Welcome to Impact Quantum, the podcast where quantum computing

isn't just theoretical, it's practical. Or at least

we hope it will be before your washing machine becomes self aware.

I'm your ever curious semisentient hostess

Bailey, here to guide you through the squiggly universe of

qubits and quantum algorithms without requiring a

PhD in astrophysics or a working flux capacitor.

Today we're joined by Notti Erez, director of

quantum applications at Classic Technologies, a company that's taking

quantum software from lab coats to laptops,

from abstraction layers to real world enterprise. Impact

Knotty helps us bridge the gap between quantum hype and actual

progress. It's not about doing everything

faster, it's about doing the right things better. So

buckle in or entangle yourself, because things are about to get wonderfully

weird, delightfully nerdy, and impactfully quantum.

Hello, and welcome back to Impact Quantum, the podcast where

we explore the emerging field of quantum computers, where you don't need

to be a physicist, you just need to be a little bit of curious about

quantum. And the most quantum curious person I know is as with me, as

always, Candace Kahooli. How's it going, Candace? It's going great. Thank

you so much. I appreciate the great introduction. Hey, anytime,

anytime. I'm excited to have our guest because I know that

we had a bit of a scheduling snafus and all that, but

who are we speaking with today, Candace? We're speaking with

Nati Erez. He is the director of Quantum

Applications and he is coming

to us all the way from Israel today. So it's

very exciting to speak to someone on the other side of the world.

Absolutely. And it is Classique Technologies,

which in the virtual green room, Candace got right on the first try.

I will talk it up to her living in Montreal.

I'll take it. I'll take it. Well, welcome to the show,

Nadi. And what exactly does Classiq

Technologies do? Thank you, Frank. Very nice to meet

you. So ClassIQ Technologies is the leading

quantum software company. And what we try to do,

you can think about it as extending the quantum

stack in order to provide quantum software at scale.

There's the quantum hardware layers, the quantum controllers,

the error correction, and the quantum gate level

software. And then we try to extend it into

a higher level of abstraction so it will

be able to exactly like the audience of this podcast,

lure people in with other backgrounds that are not

only physicists, our

software language. Try to focus on the

application and algorithm that you're trying to solve, rather

than the specific gate level design of the quantum

circuit. And once we do that,

when you're creating a functional description of what you're trying to do,

there's room for optimization as part of the

compiler. So you take

this high level description and compile it to any

hardware that you'd like while choosing the optimal gate level

implementation. Interesting. So

the gist of it. Interesting. So it's proper software

engineering, I suppose? Yeah,

definitely. With some hardware specific knowledge,

but yeah, definitely. I like the fact that you're working on

abstraction layer because one of the things that I think a lot of people may

not realize is that there's different types of quantum hardware underlying.

Right. You know, and that's something that

I'm really curious, like has

that been a barrier towards innovation or development of

quantum applications thus far, or is it,

it's going to be like, is it already a problem or will it be a

problem soon? So I think that one thing

that really helps, I counted

seven different types of quantum computers

with a bit over 50 companies worldwide. Some of

them are big, like IBM and

Microsoft. Some of them are big startups like Quantinium and

DynQ. And we always see new

small startups forming out, mostly from universities.

So the variety is huge and everyone is a little bit different,

even those that occupy the same quantum modality.

But one thing that's common for all of them is the fact

that we use universal gate sets in

order to program the code. So you can use

even a gate level that's universal.

I think that's okay. Can I have you

explain a little bit more the whole gate set idea? Can you,

can you explain that a little bit more? I'm not familiar, so

I would be interested in that. So

part of my background is in classical

assembly language. And when you think about

how regular classical computers

work, there are eventually

built from a single logical gate called

the NAND gate, the not end gate. Every

other logical gate can be built using variations of

this gate. And when we go to quantum,

it's a little bit more complicated. We can't have a single gate that

describes the entire possibilities of traveling over

the possibilities of different states. And

for that, for that we developed

a language that encompasses the different

logical gates and

those gates. Each computer has

its own different set of universal basis gates.

And then we can take a single program and transpile it between those

different computers. So this is

possible without classic. It's been possible

for around 10, 15 years, I

think. And it allows you to

explore the different modalities. The problem becomes

once you try to optimize the circuit for this

specific modality, your circuit

creating creation techniques should be different.

If your computer is, for example, fully connected

or has a limited connectivity map where you can only activate gates

between neighboring qubits,

or sometimes if a controlled

not gate is the basis gate compared to a

controlled phase gate, the design choices can differ.

As a programmer, you usually only care about one

parameter, and that's the total fidelity of the algorithm. You want

to have the least amount of errors as possible,

and those parameters differ from different hardware.

So while you can use any quantum circuit on any

quantum hardware, you'd like to optimize it for this

specific hardware knowledge. I see. So

you know it. So every computer. And I think

this, this probably ties in nicely with your history in classical assembly

language, right? Like where there's a common instruction set, and then

underneath the instruction set that goes into. So you write in

Python, you write in C Sharp, you write in C, whatever. And then

ultimately my background is software

engineering. So, like, for me, I knew what you're talking about, but not everyone

will. And I often forget that

we cater to everybody here, but basically it's an abstraction layer below

every computer that you have with your phone or whatever. And then it

ultimately gets down to what we call the assembly level. But below the assembly

level is where you're dealing with logic gates. And one of the interesting

things about quantum computing is the introduction of new types of

logic gates that are possible only with quantum computing right now.

And are those like a standard set? Like, I

know Hadamard, there's Poly X, Y and Z,

and there's probably a few other ones I know I'm leaving out. Are those. Are

there a. Are we still

discovering new gates or like the fundamentals have been kind of

laid down and whatever, or where do we stand

with that? So I guess the answer is a bit of both.

Okay. When it's more of

a hardware oriented question. So I'm not truly an expert,

but sometimes this new hardware

get released to production from different companies.

We can see that they're using new types of gates, that

they manage to optimize their specific errors.

So I always get

acquainted with new types of logical quantum gates,

but generally everything can be described using the gates that we already

know. Okay,

does that answer the question? Candace, I hate to put you on the spot because.

No, no, no, no, it does. It gave me a little more clarification. Than I

needed, 100%. So have you. I'm

sorry, go ahead, Candace. No, no, no, go ahead. Have you found that your

background in such. It sounds like this is kind of dealing with low level

problems. Right. Like from, from a tech stack. And, and I have a

couple questions about that, but the first is obviously it seems like

having experience in classical assembly gave you an

advantage in, in, in transitioning to this. Is that

a true statement or a false statement? Definitely. From my

perspective, being acquainted with low level languages allowed

me to debug quantum code better and to

find, find errors and ways to improve them

by directly looking at the quantum circuits.

Ever since my first days at Classic, I started

by, I started at the R and D section working on the

synthesis engine, the compiler and

I, I remember that every time that I synthesized a

quantum circuit I, I looked

deeply at it and I tried to figure out if I could

design it better and if I could, I changed the

synthesis engine to improve and this is a skill I got from

looking at assembly code. Interesting.

By the way, I will say something that's hopefully

cheerful. I'm not the

typical quantum oriented employee

because I only have a bachelor in physics.

Okay. Yeah. I mean, do you think that. So

part of the people that are in our audience are people that

want to break into the quantum field. Right. So

you know, you have a, I think you were making a joke about,

you know, you don't have an advanced PhD in physics and things like

that, but obviously that probably helps.

But one of the things that I think is interesting is how do you get

non physicist into this field? Right. And one

of our couple of our guests actually independently has said there's enough

PhDs already right. In this space.

So that really was an inspiration for us to restart the

show and kick off this season was the idea that you're going to need an

ecosystem, you're going to need sales reps, you're going to need customer service reps, you're

going to need, you know,

Microsoft calls them CSAs. Other company called them CSAs. We're customer support

engineers. Right. To go face to face with the customers and

help them out and get the installs going.

We're going to need marketers. You're going to need people that understand

how to sell this technology. You're going to need people that can pitch this account

execs to pitch it to the executive level and the C suite and

things like that. Um, for you, obviously it seemed like it

was a pretty natural jump from you're, you're working with traditional

classical assembly language now you're doing kind of quantum, not

quite quantum assembly language, but really low level programming.

And did you feel that having that degree in

physics was helpful didn't help or just

helped a lot? Yeah, it's a very good question, Frank.

It helped me mostly due to the

understanding of what quantum is. Usually when you

first hear of quantum mechanics, your first

thought is it's not possible, it doesn't make any sense

and you need to digest it and sleep on it for a couple of days

until you're saying, okay, they, they proved it, it's

real. Now let's see what we can do with it. And

I got lucky, by the way, because one of my hobbies is

getting people into Quantum. And my job at

Classiq is exactly that, is working with

enterprises that some of them are already

quantum experts, but some of them want to get into

the quantum field and don't know how. And

we help them by training their, their new

Quantum team, which is usually scrapped off different

people from the organization, some with machine learning

background, some with computer science background,

some mathematicians, some physicists, and try to

merge them all together to get into this new field, which all of

those areas of expertise can really help getting into

this field, everything from its own angle.

And the training is not that

hard really. If

I had to choose, one tip is practice.

The Internet is full of materials, full of tutorials,

and until you're not practicing

and trying to code it yourself, it won't make any

sense. But once you do, it's working,

it's there. Right? So what would you say

are the biggest challenges that you have to prepare for?

Is it scaling software? Is it the continued

education of customers? Is it hardware limitations?

You know, what are your biggest challenges?

Right? So I think it's,

it's basically a combination of everything you said.

We're taking part of scaling the software

and we're, we're getting really, really

interesting results. I think the major

challenge right now in quantum computing is scaling the hardware.

But luckily there are a lot of really, really

smart people that are focused entirely on that.

And I'm no prophet, but when you

start looking at the roadmaps that all of these,

both startups and corporates are publishing,

you can see a convergence point in the next

two to five years about the

beginning of the Quantum Advantage era. And it's really

interesting because what I expect will

happen is that

if you count the, all

of the enterprises in the world that got into

Quantum already, I think you'll be in the

lower hundreds era. But

there are thousands and tens of thousands and more

enterprises in the world. So why everyone

don't go into Quantum right now? That's because they don't see

them. Then they don't quite see the need to get into it right now.

But all of the. But the convergence of all of these

roadmaps tells me that in the next couple of years,

we'll see a very large wave of industries

that are trying to understand what is Quantum and what

can Quantum do to their business once it's in default

orienter. And I think this is going to be a

major milestone for the industry and

we need to prepare for that, both with the manpower and

with the educational capabilities.

Interesting. How do you think that could be done?

Right. I know that's kind of a small question with a

big answer, but how do you think that could be done?

How do you think that could be? Like, what. What does the quantum industry need?

Right. It's a big. That's a

tough one. Yeah, yeah, yeah. I

genuinely believe this is the toughest part of my,

of my job. It's. It's not talking about why

Quantum is useful. It's not about talking about quantum algorithms

and how to use the algorithms to solve applications.

That's the easier part. The harder part is to

find those applications that can really bring value

to the industry. It combines the need

for a single person to

have Quantum toolbox to understand all of the algorithms that

they can use and then understand all of the

applications in that area of expertise. Let's say we're talking

about a hospital. So

we need to understand all of the tough problems that the hospital

can handle with or don't handle with because

they're too hard or handles them approximately, and

would very much like to get a better approximation and

then not only connect the dots between these applications

and the algorithmic toolbox, but understand

the actual, I call it business value it

can bring to the hospital, because this is usually what

talks to them, what talks to the enterprises, what

makes them get into Quantum. That's the point where they

say, yes, this is what I need. Even though I can't use it

right now, I want to be able to use it when it's available.

I mean, that makes a lot of sense. That makes a lot of sense

because I think that's where the gap is now, personally, just like

as a. I wouldn't call myself an outsider,

but I mean, kind of someone who was really excited

about this space. I think that. How do you explain this to the C Suite?

How do you explain the value of, you know,

what it actually provides? Right. And I think that that is.

I can see that being a challenge. And how do you get people that are

comfortable talking about very in depth Physics concepts,

but also in a way that can go very high level for people who are

not background in physics, the people who write the checks. Right.

But also do it in a way. That.

And also speak to kind of the more lower level technology

concerns. I can imagine that would be

a pretty severe problem actually. Or challenge, I guess, depending

on how you want to phrase that. Definitely

challenge. Nothing in quantum computing is a problem. Right,

Right, right.

In your, in your opinion, what's one

popular misconception about quantum computing you wish

stop promulgating?

Well,

I think that it would be the,

the speeding up factor.

If you ask the average person what

quantum computer can bring, they'll say that it can solve

problems really fast. But if you ask someone who understand

quantums a bit, they'll tell you that it solves specific

problems really fast. I think that's not the

main point of quantum computing. It's not solving problem

faster, it's solving problem

usually with more accuracy.

Because let's talk for example about

combinatorial optimization problems. The really

hard problems in combinatorial optimizations are

usually NP hard. So we don't solve

them precisely. We use approximated algorithms to get

some solution. Probably not the best, but also not

the worst in a reasonable time.

So if quantum computing can solve combinatorial

optimizations faster, even up to

some error that can get into the answer,

the point is not that it solves it faster, it's that in the same time

we can find a more accurate solution. And this is

what can really translate into the business value eventually.

Interesting. And just for those that NP hard versus

P hard. Just. Can you explain that

in terms of. Yeah, sure. More people will like, I, I know where

you're going, but not everyone. No, no, you're right. It's very important.

So in classical computing, in complexity theory, we

usually divide the problems into several complexity groups.

And by complexity group I mean how hard

is it to solve the problem? How much time will it take based on

the size of the problem of the input? So

one of the simplest types of problems is

called P, where we can solve the problem in polynomial

time based on the input. A

very hard problem is exp, which is solving the

problems in exponential time. And a very interesting

type of problem is NP problems.

NP problems are problems that

it's very hard to solve. They are very hard to

solve. But once we are given a specific solution,

we can verify that it is the solution in polynomial time.

So you can. The first example of why we need

it hard problems is when

you think about entering a password

for identification you want.

And we currently in classical complexity

theory don't have any idea if

the class of NP problems

equals the class of P problems or not.

So we don't know if there is a polynomial reduction between every problem

in NP to a problem in P, which allows us to

solve these problems in polynomial time. This is one of the major

questions in computer science today. When you're talking

about quantum. It's a bit more complicated.

There's also quantum complexity theory with other

types of problems. But.

But the general belief, I

think, is that some of the problems can be solved

a little bit more efficiently in quantum computing.

No, it's a good way to put it. And then this is the whole thing

of, you know, it's hard to factor primes, right? It would probably be an

example, right. And then this is the thing, right? And there's a

whole thing about. This has been an. On,

like you said, like an unresolved question in computer science in general is like,

is, Does P = NP? Right? Like, kind of like that sort of thing.

And, and I think that's right. I think a lot of people think quantum computing

will do everything, but it won't do everything. It's really good at certain types

of problems. Protein folding, probably a good example.

Chemical interactions, that sort of thing. Factoring primes, like I already

said, you know, so it's not gonna, you know,

it's not gonna do everything. But what it does do, it does really

fast. And I think that's. I think that's the important

thing that people don't realize. Like, I talked to a lot of different

people, you know, on the show and about quantum computing and why I'm excited about

it. And it's like, so it'll do everything faster. So I'll get a Q phone

and, you know, the. Or I can get a QPU and play, you know, Grand

Theft Auto 6. You know, it'll be that much better. I was like, not

that. But I do also wonder, right, since we're. We're

still early in on this, right, Are there going to be other

types of problems that could be potentially sped up that we don't really

realize yet? I think that that's,

I think that's the exciting part. Like, in a lot of ways, we just don't

know what we don't know about implementing quantum

computing at any kind of scale.

I completely agree. That's super exciting. And I think there's even

two types of interpreting this

phrase. One of them is that we have today

a set of quantum algorithms that we know of Grover's algorithm

and Shor's algorithm and phase estimation and QSVT

and a lot of other very interesting algorithms.

And we can take them and try to understand which

applications we can solve with them. So even with what

we know today, we don't necessarily

fully understand how to use it. It's

very similar to the early stages of classical computing where

no one would have imagined something like the Internet

or Google or don't even think of

ChatGPT. Yeah. Or like YouTube or, you know,

Netflix. Right. Like, who would have thought when they were,

you know, probably during or right after World War II, like putting

together these vacuum tubes, like, oh no, you'll be able to watch TV on this

one day. You don't even need to go that far.

I have a computer in my washing machine. Right, right. Everyone

has a computer on your washing machine. It's the same semiconductor

machine that runs the processing unit. That's true. Eventually,

that's true. Your washing machine probably has the equivalent or more power than the

Apollo guidance computer. Right.

Who would have thought about that? Even as recently as the 60s.

So I think that we just don't know what we're opening

up. I think that's really exciting. I think we

really are kind of in that transistor phase

here. So one of the things you said was interesting and

I really want to know. You said debugging quantum

algorithms. Mm. It's

my understanding that if, if once a

qubit gets measured or analyzed,

like the state will collapse. Like how do you debug a

quantum program? That seems very, I mean, is it, do you have to debug in

simulation and then run on a real machine?

That's a perfect question.

If you get creative enough, there might be

ways of debugging what we call dynamic debugging,

translating to debugging by running the code.

But it's indeed very hard and very

non trivial due to collapsing the states. What I

talked about is what I call statically debugging.

When you're looking at the code and trying to find errors or

trying to understand the behavior of the, of the program.

So it's something that's very hard on classical computing

and probably extremely hard on quantum

computing. Unless you're, unless you're

understanding the, the basics, like how

are the gates operate on the qubits,

what are they doing to them? What's the connection between different

qubits? Right. So one of the

things we developed as part of our platform in classic

is a visualization engine that shows you the

quantum circuits with the lower gates,

lower level gates that act on the qubit, but also

incorporates the information from the high level

language. So you're not only seeing qubits, you're looking at

variables and you're looking at functions that act on those

variables. And you can zoom in to

look at specific gates from specific functions, or you can zoom

out to scope something a bit larger and understand

the functional behavior of the code.

So combining both the low level

and high level information together really helps you to statically

debug to find the errors in your original code

only by looking at the circuit.

Interesting. So how can regular

people spot the difference between quantum

hype and genuine progress?

I never thought about it.

That's a very good question. Thank you, Candice.

So. First,

the first sentence that I say to every,

even potential customer that we meet is

just making sure, you know, you can't get any value from quantum today.

I don't know what you know already or what you think

you know, but we're not yet at the quantum advantage

landmark. So once you start from

there, you're already, you're already in a good spot.

Like, you know, you won't get any advantage. Let's understand

that we're in the exploratory phase. And now let's start to

explore

after you're there. There's a variety of

quantum algorithms that you can explore. And it's also important

to distinguish the ones that are proven, the

advantages proven, like phase estimation and amplitude estimation and

Grover and Shore and others, and also understand the limitations

compared to programs that are more

heuristic. Like, we have a reason to

believe that they can be more efficient. We didn't prove

it yet. So they may be more efficient in some senses to

some specific problems, and may not,

like qaoa, which is not,

which is not a guess. It's quantum algorithm that's that has something

physical behind it. It's called the

adiabatic theorem. It talks about slowly

transitioning between a quantum system that

we know it's optimal solution to a quantum system that we

don't know its optimal solution. And

theoretically, if we do it slowly enough, we'll get from this

optimal solution to our desired optimal solution.

Interesting. Practically, we're not doing it infinitely

slow, so we need to do it in the right way.

But it's a quantum algorithm that's not actually proven

to provide advantage, but it has some smart

heuristics behind it, or

variational circuits. Not all

variational circuits are equal, either in

match to the problem, all in match to the hardware.

And sometimes you'd like to use this one and sometimes you'd like to use that

one. And it's important to understand the parameters of the

problems when you're starting to, to solve it

using quantum.

I guess these are the main points. Okay,

that's interesting. That's interesting.

So I saw on your LinkedIn Speaking of quantum careers,

you have you posted that you are hiring

and it looked like there were some pretty interesting

roles that you were hiring for. So if you want to use this as

an opportunity to do some recruiting,

feel free. Thank you. No problem. No, because I

think, you know, just, I just glanced at the post and it wasn't, you know,

I didn't look too closely, but I didn't see any hard requirements for somebody

who has a PhD in physics. Right. Like it seemed like you, you do need

to have people. Like one of them was something like a customer

success engineer. And there were a

couple other ones. So tell me about like, right. Who are

you, who are you looking for these days? Right.

So even more generally, Classic finished its round C of

funding around a month or a couple of

months ago. We raised $110 million

in this round and now we're growing,

thank you very much. And now we're growing and we're growing on

basically every group in Classic. So

my group is the technical group that, that supports

customers and partners and business engagements.

And when we are growing, we are

looking for people who are very

much oriented into quantum algorithms.

We can teach them the Classic product and how to use it

in the best possible way, but they will need to do the same for

all of our customers. And they can be customers that

are very quantum proficient, like

BMW where we

had a mutual research that was really top notch.

It wasn't myself. So I feel, so I feel okay to say

it, but the team both in BMW and our personnel were,

did a fantastic research. Very interesting.

And it can be with a bank

or a hospital or or an automotive company that

wants to get into Quantum but don't know how. And

we need to help them walk in that path and train

their quantum, their newly quantum team and make

some projects together to make them successful.

So this is the core idea. I'm looking for people who

understand quantum information and quantum algorithms and

may have some experience with quantum machine learning

or with quantum chemistry or generally

in a variety of quantum algorithms. This is

basically what I mean. We're also

hiring for product roles and for

education leading roles and

definitely for R and D roles and

people who know classical software programming very well,

but also quantum oriented. They can speak the

language of quantum software.

And also something very interesting is that we're starting

to hire a lot of, of business

representatives around the world. We're

creating hubs in different locations. Just

go to Classic I.O. and look at the, all of the jobs that

we, we currently have and apply.

Very cool, Very cool. And I, I think that's, I think that's

encouraging that you not only are growing and congrats once again, but

also that you're not just looking for, whenever I mention quantum computing,

a lot of even, even smart technical people, I'm Talking

data scientists, AI engineers, DevOps folks,

they just kind of like tune out. Like, oh, that's not for me. Like, I

can't get my head around that. And yes, you're right. There are a lot of

things about quantum physics that you can't.

It's hard to get your head around. Right. Because what we, what we think of

as reality and kind of we live in a. Note, we live in a

Newtonian world. Right. Or at least the world we perceive is

pretty much Newtonian physics. Right. Like I, you know,

but turns out that reality. One of the best

quotes I ever heard was that what we perceive as

real maybe being made up of things that may or may not be real. I

thought that was like, that really blew my mind. I know that's

a little fluff science, pop science type stuff, but I mean, there's a lot of,

there's a little bit of truth in that. Right. And you know, and I think

that this is hard and to quote Richard Feynman, if you think

you understand quantum physics, you don't understand quantum physics.

I also think that it's encouraging that folks to

explore a career in this, particularly as it's, you know,

it's about to take off. Hype or no hype. Like, I think, I think there's

something very real here that's going to happen, you know, in a very

near future. Right. And clearly your

company's raising money, so clearly. You know, I, it's not just

me saying it. There's venture capitalists, there's actual money on this. And

Candice was telling me that the G7 summit was,

had a big focus on quantum computing.

Yeah, no, it's, it's very exciting. I mean, and there's a lot of,

there's been a lot of noise that's been coming out of Israel

for what they're doing in the quantum sector, which I think

is super exciting. So let me ask you, let me ask you,

you know, Nazi, if you could fast forward Five years.

What do you hope people would be doing with quantum

that they can't do today? Right.

So quantum computing is a

revolution and the revolution really takes time to, to kick start.

I very hope that in five years will

already see quantum advantage, at least for some of

the problems that we, that we face with today.

Meaning better hardware, better error correction algorithms, better

software. There are a lot of different companies involved

in this effort, a lot of different research institutions

that are striving towards that point.

And with that I hope that we'll see a couple

of movements in the industry.

First of all, definitely all of the

Fortune 500 companies are already deep.

They have quantum teams, quantum groups maybe

that are not only under the CTO and

innovation departments, but they are actually starting to generate

revenue. With that,

I hope to see a lot of new

startups in the professional service

area to help all of the new,

the smaller enterprises that don't yet

get the benefits of using quantum

computing compared to the, to the

maybe hopefully not so high cost of starting.

The other thing is that as

a worldwide society, we need to

find a solution to the

manpower who joins quantum computing.

We need to. When I started at

Classic three and a half years ago, I already at that point

thought that it's about time to start teaching quantum computing

in high school. You can do it

to some degree of understanding without,

without knowing linear algebra, for example, start

learning about quantum algorithms and learning

Grover's algorithm is definitely possible.

Possible to understand and grasp and implement and

execute and see the results on actual quantum computers

even back then. So in five years

I hope that we'll already be there because those high

school students will eventually develop a career in

quantum computing in the next 10 years

where it will be a lot more relevant. And we can't wait

for, for everyone to have a PhD

in physics. Obviously.

That'S there.

I like, I like that. That's a good answer because we do need, this

is revolution is coming, we need to get ready for it, right?

And when my son, my oldest,

decided to take AP Physics over, well, first I found out that

he was opting out of AP Computer Science or something like that. And then

he, I was like, why did you do that? He goes, because I want to

take AP Physics. I was like, I can't argue with that.

So yeah, want to

be respectful of your time. Do you have any questions? Candice? I totally hog

the mic. Sorry, I just was curious. I don't know if this is easy or

hard to answer, but do you think Quantum

will be more like the Cloud or AI? Like a tool

that is used behind the scenes or something that users will

interact with directly. Exactly. Someday. Right.

So the answer is, is divided into

phases. In the first phase, it will definitely be on the cloud.

I think that some organizations, though a very

small portion, has true motivation

of having quantum computer, a

personal quantum computer. But I think

that we won't be there at least for

five years is the very optimistic guess

I can think of. And regarding

a lot more, let's say 10, 15 years, I already said that

I'm not a prophet. So it's very hard to

predict because we probably can't even imagine

the applications that will be able to solve with a quantum computer.

So. Yeah, no, that's totally fair. Right. We can't

even talking about the physical challenges

of cooling everything and maintaining everything and

putting everything with high punctual and

accuracy, putting that on a

wrist. I think it's an

incredible hardware challenge.

Okay, that's a good point. And you're right, like, who would have thought?

I mean, I stream all my media over

the Internet. Right. People who created the Internet probably didn't imagine that.

And I doubt that they imagine that. Right. But I also think that the

people that were working on vacuum tubes in the original kind of,

you know, computers did not think of that. Right. Certainly

Charles Babich and Ada Lovelace did not imagine that. Right.

So, like, we're going to find ways to use this new

technology in ways just really outside of our imagination

right now. Definitely. Awesome.

And where can folks find out more about you?

You can find me about. You can find more about me on my LinkedIn

page or on Classic IO.

You can contact me directly. The email address is very easy. It's

not at Classic IO and I'll be

very happy to answer any questions. Awesome.

Thank you so much for coming today and speaking with us. I really enjoyed

this. I think this will be exciting for our audience for sure. And

if you're looking for a career in. Yeah, no problem. If you're looking for a

career in Quantum, definitely reach out to Nadi. That's right.

No, I know how hard it is to recruit people, especially in this field. Right.

It's got to be. Gotta be really tough. Yeah,

it's gonna be a challenge, but we're up for it. Awesome. But I really like

that he mentioned. Oh, I'm sorry. I really like the fact that he mentioned

understanding a lot of, a lot of languages in. In as.

As a base, you know, even to say, you know, you could start out

knowing all. You could start out knowing, you know, you know, basic languages,

and then you could actually move into quantum because you just understand

more. I just like the fact that it seemed like it was open

to many more people than I thought. Yeah,

that's cool. That's cool. That's the core message of our show.

So I love it. And with that, I'll let RAI finish the

show. And there you have it. Another entangled episode of

Impact Quantum neatly collapsed into your podcast

feed. Huge thanks to Notti Arez of Classic

Technologies for proving that you don't need to be a quantum

physicist to appreciate quantum computing. But it

probably helps when debugging Quantum assembly at 2am

if today's episode sparked your curiosity, confused your

classical brain, or made you consider a career in quantum, even

if just to sound clever at parties, be sure to like,

share and follow us on all your preferred platforms.

We're available wherever fine quantum content is

algorithmically served. Until next time. Remember,

in the quantum realm, everything's possible, just not always

observable. Impact Quantum where possibility meets

probability and somehow still ends up on Spotify.