Welcome to Impact Quantum, the podcast for the quantum curious

and the entangled enthusiast alike. Today,

we're diving deep into the fascinating world where theoretical physics

meets real world engineering with none other than Maruan

Salhi, physicist and CEO of Qubit Engineering.

Forget Skrodinger's cat. We're talking about the kind of quantum that

optimizes wind farms and power grids, not

feline survival probabilities. Maruwan shares

how his team is tackling massive engineering challenges using

quantum inspired approaches, all without needing a

working quantum computer yet. From turbine

layouts to toggling thousands of grid switches like it's a game of

high stakes Tetris, this episode is proof that

sometimes the most boring problems are where the real

innovation happens. So if you've ever wondered how quantum

computing is quietly reshaping our infrastructure, this

one's for you. Let's jump in.

Hello and welcome back to Impact Quantum, a podcast for the

quantum curious. And with me today is the most quantum

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

Candace? It's good, it's good. Thank you so much. I'm so happy to be back

and happy to talk to our guest today. That's good to see you back

in the studio. And we have a really interesting guest today,

Marwan Salhi, who is a

physicist and he's also the CEO and co founder of

Qubit Engineering. And I love the tagline that

they have. It says harnessing the power of the quantum realm.

Getting a very distinct ant man and the wasp

kind of vibe from that. And judging by

the look on your face, I'm not the first person to say that

in the virtual green room. We talked about some of your work and

you've lived in Maryland for a time, so. So

tell us about yourself. Welcome to the show. Yeah, thank you. Thanks for

the invite, Frank. Happy to join you. Candice, here.

So, yes. So I am a physicist. I'm computational

physicist, slash theoretical. I did my interest in

quantum physics, actually in quantum computing to be precise, started

early on, but you know, we only saw

the availability of quantum computing machines, quantum

machines, only in the last few years

we to be kind of precise, that's when you can actually have

more. You have an actual access to play with the machine and visit.

So a quantum physicist would focus on quantum

optimization. I am also the

CEO and co founder of Qubit Engineering, an optimization

startup for

quantum formulating to quantum to formulate problems in

engineering in. In a way that we can run it on

actual quantum computers. I co founded the

Cubit Engineering with my colleague

George is also a physicist. He's a professor at University of

Tennessee in quantum information science. And also

our third co founder is Hatton, he's

an engineer. We, we kind of got

him into working with us and helping us in building our software

for, for quantum applications. Very cool.

Very cool. I, I just have a, a lot of questions because there was.

What types of engineering problems have you or are the most popular?

I've just wondered about that. That's a good, that's a good question.

So I would say it's not, it's not about how

popular, it's about finding the right use case.

A lot of effort in the community is to identify which

problem can benefit from quantum computing, from quantum

algorithms, from quantum optimization that we can see advantage over

classical methods, over classical approaches. So

and that's, that's also how we, how we looked at it. We looked at

the. So in fact, in fact as I said, we're, we're

not engineers to start with, but we are physicists working in the

engineering now industry. And the first thing

that we started thinking of, okay, what kind of problems can we

solve? What kind of problems can we see real

impact or quick impact or the

low hanging fruit kind of we can capture using

quantum optimization approaches. And the

answer comes from a mathematical, it's purely

mathematical. So we needed to understand the type of problems

that, that have interest in the engineering industry, have an

impact, but also something that the quantum

optimization can, can contribute to.

And the, the, the answer for us is, is

not not only for us, but the answer for, is basically any problem,

any engineering problem that we can represent

as a network of nodes and edges. For

people who are familiar with the quantum annealer machine D wave,

try to think of this, the

topology of the D wave machine, it's

built of qubits and connections. So you need to find a

problem that cannot be mapped into that. And you'll

be surprised in our engineering world how many

problems are. And one of the problems,

the first problem that you started working with is the design of

wind farms. Wind farm layer optimization. Yes.

And maybe you don't see it that way, but let me, let me kind of

hint into that. Turbines in a wind farm, if you, if you look

at the wind farm, so

actual turbines, you can think of them as nodes.

And then the wake interaction between any two

turbines, you can think of it as the edge connecting them.

And that changes based on the relative position of these

turbines depends on

the distances, depends on their altitude. So

it's actually physically, if you look at it from a physics

perspective, it's a perfect network

of what we call a fully connected system that

matches, you know, this, the type of problems we're looking for. And that

was a choice. That's how we selected the first use case.

Interesting. I, I would not have thought that.

I mean it makes sense now that you say it, like turbulence and things like

that. Um, because these windmills are, are massive. Like I,

I mean I've never been more, less

than maybe a mile or

two from them. And they're just massive like you just.

And, and I would imagine, I mean they're like airplane wings basically, right? I mean.

Yeah. So I mean the, the, the hub altitude, the

altitude of the hub, the center of the turbine where I feel ways

of rotating. I mean it can go

up to 120, 140

meters in the big ones. So the

actual diameter of turbines, the big ones, I think they can

go to, yeah,

116. I think that's the biggest you've seen.

So they can be really huge. Again, the way

we look at it doesn't matter the, the, the size.

From a study perspective, there are

a point or in a network, of course we

associate with that particular, and obviously not a point but a variable in

our system. But that particular variable is associated

with a power generation. It's associated

with altitude, exact position,

it's associated with wake effect. It's

causing. And it's also submitted to a kind of

feeling the wake of other. Generated by other turbines around.

Interesting.

How specifically do quantum computers help with that? In ways

that, you know, a classical computer wouldn't like. What

is, is it just a good old fashioned optimization you're trying to

find? Is that what it is? So, so,

so let me, let me, let me step back a little bit. What we are

solving, we're solving challenging optimization

problems which are as I said, are built

in the form of the network of nodes and edges.

And what this does to the problem, it

creates almost an infinite

search space of possibilities you have.

So the best example we can give a simple, a good

example would be if you have a room with

100 seats and you have 50 guests and you're trying to

distribute these guests, there is almost

an infinite number of possibilities. The exact number would be 10 to the

31. Oh, wow. Okay. If you, if you

want. And, and we did actually this calculation with our, with the, with

a collaborator from the supercomputer at operational lab. And we

said if you want to do a brute force and consider all

possibilities, how much time would we need

using your supercomputer. That time was Titan, which is,

I think at that point was maybe the first or the second

fastest supercomputer in the world. This is, this is just a few years ago.

And the answer was around 31 years.

I think with the new machine available at ORL now

Frontier, it's probably maybe 30 years

or something. Wow. But it's, it's

so, so that's, that's how rich these, this type of problems

now. The, the. And that's why

quantum computing can make, can make a, can make a huge

impact in the future because it can navigate

this space not through

trials of looking at every single possibility,

but just by literally zooming in through that space and

finding the optimum configuration.

Interesting. I mean, how long. What's the, what's the time on a quantum

computer to compute? So, so, so on a

quantum computer, this, this, this problem is like microseconds.

It's very physical. But okay, this, this

problem of 5,000 from A.

And, and this is a very, it's not, from an engineering perspective, very

interesting. It's simple. It's also kind of boring. You know,

it's not like the, you know, we're gonna change the world, we're gonna do this,

we're gonna break encryption, we're gonna cure cancer, map all the

protein folds and whatnot. Right? Like, it's pretty, to be

blunt, basic. But you know what, boring

is where the money is, right? Like, there's a lot of these financial

gurus. The more boring something is, the less competition is going to

be. I don't want to go down that rabbit hole, but it

sounds like boring tends to pay the bills. Right?

That's a good point. In fact. In fact, you know,

in general, industry only care about what

kind of advantage you can provide them. It doesn't matter whether you're using a quantum

computer or simple Excel sheet. This is, this is the reality.

But of course, we will reach a point where sophisticated

or, you know, basic tools are not the solution, and

even some sophisticated classical methods cannot even

cut through. And that's why you need to start thinking about

new innovative approaches and what we really do. And the way I look

at what I'm doing over the last few years is bridging the gap

between some interesting tools that are mainly used

in research, that usually engineers are not trained to

use them and bring them back to the engineering and say,

hey, using these tools, we can get this serious advantage.

In fact, you know, we've been doing this. As

I said, our first use case was in the wind farm. The first, the first,

the first. You know, the first. When we

started working on this and we did not start on our own, we started

collaborating with actual wind engineers, with actual

wind farm developers. The companies, different companies

from almost everywhere. The first thing I say, you guys, you're not

wind engineers. What are you doing here? What, what, what's the, you know, what's the

purpose? And say, hey, we have some, some cool tools for

optimization and we want to test them with you guys and want to see how

this. And then after a couple of weeks, you know,

once we exchange the data and show them the results, the, the.

They are basically now they want to know more. How did, how did you do

this and why are you getting so. And then actually

even they get surprised with the results we can capture. They say, hey, we want

to do this test again like we

think. I mean it almost seems like a little bit.

You got lucky on this one. Let's, let's try again. Let's change the problem. Let's

increase the size a little bit. But it's not,

it's not magic or Sonia. It's not. It's basically a new way

of solving a problem that they've been using the same method for the last

three, four decades now. I'll give a simple

example. When it comes to the wind farm layout optimization,

the way, the way it's done, basically there is a

program, software, you commercial ones,

sometimes some developers develop their own internal system

and they, the way it starts, they. They

basically pick the lands, they have all the data required for the

project and then they start from a random design,

just random one. And the way they do it, they basically starts

moving one turbine at a time from one location to the

other while watching how the

energy of this change and going up and down. And of course this

goes through an iterative process, you know, as long as possible

until they see that there is no more progress. Then

they stop the calculation. This numerical search and they say

we got. This is the, this is the best we can do.

We don't do that. We. The way we do it is basically

by selecting the position or

selecting configuration from

thousands of possibilities. Just like the selection

of where to place your guests in the room. 50 guests in 100 room.

We generate thousands of potential sites for the turbines

and then we select the exact number that we want. The

advantage here is that you're selecting one

coherent configuration rather than

moving one turbine

which will impact, maybe it will improve the

production of one. Basically ease up a little bit on the wake for One

turbine, but maybe it will increase the week on another one. And which is an

iterative process. So this is, this is a very, it's a very

different approach. It's a combinatorial optimizer, which is

what quantum computers are meant to. And

you know, I will start talking about quantum and maybe I should, I should hint

to this. We've been doing. We, we started using

quantum annealing machine machine. We built our, our main

system using one maneuvering machine or four quantum

machine. And and, and slowly we,

we, we, we shifted a little bit to using

slowly we shifted to using simulators or what

we call quantum inspired solvers.

I believe this,

this name quantum inspired solvers or quantum inspired

optimization was introduced to us by

Microsoft Azure Quantum. They were pushing for

it and today it's,

it's the way to go for to support the development

of new quantum applications. So the challenge for

quantum engineers or quantum application engineers is that they are,

they've been trying to map

some large engineering, complex engineering problem

into a quantum machine that is very

limited. The number of qubits, number of connectivity in the,

you know, that's submit to, that's subject to noise and errors and so on.

And that actually

impacted a little bit. So that shifted the focus from

developing the application. We're trying to match your application

with the current hardware. If we fast forward in the future

and we'll have the best quantum computer,

then we will never worry. As a quantum

application engineer, you will not worry about the machine. You'll just

worry and focus on developing your problem, on developing your application.

So today the engineer is divided

between not only trying to rethink this

problem to map it into a quantum machine, but also worrying about

the capacity of the machine that he'd be running his problem.

So this was kind of clear to us and the

opportunity of shifting

towards these quantum inspired solvers.

What it does first, it actually

let you focus on the application rather than on

the limitation of resources, rather than on limitation of the number of qubits

and of connectivity. You can ask me and

say, oh well, you know, you can build the problem

however you want and you build your application. But

yes, it's not going to run the same way if it's running on a

quantum computer versus running on a classical CPU and GPU

machine. That's true, but we don't have that machine

yet. And another

very interesting point is that what we realized

by rethinking the problem, we're actually

saving a lot of this search space. We're simplifying a little bit this

huge search space which is allowing us to

achieve and get better solution than classical

approaches. When you are selecting

a full configuration of a wind farm,

you have more chance to get better solution than

iterating on moving one turbine at a time

based on whatever resolution you have based on doesn't matter

the number of iteration you do, you will be stuck in

local minimum. Definitely you'll be struggling there.

So yes, this

quantum formulated wind farm layout optimization problem is not

running on the actual quantum machine, but it's running on

a solver that's

behaving or trying to behave like a

machine. And we still get significant advantage.

So this is, this is something we've been advocating

for and we think this is the lowest hanging

fruit. And it is clear

today that there is a big shift or towards

or there is a serious consideration to quantum spike

optimization. And

this is, this is in fact if we want to

say the priorities today are as follow of what, what

can you possibly do? The best thing you can do is to develop

applications for quantum inspired solvers. One the

next step, which we're not there yet,

a lot of companies are open. This is running a problem on

a hybrid system, classical, you

know, basically a system made

up classical computer and quantum computer.

The next level would be running it fully on the quantum

computer, I believe even running on, on

a hybrid system, the classical slash.

We are still struggling there because we were

not really sure how to decompose the problem

between the CPU and the qpu. How can we

divide our optimization problem between. It's interesting

you say that because what is the,

a lot of people will kind of scoff at simulated

quantum machines. What's your

thought on that sort of debate? Or is it kind of just

one of those silly debates that people like to get into?

So, so I'm here, I'm talking,

I'm focusing on simulated quantum

optimization, simulated solvers for,

for, for,

for quadratic and constrained binary optimization or quadratic constraint

binary optimization or even if we consider polynomial

problems, not just quadratic. Now the,

I would, I would say if you, if we're talking about

general simulating a quantum physics system, that's a

different story. Simulating a molecule, there is nothing

better than actual qubits to simulate molecules.

And that particular

discussion, it's very, it's, it's,

it's clear that the quantum system is multiple.

It's better. The challenge there again is how big of a molecule

can you simulate today? Right.

If you want to do that on, on a classical computer. I mean

people have been doing this for Decades now, you know, people are

studying molecular dynamics and

just trying to simulate the quantum physics of

molecules and atoms. They've been doing a lot

of good job and a lot of

applications and a lot of similarities have been built for that.

And their main challenge is that every time they need

more and more bigger machines

because it doesn't scale up, you know, the same way as a

quantum system when it comes to, so, so

that's what, that's what mostly uses simulating on a

quantum computer for, for when it comes to material size.

I think quantum systems will be, will

be, would be the best. And some

sophisticated high performance

computing kind of modules have shown very,

very good results and they made a lot of good progress

there, but they're still very expensive computation. In

fact, the impact, one of the most

expected impacts of quantum computing

and the industry is on the pharmacology,

designing new drugs, designing new molecules. Right.

Biochemistry and all that.

What we are working on, on terms of simulation is, is

purely mathematical. In terms, we are mapping

engineering problems, formulating them mathematically in a

way, mathematically in a way that we can

solve them on these solvers and these quantum spirits.

So, so these are two different kind of

fields. So when you're trying to simulate the quantum physics

system, you better simulate that on an

actual quantum computer. But that definitely, it's more natural. But

we are taking an engineering problem which, like wind farm design

and then now trying to optimize it and simulate it.

In fact, what we do, we do simulate the interaction. We take

the whole problem, the whole dynamics of it arm and map

it into this network of qubits.

And we basically, you know,

literally match every

turbine with an actual qubit and the different interaction it has

with the other qubits, everything. So we kind of simulate

data. But as I said, the challenge is the machine, the size of

the machine, the number of qubits, the number of connectivity you can have and so

on. Right.

Does this make sense? Yeah, go ahead, take us a little bit away from

this for a moment to speak to some of the, you know, the interests and

concerns of audience members. So I'm going to ask you. So for

someone looking to get involved in the quantum computing field, whether

as a student, a developer or an investor,

what's the most unexpected piece of

advice you would offer? I mean, your experience is quite

extensive and the way you talk about everything, I mean, clearly you've

got, you've got the skills involved. So what skills do you

believe will be the most valuable in this rapidly

evolving landscape?

That's a Very, very good question. And

I have to say this. You know, let

me, let me, let me just point something. There are lot of people are working

on different things when it comes to quantum computing from

the hardware to the software to the error corrections

and everyone is contributing to, from, from its own

position. The,

There are, there are two ways to be part of this

game, this, part of this

development, the technology development of quantum. Whether

you're looking at contributing to it

at a earlier stage or in

the long run. I believe the investors who are

involved in developing and investing in,

in quantum hardware, they have the long term vision

where they say hey, we want to be, we want to contribute to building this,

this, this fabulous machine. This, it's sophisticated machines

but it takes time and they look at it that way,

they understand it and even you know,

as we move forward we will. Right now

the space is divided in with superconducting

photonics. I don't know, you know,

cat cubits.

Exactly, all kind of, all kind of qubits.

At some point we will see some kind of

preference and say oh actually

the winner is whatever it is

from starting from superconducting to iron traps to

neutral atoms to whatever you want to call it, photonics.

Right. So, but it's,

it's part of you know, investing and, and so on. It's part of the,

the vision and the risks that people do. I think we're all learning

from, we're learning what is happening from the iron trap side. We're

looking from the superconducting, from the photonic. So

it's nothing is wasted, everything is useful and we're learning from.

Now from. If you look at it from the perspective on an

engineer who's trying to be involved, this

depends. They want to be part of the hardware. It's different

than if they want to be part of the software. I

believe there are efforts that will be limited in time.

I mean at some point, let's say Microsoft,

you know, the majorana, the new topologically protected

qubit will be an actual reality

and we'll have much better

qubits than a lot of the work that we've done so

far. And some of these,

noise reduction, error correction, all that. Maybe

we don't need that anymore. So, so, so I think

I, as I said it depends. So you need to choose what,

where, what you want to play now. You want to be part of the,

the, you want to contribute now early. This is what we need today.

That's what we're trying to understand or you want to be part of the future

in terms long term. And I don't think there is an

answer for one answer for all of them,

whether for investors, whether for engineers,

whether for entrepreneurs. It depends how you look at it. Let me

share what the way we looked at it, we

looked at problems that are

coming from the engineering. In fact I do. I still

remember my first presentation at the IEEE Quantum Week

when I said we're, we're looking at an energy problem using

one. The first and natural reaction

was like we're, we're still talking about atoms and molecules

and you're talking about energy. I mean today

we're working on power grid. We moved

from turbines. So, so if we progressed

like I don't know what they would say to me. They say hey, I'm trying

to solve power grid management optimization today.

But the, the, the idea is that you want to look at it differently. It's,

what we are doing is

we are mapping the mathematical

dynamics, physics dynamics into a theoretical model.

It doesn't have to be molecule, doesn't have to be atoms. It's,

it's a pure optimization. What

it does it give us access to some

solutions. Let's call it configurations. Let me give another

example that we've been working on for the last three years

now. I started with the wind as an example. We're still

working a little bit on the way but the focus right now is on power

grid management for many reasons. But

the good, the Sorry, I lost it.

You know, going from

there is this idea of finding a problem

that has a specific mathematical structure

where you can access a solution that you cannot

extra sophisticated is feasible through this new way of solving

problems, this discrete combinatorial optimization. Let's call it

quantum combinatorial optimization. The idea

why, in fact why, why we want to solve combinatorial

problems using quantum computer. So

quantum computer of qubits. They, they actually

embed this idea of having all possibilities at

the same time through superposition. It's almost like

packaging all possibilities in fewer variables,

fewer smaller systems and they can navigate through very

fast. And that's why they offer an

opportunity to solve this problem that in

a. From a classical engineer. When I mean classical, I mean

using classical methods. This who's trying to

avoid what they call the combinatorial explosion.

It's literally they say hey, number of possibilities is exponential. I can't

deal with this. Yes, of course. That's why you need to change your

approach. Now going back to where I started, I said

the way we look at it, we saw that there is an opportunity in Engineering

where we can map some of these exponentially growing.

In fact the correct term of using it called anti hard problems.

We can navigate this

space slightly better and faster to get

results or solutions we cannot get before. And in fact

we learned from it. In fact, that's why right now,

for example, we're solving, we're solving large

scale problems in this discrete space which

wasn't very clear, wasn't very

intuitive to many scientists or engineers. The beginning when you said,

when we suggested this, this, this approach.

Let me, let me connect another example.

So I mentioned the grid. You can think that the

grid is, is, is a very large infrastructure. It's very important, it's

critical. The way, to be specific.

Yes, yes, the electric power is good. The electric power grid, the

way we look at it through our algorithms,

if I may to simplify this way, it's a bunch of

switches that you have maybe thousands, tens of thousands and

you're trying to find which one to keep on, which one to turn on.

It's again another combinatorial optimization.

And for that you

need, you cannot do it the classical. You cannot do brute force. You cannot do

classical where you try one at a time. You need to have a little bit

more sophisticated approach. And quantum. As I

said, people always expected that the quantum computing

contribution is only coming from the hardware.

What we learned over the years. No, it's also

coming from the way that the new way of

thinking the problem, we look at it differently. We're

solving now a network of

nodes with branches, different vices, different

coupling. I see what you mean.

So quantum inspired algorithms also play into this.

In fact, in fact, I believe

now the GOE Office

of Science, at least they showed in the last presentation I attended

that they are prioritizing now quantum inspired optimization,

then hybrid quantum computing, then quantum computing,

then the actual algorithms. So this, this is the first time I saw

it. In fact I took a picture of it. I was so excited to see

that they kind of got the message in a way.

And, and, and as I said,

you need to, you need people to invest in the quantum hardware. You need people

to work on developing the machines and which is a long term

kind of things. But you need also to be ready by

getting a community of quantum engineers developing new

applications. And the main motivation is

it's not, we're not just building the application

for, to be used when the quantum computers we know we

can use it. And they are actually offering us an advantage today.

Right. And even more in the future when the quantum

hardware will be right. So

this, this Is this is. So this

is honestly stepping back a little bit out of the hype

that people talk with a quantum and say

to some extent, okay, in the next year or two or three, we're getting a

quantum hardware. No, we don't know that. But we

can actually do something useful. We can

rethink our problems, we can rethink our algorithms and have

an impact today. And also we will saving time

by bridging and connecting some engineering problems like the one

we are doing at Qubit, engineering energy problems that people

never thought that we can actually cast them

and project them into

a formula like an anon one that we. So if you ask me

what we do or core expertise is in the quantum

formulation of the problem, this combinatorial optimization, which

involves a significant domain expertise, you need

to understand really well the application. You need also to understand

how to build your quantum formulation of your problem.

You know, so of course there's a debate. Say you're

running it on classical systems, why you want to call it.

I would say yes, it's running today on a classical systems. It's generating

better results than, you know, the

classical approaches that been developed for the last two, three

decades. But also it can run on a quantum

computer, if you give me one now. And you will not be

able to match the results that I would get out of it.

Interesting.

Very interesting.

Okay, so let me ask you this. Looking further out, let's say

10 to 15 years from now, what's one moonshot

application of quantum computing that

you personally find the most exciting or transformative?

Even if it seems speculative today,

what fundamental breakthroughs would be required to make that a

reality?

So that's a, that's of course very good and very

hard questions, but I'll, I'll try. Yes,

I believe our

next challenge is to

understand how we can

connect classical computers with

quantum computers. How can we divide? So this is a more

general kind of concept in the sense that I think hybrid quantum

computing will be our next challenge for the next 10

to 15 years. And in fact we see it

as a continuation of developing

applications and running quantum applications on a

simulant. So the simulator right now is

limited to CPU soon, once we

have a better idea how to incorporate

part of the calculation on the quantum

while running it on this, on the, on the classical system. That's going to be

the next. That's the next. That, that will have a very

serious impact. In fact, in the future

it's not going to be purely

quantum. Even in theory age, when we have a very good Computer. I

believe this idea of running

classical and quantum computer at the same time is,

is the winning course. We're not going to

be able, we need to even rethink the

problem now even more in the sense that

where and how to connect classical and quantum computer when

it comes to our optimizations now

for the applications and the use cases,

this is what,

in 10, 15 years, I think whatever applications

we are doing today, whatever use cases we are developing

today, will continue and will get even better.

And that's why we need to start now.

Actually, not to interrupt you, but like, I think

I want to click on starting now, like the importance of starting now because I

think there's a lot of people and you're a trained physicist, right?

And you even said like, you know, you're not primarily an engineer.

So what could people who are not physicists do, like

software engineers, AI engineers?

Because I think that's really. One of the

people asked me about this a lot, like what do I think about what they

should do about quantum computing? I was like, well one, if you're in the C

suite or the corner office, you should really start thinking about

being ready for post quantum encryption, right? That's kind of the

first thing, right? I used to be an emt, right. And the first thing is

you remove the body from the burning vehicle

when you start treating it. Right. But I think the second thing is

in terms of career projections, I tell people, just get

used to it, right? Just get used to the content

concepts, right? Get ready. Because a lot of

what traditional computer science people, myself

included, we kind of have to unlearn what we've learned in a

very real way. It's not that I have to throw

out everything, but I kind of have to stop and think

a little differently. Am I, am I on target with that? Am I off

base? What do you think? I, I would say we are,

we're going or we're moving forward by being a little bit more

interdisciplinary and to some

extent complementary, right. I, I

think every different kind of engineer,

they build certain way of reasoning and they are used to

some kind of input, some kind of output and, and

a process in the middle. Right now

you're, you're talking about a different dynamics,

a different, slightly different engineering, quantum engineering.

So you need to be comfortable a little bit

understanding the dynamics of. And

I wouldn't say throw, absolutely not, but

it's more adding on top of it. But

you could be a computer science and you, you have, you have a

background and you have a good, clear understanding of

how to write programs or softwares in

classical way. Now you need to learn some new skills when it comes

to. And again, I don't think

moving forward. What, what,

what the way I see that the workforce will be, we

will need to be able to build teams that

are complementary. We're not expecting one

person to know everything or to totally go

from computer science to quantum computing,

but we want him to be able to work with quantum

physicists, to connect the dots and to

basically, you know,

have that flexibility and that of communicating with, with

other colleagues, doing that and, and using their language and,

and so on and even building something together with them.

That's, that's the idea. So we are moving

slowly towards an interdisciplinary kind of team set

where the engineering is getting. And of course it

depends on the application, it depends on what you're building, but

it's getting more and more interwind and

you need collective efforts. You know,

even, even computer science or in software engineering, you have people say

hey, I'm a front end developer. Hey I'm a back end developer. I'm,

you know, I'm full stack. Right, here we go.

So, so, so I think, I think this way. So I don't, I don't see

a problem. I don't see it as a, as a challenge. Oh, you need to

shift, you need to unlearn. Absolutely not. No, you need to continue

and build on top of it. And I don't think even there is this

concept of unlearning anything. I think we only can keep learning something.

Right. Maybe the better way to phrase it is drop assumptions.

Yeah. You know, so,

so, so let me, let me bring. So I know this is, this is me,

this is two website but let me, let me put this.

I think there are. As we are, we're moving

forward. The quantum industry

is getting better at identifying its main challenge.

It's getting better at understanding what use cases

we can build, what kind of skill you need in fact

for what we do, optimization. And you know, have

to be careful right now even because when we

say quantum optimization, I mean as I said, the debate whether

you're running on a quantum computer. Yes, we did run on the quantum. And the

leader by the way. Yes, we, we. I love, as a physicist, I

loved running on actual quantum machine because you're

you. Especially when you have access to the different knobs and, and you

see how the output is changing and how you.

It's very exciting. But now

what I'm trying to, if there is a. The message I want to say is

that we're not.

We need to have combination of the skills

when it comes to the application, the domain expertise and you need to have that

quantum. So having it in one person, sometimes it's hard.

But working in a group, in a team, that's where you can build

something. In fact, that's for, for our team, that was the

reason we started working the energy space because it's physics and we understand the

physics and then we have the background in the quantum computing, then we can solve

the problem. There is a, there is. I mean let's, let's

say, let's, let's point out the elephant. I mean quantum optimization now the

financial market, all the portfolio optimization effort that

a lot of companies are trying to solve this problem. And one of

the main challenges is that you all, you need one guy who is

really experts and the actual problem in finance and

understanding how the market goes and what parameters

really have influence versus others and

you need to have someone who can formulate that problem and

so on. So combining these two

expertise is I think the way for a successful

development of the solution.

Of course, what, what happens here is that either you have people

who've been doing this the classical way, they're trying now to understand the

quantum computing and trying to implement what they learned there, or

the other way you have quantum computing

experts who trying to understand more the finance and so on. At

the end of the day, what you will end up doing, you'll end up doing

working with teams

made up or of different skills and they need to be able to

communicate and collaborate

and to. To solve the problem.

Interesting.

So what would be your. I'm sorry Candice, go ahead. No, I've hogged the mic

the whole time. I genuinely. No, I genuinely did have something to say. I just

absorbing. Please continue. Go ahead. What's your

advice for people today that are in school,

whether they're in physics, whether they're in engineering, whether they're in compi,

marketing, etc. Like what, what would be

your advice to someone who wants to

get into

get ready for the quantum shift.

You know like you call it, it's a quantum shift.

It's. It's moving fast, it's

changing depending on the,

you know, the interest and so on. I

think the, the idea of, I

think what the most valuable in this evolving

time because we're talking about things that are changing

every day, whether it's algorithm, whether it's hardware, whether

it's technology and so on. I would say the,

the best thing I would do is to

work join any team

that can offer the opportunity of looking at

quantum technology from different perspectives,

whether from the algorithm side, whether from the, the

hardware side. Doesn't mean you need to work on both,

but you have that, that possibility of interacting.

I think that would be the best when it comes to building the

actual quantum machines in the future. So the ability to see you're not

just focused on the hardware itself, but also on the interface,

connecting the hardware and communicating. Right.

On the application side,

I mean, there are, the application is. All

applications are moving towards quantum computing or

quantum. Of this new way of doing hybrid quantum computing in the

future and having a better understanding

of how we are building these quantum algorithms will be

a huge plus. It will be, it will be as important as

learning your, you know, analysis and

algebra to solve some of your

engineering problems. That's, that's, that's how we are going. That's what we are

moving forward. So it will be a tool. You need to

understand it, get comfortable with it. And of

course you need to understand the application that you're developing. And

so it's so, so having that, that, that

one answer, I don't think it's an easy, it's, it's possible.

But for any fresh

engineer, I would say, for any young engineer, I would say

try to understand your, your application as much as possible and try to

think of quantum algorithms, quantum optimization, quantum computing

as an important tool that you need now to

master. And you will use it. Because

think about it. In the future, all of these quantum computing

companies, when their machine are ready,

they will say, okay, here we go. Other machines, go ahead

and use them. You can do so much. You need to be ready by then.

You have the software, you have the application. You understand how can you can

run your application, your problem on the left machine,

you know, so

it's, it's very, it's very dynamic.

Interesting.

We're almost at time and any

other recommendations you would give or. Candace, do you have a question?

You know, he gave advice to our, to our listeners on what they should

think about considering and how they need to get involved. That's always

usually the basis of my questions that I like to ask.

I asked him for his thoughts on the future as well. So

I'm going to say right now I feel like we've gotten a lot of

great advice and information, so I'm going to say no.

Do you have anything that you'd like to ask right now? I, I mean,

I, I asked all the questions. I mean, we could probably go on for another

couple hours, but you Know, but, but I think it's interesting to get,

you know, you've been in, you know, if you looking at your resume on LinkedIn

and whatnot, like you've been doing quantum or quantum networking for quite some time.

So it's good to get your perspective which I think is probably been the most,

one, some of the, one of the most grounded conversations we have. Like you know,

this is, you know, don't get, you know,

it's very grounded, right, because like, you know, it's the boring stuff. Windmill arrangement, right.

Very critical. Right. These windmill farms are massive. They're

not insignificant amounts of money are being put on the line. But it helps you

can get the most out of it. And I think that's really,

you know, it's the optimization problems, right. It's not

that are going to really, I think make the most waves for

business and you know, those are not going to be

glamorous, cure cancer, figure out protein folding,

photosynthesis and all that like sort of thing and optimize that. But I mean

those, those types of problems I think are going to be crucial

towards solving a lot of these intractable problems.

Correct the learning part of it absolutely. What we are really.

Yes, the problem may sound boring when you think of

new drug and discovery, but the, the basis

and the learning is actually helping us slowly getting into

a way better, you know, much better understanding of

how things work and what we need to do better

and so on. And just like we did, we, we started working

on wind for the last, now over the last couple of years we've been

working on grid transferring that knowledge, the idea

of the ability to solve these complex problems

and so on. And I think, and

I think this is, this is the. Like you said, maybe, maybe

you know, it's first these are problems we need to solve that difficult

today, especially with the grid. So what happens and the blackout happens in,

in Spain recently and in Greece and southern France

and, and we're started. And maybe I should say one, one

thing about this. You know, one of the biggest machines we've

built as humans is the electric power grid infrastructure.

It's huge, it's complex and we are reaching a point and

we kept growing it. Every year, we kept growing

it and we reached the point today that

we cannot manage it using even our supercomputers.

This is a serious problem to them. We built a machine

that we are barely maintaining using

classical. And we need to rethink our tools. We are, we need to

rethink the way we manage it and we solve it

and right, here we go. These

techniques, this. This different way of looking at problems, the way

we're navigating the. The space of possibilities. Like I said, it's

a bunch of switches. You need to know which one. You're not going to just

turn on and off randomly. Absolutely not. Right. So you need to be

a little bit more sophisticated. And that's what this new way

of thinking of quantum optimization and the way you were dealing it and

solving it will be the answer for that.

Interesting. That's awesome.

So we want to be respectful of your time and thanks for coming

on the show. And where can folks find out more about you and your company?

Yeah, so

cubatengineering.com that's our website. Please

reach out. You can find me on LinkedIn too. We're

happy to answer any questions, collaborate,

connect. And yeah,

excellent. Fantastic. And we'll let our AI

finish the show. And there you have it. Quantum optimization,

wind turbines, power grids, and a healthy dose of

reality From Maruan Salhi. We've journeyed from

theoretical physics to practical engineering without so much as

collapsing a single wave function. If

today's conversation has shown us anything, it's that

quantum isn't just about the future, it's about rethinking the present.

Whether you're a physicist, an engineer, or someone who

just enjoys saying quantum at dinner parties, there's a place

for you in this evolving landscape. Be sure to visit

Quite Engineering. Come to learn more about the work

Maruan and his team are doing. And as always, if you

enjoyed the show, subscribe, leave a review or

shout superposition into the void. We'll hear it. Until

next time, stay curious, stay coherent,

and remember, in the quantum world, even boring can be

revolutionary.