coffee, so I dumped a couple of questions

on you. Sorry about that.

But the first question I want to ask,

let's hit the undo button on that.

Trapped ions, where. What problems are

they really perfect for? Is really the question I want to know. Like,

where. You know, if I'm looking at a whole suite of problems,

kneeling is good for one type of thing, photonics. And where does.

Where does trapped ion really excel? Because, yeah, I think the target

for trap ion is the universal quantum computation. It's not just

any gotcha. Okay. It's like, yeah, in any

algorithm that you can give it and get the answer, it's a matter

of like quantum volume, like a number of a qubit, error

rate, those kind of things. But the target is to solve

like a hard problem that the classical computer cannot. That. That's the

target. Gotcha.

Yeah. Because I can easily see like, you know, kind of. I'm old enough to

remember the, the early, like WINTEL days where like this is 100

megahertz, this is 150 megahertz, this is 166 megahertz.

Right. You know, like that, like that became like a marketing scheme.

Like, and I know that there was a hard. There was a speed boost attached

to that, but yeah, beyond a certain point, it was not really a

meaningful measure of how fast the machine is.

And it seems to me that quantum, like number of qubits and all the.

That's even more complicated. And number of qubits does not

necessarily mean number of logical qubits.

So it seems like how you know, at some

point when this becomes real, real, real and not that it's

not real. Today. But when it becomes something that you know you'll see ads for,

how are they going to be measured? Like how do you compare one quantum computer

to another? Like you know, that was, that was the question. Actually Kendence

mentioned that benchmarking has a several different

like layer, right? Benchmark different like

component level and say okay, I do one

gate, two gate and the spam detection with this much

error. That's very good. But it's not the whole

story. The whole story is that you give me a like

a sample algorithm, that it's a standard algorithm and then

you can run it on classical machine and see, okay, takes 10 to

25 years to be solved and then run it on like a quantum

computer and then see how long it takes

and how accurate the result is and then compared

with the other machine. Like this type of benchmarking is already

is ongoing. Like a lot of

quantum machine that is out there. They are

publishing based on that kind of like they're trying to

publish several algorithms. 1, 2, 3 and the paper and saying okay,

we did this on this machine and then this is the result.

And then even they compare it with some other machine back to back then

show the result how like how much

error with how much uncertainty you can give this

answer those kind of measure is a more

system level benchmarking. That's more important

at the end of story. I gotcha. All right, that makes

sense. So for students or engineers who are

fascinated by trapped ions,

what skills or areas of study would you

recommend they start exploring now?

Definitely start with physics background and AML physics,

atomic and molecular physics. That's the

core for

understanding the single ingredient

inside. But at the moment we need a lot of

other skills need to be developed also like optical

engineer. Like we need to

take the data from atoms or ions to

our detector and then we need some collection

system and imaging system for example. That's very important.

Understanding laser, how we can use the laser,

how like we can control the different spec of the light.

That's very important. And either

mechanical engineer comes with the game because you need

to make like here at oqd

every time we hire several co op students that we have

in here in Canada that the students in the bachelor level in

mechanical engineer should come to some company and learn for

something for for like a four months and they have to be

paid also they come to this machine and then they helps us

to build some optomechanical modules

that we need to make it with specific spec

to build our whole machine. That's also

important. It's not to the core of the like iron

trap, but it's very super critical

to, to, to show the criticality of this

like making mechanical stable system. Like we

have 30 ions sitting 4 micrometer

beside each other, like 120 micron. And

then this is the, like a line of the ions that we start

working and, and doing quantum computation with them.

Like the thickness of the hair, human hair, is 100 micron.

Like this, like a string is sitting at the cut of the human

hair. And then you need to control each

individual atoms with lasers.

And then the laser that you're talking with atom number one

shouldn't talk to, with atom number two. You shouldn't have crosstalk

like four micrometer away and

four micrometer away. And then you can imagine

how stable your mechanical system needs to be

to not make these two mixing each other.

And it become very important indirectly to

the main problem. But for people who are interested

as a builder or as a user, two different categories.

Builders are the people who should know about the

atomic physics, this laser, optical engineering,

mechanical electrical users.

They should know more about the software because like whenever that

you use your classical computer, you don't ask about like how the CPU

is built. The software level,

like more theoretical physics will be the user. Like

the comparison will be builder of telescope and user of the

telescope. Like there's like a two different category and we are in the builder

side of open quantum design. We are building the,

the machine. And then some people coming from

theoretical physics can come and use it afterwards.

That makes sense.

That makes sense.

What's the most surprising or beautiful thing

you've ever seen happen when working in a quantum

experiment?

Yeah, like you see some unexpected effect

and you see that kind of noise and then after some

investigation you see that, oh, that's another phenomena that it's

kind of coming to your game and showing and it,

yeah. Then go to the theory and see, oh, that's, that's kind of interesting things

to develop. Even sometimes you will be

sidetracked to that kind of problem and see some

achievement happen at that kind of noise that you saw in the system.

I've seen several of these examples on my

own research. It's kind of very super

interesting and exciting. Yeah, I also wonder too,

like, you know, how much, how much of it is you get this, like, is

that, wait, is my equipment like messed up or is my seeing a new

undocumented phenomena? Right. Like, you know. Yeah, yeah, yeah. There's probably a bit of

excitement, a little bit of skepticism and a little bit of like you know,

not sure which it is. Yeah, that's very important in

the AMO physics lab. One of the differences that I would

I should highlight here is that in. If

have you ever been in like a experimental lab, like AMO

lab? I have not, no. No, it's. It's kind

of very super messy. Everything like the

cables coming, lasers and the mechanics. And then

in principle that system works just once and then paper

published and then second time you don't know that it's working or not. You need

to spend a little time to like in principle students

spend like a 90% of the time to fix the problems

ongoing and then probably 10% doing a real

experiment on a normal like

AMLABS. One of the goal of the OQD

is that change this ratio to lower value.

Try to make the system more stable and more modular

in the way that we can monitor different sections

and also make it stable

overall working for a long time. More

work as a commercial product compared to the R and D product.

The comparison will be like a breadboard in electronics and then the

PCB version, like breadboard is the time that you do

testing and then PCB is like a solid.

And yeah, in OQD we try to go from

like a breadboard version, messy version to more solid and

PCB type version and try to change its

balance. Like spend less time on fixing problems, spend more time

on doing the experiment. Actually that's

one of the main goal here. And we could see like we have another lab

design, the OQD that is R and D based in

the grandpa of our lab. And we could see

the thing that they spend like a year to achieve. We

could do it in like two weeks.

That's very, very distinct value that

you could see. We have a lot of camera photodiode in our system, probably plus

hundred that monitor different level of the system

and report the errors. And that will

help you to maintain the system commercially.

Not. Not as a physicist, it's a more engineer.

Okay, interesting. So there's a

lot of kind of interdisciplinary collaboration that's

happening exactly in quantum development. Right. You've got your physicists,

you've got your engineers, computer scientists. So how

do you, how do you find the shared

language so that you're able to kind of bridge those

disciplines effectively? Yeah, Someone

who has like a little bit of each one of those needs

should be on top of the project. Like should lead everything.

Like a person who knows a little bit software, mechanical

optics should be on top of that too.

Whenever that you hire someone from a Specific vision

can get benefit of his experts knowledge out.

Yeah, that's very crucial to have someone who has

done a little bit on some of those kind of activities.

Okay.

Yeah. For people who probably trust that you can later

put like open Quantum design Link and also GitHub

to the people who are interested to see what kind

of things already it's on public level that everyone can

see on hardware and software. We have also some

simulation level which is very important also from aim of physics

simulation that anyone who want to run

anything can use that from our GitHub and this

GitHub will be more and more published in future

whenever that we get some spec of our prototype

and then they could have more information of each

individual module that we have here.

Interesting. Yeah, I think there's a lot, there's a lot to say.

Like you know, it seems like quantum computing

is going to need a lot of multidisciplinary and

people. So I think would that be good advice for people like if you're really

good at one thing, learn a little bit of something else. Yeah, yeah. And

actually I think from the report in the

North America I've heard some, I've seen some like a

publication that there is shortage on the workforce

for quantum development.

That's interesting. And that coming, that's coming from

different kind of principles. As I mentioned

it could be completely not relevant to

the. To the quantum word, but it's directly

relevant to building the quantum computer. Right.

So how close are we really to seeing

quantum technologies like quantum memory

or quantum Internet change our everyday

lives? Yeah,

I think we like a quantum related phenomena

is already affect our life. Like whenever that

you are using gps, you're using atomic clock.

That's definitely direct use of

the quantum word in daily application. If it

comes to the quantum memory also

my PhD was on the quantum memory based. There are already some

companies that building quantum memory and quantum

repeaters across the world testing across

like a hundred fiber, 100 kilometer fiber and.

And that will be crucial also for future

quantum like a cryptography or for quantum

computing even like some, some company already doing

that. And for quantum computing it's a little

bit further out. Like

quantum memory, quantum communication, quantum sensor is closer

and you have some product already. But quantum computing it's a little

bit back and still we're not there to say

this is directly used on our daily lives. But

some problems like finding the proper drug

for example, that's one of the things that

the quantum computer can affect.

If you have a quantum computer easily can break

your cryptography that you have for

bank account these days. That's a thread to

a daily life if it comes to the reality.

And that's already has a lot of people on it.

Yeah, yeah. That also could be something that

yeah. Will affect.

Yeah. But yeah, it's a very tough question to say

exactly what, what will be there? Who knows?

That's fair. That's fair. I always like to, I ask this question in every

single interview and I always love the answers.

How would you explain what you do

on a daily basis to a non technical person who's not in

your field, like just completely non technical. How can you explain

it and break it down?

You mean what in terms of what. In terms of like

in terms of what, what you're doing in quantum computing. In terms of what

you're, what problems you're trying to solve. Is this the

cocktail question? Kind of, yes. Things like, you know,

like if you're at a cocktail party or whatever and what do you do? Well.

How do you break it down? How do you unpack it a little bit.

For like explain. Like I didn't fully get probably the

question that explaining the, the problem that I'm working

with in or in general, like a quantum computation.

Which one? I would say in general. Yeah, you get the question

what do you do? Right. And you have to assume they're a civilian,

right? Like they're not. Yeah, yeah,

yeah. I could go like, I could say like for example, if

you go to the, to the beach and get one of those

tiny sand on the beach that

has 10 to the power of 20 atoms inside

one of those tiny sands and here

we are working with one single atoms.

Wow.

That probably can bring you

in the scale size. How

difficult is to work with this guy

in terms of the scale of your system control? For

example, if you consider Toronto to London, for

example, you want to report the distance

between these two cities by nanometer scale.

That's also in terms of scaling factor

for controlling your laser light, whatever that you do that you want to control this

single atoms, you need to have that kind of control. And we have

that kind of control that we can report the distance between

New York, London with nanometer scale.

Then you can go to your cocktail party and think about this kind of

things.

We have a lot of folks who are, you know, who are

currently, they're, they're physicists, they're really in academia

and they want to grow over the bridge into industry

where you are now. And they don't necessarily know how to navigate

that transition. Is there anything that you

could talk about in relation to that, the transition you took.

Yeah, the industry is. Yeah. As you know, it's

completely different world compared to the academia

or going towards that direction, like improving the

skill set of like a

daily activity and close some small like

task that's very crucial for, for

industrial level activity compared to the academia. The

academia is more like a long term plan working on something for

very long term. But in industry you need to deliver

something on some specific day and that

makes you like decide differently.

Probably your system that you want to close, it is not perfect, but it can

do the job that you want to do for that specific task.

That kind of, in terms of the mind level, you

need to change your mind from like very

optimized for the best system that you want to make

it to the system that it worked for that specific task.

And whenever that task finished, then the second

task can like improve that third task can improve

that and then you, you will reach to the final goal but in different

way. In industry. Yeah. You have some

commitment to deliver some result in, in a team,

in a big team, like a 40, 50 people, hundred people,

they want to accomplish some, some goal

and that's, that's very important to do your task

on some specific tool like task

at deadline.

Interesting.

Any other further questions? I know we're getting close to the top of the hour

and. Well, I've asked my favorite cocktail question. I know, I

know. That is my favorite one. I love doing that.

But it does force somebody to kind of explain. Right. Like, and I

think that's also going to be an important skill going forward for anyone in

academia. Like you can't assume that people even know. Like

you said 10 to the 20th power of. You can't

assume that anyone would know. What that even means. Right. Like,

you know, but no, I

mean that's important. Right. Because you know, at some point if you do want to

make that bridge, you do, you're going to have to stand in front of an

investor in some of the, in front of a customer. You kind of have to

explain like what this is. Right. And you know,

that's, that's. I think that's a challenge. Every technologist, regardless

whether it's quantum physics, whether it's AI, whether it's, you know. Yeah.

You know, has to come across. Right. The, the person, the hippo, which I don't

know if you've heard that acronym before. Yeah. Have I used that acronym in front

of you, Candace? Yes, I've heard it. It's one of, it's one of Your favorite

ones. Also NIMBY is your other one that you. Yes,

yes. So hippo. Not my bad. Backyard. I've learned that one.

Tell them the hippo. You know, have you heard the Hippo? The hippo acronym?

Yeah. Highest paid person's opinion. Yeah, I, I first

heard it at Microsoft and somebody was like, well, you know, doesn't really

not going to name who that hippo was, but that was, that was pretty interesting.

You know, it was like hippo, like. And my, my mind went immediately

to hungry, hungry Hippos. And then it was like, no, no, no. This means like

highest paid persons. But you mean the highest paid person in any

given enterprise is probably not going to have a

background in quantum physics, right? Yeah, I think that's a safe

assumption. Could be wrong. But again, you know,

but yes, you have to. The people cutting the checks or, you know, you have

to, you have to convince them that you're a startup, the value of

it before they write that check. Um, and I

think that this is something that, you know, this is

what separates kind of like the famous entrepreneurs in

tech from the ones who are not. Right. Like, you know, Steve Jobs,

you know, was he the most

proficient software engineer or computer builder? Probably not.

That was probably Woz. Steve Wozniak. But what he could

do is sell vision, sell the end result. Yeah, I think that's really,

that's where the magic is, right? Yeah, this is the same like in

academia world you have some Prof. Which

is very, very knowledgeable, but they're not good at teaching

and vice versa. This is the same thing in industry,

like presenting idea and compared

to the knowing knowledge deep of that kind of area.

The same thing we have in academic also, like. Yeah.

Well, I think that's, I think that's a good, a good point to, to

leave us off on. I think that we've asked some really good questions

today. I've loved everything we've been talking about. We really haven't

had someone who's been able to focus on trapped ion for us before.

So that is very exciting for us. Yeah,

that really is. Because like, I know that's one of the core. That's one of

the oldest, like, so like it's, it's something that like it's can't be

ignored and we've kind of ignored it up till now. So.

Yeah, great. That, I mean, that was a good

opportunity to talk to you. And

also I heard about the other podcast during the last

couple of days. That was also interesting too. Thank you. Yep.

Thank you. Thank you. Thanks for joining us. On impact Quantum

Today's deep dive into. Trapped ion hardware showed just how precise. And

powerful this technology can be. If you enjoyed this episode,

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