In today's episode, we dive deep into the rapidly evolving world of quantum computing with Tal David, co-founder and CEO of Quantum Art. From the earliest days of quantum theory to the edge of commercialization, Tal David shares insights on technological breakthroughs, scaling challenges, and the journey from academia and government policy to startup innovation.

We explore what it takes to build the world's best quantum computers, the shrinking resource requirements for breaking cryptography, and why now is such an exciting—and pivotal—moment in the quantum industry.

Whether you’re a quantum curious newcomer or a seasoned expert, today’s conversation unpacks the real-world impact, technical nuances, and bold ambitions shaping the future of computation.

Links

• Tal's LinkedIn Profile - https://www.linkedin.com/in/tal-david-5340b423a/
• The 50 most promising Israeli startups for 2026 https://www.calcalistech.com/ctechnews/article/p9n9w144o
• Watch on YouTube - https://youtu.be/oOyhUIYKaeI

Time Stamps

00:00 Career journey and startup motivation

06:32 Origins and development of quantum computing

08:14 Quantum computing advancements

13:18 Scaling challenges in quantum computing

15:41 Hybrid quantum-classical computing systems

19:08 Challenges in developing novel tech

24:48 Stabilizing qubits in a vacuum

27:23 Connecting ions with laser plucking

31:33 Transition from vinyl to digital technology

35:51 Military experience fosters responsibility

38:18 Israel's emphasis on academia and industry

41:21 Future of the quantum industry

44:07 Launching new cloud service

48:56 Candace and Frank's Quantum Podcast

Went from, is this really possible? To

now we're at the point of not only is it possible, but it's going to

need less resources and qubits than we thought. And

I mean, what a time to be in this industry.

Yeah, I'm not sure it's that surprising. You know,

my partner, co founder and chief science officer Professor

Roya from the Weizmann Institute of Science, always says now he,

when he was young, he had a Commodore 64.

Welcome to Impact Quantum.

Hello, and welcome back to Impact Quantum, the podcast. We explore the emerging industry

and field that is quantum computing. We don't need to have a PhD,

even if it helps. You just need to be curious. And with me is the

most quantum curious person I know, Candace Gooley. How's it going,

Candace? It's great. Today is like the first

truly warm day we have had in April.

And it makes me believe that this is not going to be a fall

spring in Montreal. We're going to actually finally get some warm weather.

So I'm very excited today. That's cool. I had to drag my tomato plants

inside because we had a frost warning here. So there you go. And

they're, they're so delicate. In the very beginning, everyone's, everyone's doing their, their

early planting inside the house right now, getting all the little, all the little

buds ready. So I'm very excited about today.

We're going to be speaking with Tal David. He is the, he is the co

founder and CEO at Quantum Art.

Oh, very cool. Hi, Tal, how are you doing today? Hi,

guys. I'm good. Thank you for having me.

Awesome. So what does Quantum Art do?

Quantum Art is a startup company building the world's best

quantum computers based on trapped ion qubits and with

unique and proprietary engineering. Map

on how to scale up quantum computers. From the small systems that we have

today, which are very impressive, but not yet doing

commercially interesting stuff, to large enough systems that

still retain the base performance, but now would be able to

tackle the world's largest compute

heavy tasks. Oh, very cool.

So it's not, it's not a. It's not an art

art company. Sorry, sorry.

Yeah, it's true art and engineering. Fair enough. I'll go with that.

So you've moved between academia, government, and

now you're involved in startup leadership. So what has

changed in how you think about impact

across all those worlds? Well, you know, there

are different types of impact, different places in your life. When I did my

PhD, the thing that interests me most was

tuning the knobs and taking graphs and measurements and stuff like that.

And then when I went to the industry, I wanted to do

stuff that actually get used by people around the world

in products and create value. And then when I moved to

government, I wanted to move policies and processes

and arrange for large scale programs to

be advanced. And

then at some point you understand that doing things from within government

has its own, own specific dynamics and maybe we

could do things even faster and better than that. And then I decided

to go outside of government again and form a startup as

a very, very deep technological project,

but something that can also go and do impact and create value in the world.

And it's like having a baby all over again. Right? It's building it from

scratch and

trying to understand what it wants from you when it cries. And then

getting it all grown up and hopefully going to graduate

school someday.

I mean, that's a good way to put it. Right. Because there's certain advantages to,

I, you know, I live in the D.C. baltimore area. Right. So

there's certain advantages to being part of the government ecosystem and there's certain

disadvantages of it. Right. And there's certain advantages of the entrepreneurial

world and there's a lot of disadvantages too. Right. So it's, and I think

governments around the world are, are because technology

is such a foundational

aspect of a modern economy. Whether those are gpus, quantum

computers, or you know, pick your, your tech

it. I think everybody's trying to figure out how do you get, how

do you combine the two? Like how do you get the best of an entrepreneurial

with the kind of the, the aspect of government.

Yeah. So first of all, I'm not alone, right. I have

amazing co founders and I have an outstanding team and

I'm just here moving papers around the desk. But I

think that taking the

experiences from different places along the, along the path

allows you to form a more complete holistic picture and

understand what are the rights, what are ways to

move or to motivate processes, depending on

what kind of processes there are and who you're talking with.

Okay. In Quantum, what we're doing is hard, hard

tech. There's a lot of work to do in the lab, which is

quite deep and, and difficult to understand for a lot of people.

But again, when you want to raise money to do, to

enable the company to grow, when you want to get

to the relevant funding, which at this point is still very much tied

governmental funding and stuff like that, it helps a lot

to understand how the language of going in those

corridors needs to be what motivates the people

there, how the processes behave and what's the

dynamics in order to be able to get to

your goals and in a focused, meaningful way. But it's not only

about one person. You need to assemble the best team on the planet if you

want to win in this game for sure.

Was there a moment when quantum stopped feeling like research and

started feeling like something that must be built?

Well, in general in the industry, I would say that

the idea of quantum computing was coined by Professor

Richard Feynman in the early 80s when he said, well,

if nature is quantum, let's, let's build something that simulates it

quantum mechanically. That makes the most sense, right? And then

quantum computer was a nice esoteric idea for a couple of

decades until in the mid-90s, Professor David Shore

from University at Yale came up with

an algorithm that is now known as Shor's algorithm

to factorize large numbers into their prime factors, which is

at the heart of decrypting

most of the public key distribution methods that

we have today. And that was the first time

that an actual, practical, meaningful,

commercially viable and also scary application

came about that said, hey, there's something to do with this crazy

science thing that might drive it into applicability.

And from there I think that from the mid-90s until let's say the 2000

and tens, there was technological advancements to show

whether this thing could be built. Because we're working with

quantum systems, they're difficult to do. A basic building block in a quantum computer is

the quantum bit or qubit. And Shor's algorithm

envisioned a computer that would need like a billion

quantum bits in order to run that kind of algorithm, in order to break

RSA 20, 48 or, or equivalent. So

we spend a lot of time trying to understand

if this thing could be built. In the last decade or so, I think the

question moves from can this thing be built at

all? To when will be able to see

that computer at scale. And there are two opposing trends

going to the same goal in this sense. One

trend is that the technology is maturing enough

and starting to scale up enough in order to start doing

meaningful, interesting demonstrations of applications

in 2025. We have a bunch of those. For example, from JP Morgan Chase

working with Quantinum's trapped ion system, and from

Google Quantum AI with their superconducting qubit system

that shows initial signals of usefulness, not just

tailor made demonstrations of the potential of quantum computers. And

on the other hand, we have an opposing trend which is really hot. In the

last few months of working on the algorithm aspects

of quantum computing and trying to narrow down the

requirements of what is actually needed from the computer in order to realize

these algorithms. And so, for example, in Shor's algorithm,

we've gone down in the last couple of years from a billion qubits to a

million qubits to 100,000 cubits. And just last week, there was

a paper by a very serious group that

showed that in some instances, they could go and solve

and break RSA 2048 with only about

10,000 physical cubits, which is amazing, the rate

of advancement. And so when you bring all of these two

trends together and you pour in a lot of investment,

both from governmental programs and from the private and public

investors in the market, it, it builds up momentum that

shows that we are right, right before a big break of

applicability of this

technology in the next two, three, four years. And the forecast

is that it will impact huge, huge markets in the trillions of

dollars in value. I

mean, that's absolutely insane because it went from is this

really possible?

To now we're at the point of not only is it possible,

but it's going to need less resources and qubits than

we thought. What a time to be in this

industry. Yeah, I'm not sure it's that surprising.

My partner, co founder and chief science officer and Professor

Ruiz from the Weizmann Institute of Science, always says that

when he was young, he had a Commodore 64.

Commodore 64 had 64 kilobits

in the computing power. And in a matter of, I know, 10,

20 years, with the invention of the integrated circuits and

transistors and stuff like that, the amount of computational

power of classical compute went up by four orders of magnitude.

Okay, so humanity knows how to do these big jumps. I think

that the difference here is that because we

are more advanced in technology and in modern times,

the pace of progress is accelerating even faster

than what we've had 50 or 70 years ago. And

secondly, because of the properties of

quantum physics, the computational power scaling

is at some point exponential. So the benefit that you

get from each jump in the capabilities of the technology

is really, really tremendous. So I'm

not, I'm not sure if it's that surprising that we're moving ahead that,

that fast. If you talk to people inside the business, they will tell you

about the difficulties and how complicated it is to get to,

to commercial viable grade scale

and so on. And this is what we are here to solve. But I think

that this is truly an exciting time. And if

you look at the next until the end of this decade for sure. This will

be the ChatGPT moment, so to speak of Quantum or some people call

it Q day or all sorts of silly names like that.

Y2Q. This is Y. This, this is, this is a

truly exciting time to be at. Interesting.

So because you mentioned Commodore 64,

my focus thing is not working, working. But this is the Commodore 64

reference manual. I, I was looking around for it and I realized I put it

back up on the shelf. But no, it's. And you

know that, I know that sounds like a bit of a segue, but you know,

when I read this, I got, I read this book, this is a book on

registers and things like that. Honestly, when I read this when I was like 11,

it made, most of it made no sense to me. But I was curious and

that curiosity led me to figure out what was

what some of those things meant like words like parameters and you

know, all those things didn't really mean anything to me at the time.

And I think there's a good parallel there for

this. Right. That curiosity tends will take you through the

difficult points. Yeah, definitely. Maybe one

more point to make about that. Yeah, that the challenge in

quantum computing is how to scale up the, the systems, as I said before.

And when you hear that sentence, it tells you

or it throws you in the area of hardware, right.

How many transistors can I put inside the chip? Or how many qubits

can I put in my quantum processing unit? But it's not only there

because it just reminded me when you said that when you brought that

book of how to write code lines and logic

gates and so on. And now what we're doing in quantum computing is really at

that point we're taking logic gates and comprising algorithms

from them and so on and so forth. And part of the

scale up challenge is also to scale up

the quantum software and the compiling capabilities

and automate the design but now of quantum algorithms.

And this is another exciting trend. And there are a few companies, including in

Israel, that are doing amazing stuff on how to go

from back of an envelope kind of things, which is nice to begin with, to

actually doing large scale quantum algorithm

development that you would not need to understand at the level of

the individual gate what happens under way, way under the hood

in the quantum computers. And this is something that is also part of the scale

up, scale up challenge that we're facing as an industry

now. So you describe

quantum art as a full stack. What does that

mean in practice? Not just technically, but Phil

Philosophically, yeah. So there are full stacks and there are

full stacks. And everywhere, everywhere you think that you

end and you said, okay, this is the full thing. Then someone comes and says,

okay, but you need to look at it from broader, broader space. And I'll

explain what I mean when I say full stack. What we mean is that the

product is the whole computer, the hardware layers,

the software layers, and even the application layers. Although we don't necessarily

need to be an application developer per se in order to develop the

whole product, which is the computer. And the computer can

act as a standalone device that you deploy to the customer, or

it can stand in your lab and provide quantum

computing services over the cloud. Both of these models are viable

and happening. Why do I say that this is not

necessarily a full full stack? Because at least in the near future,

in the next 10, 15, 20 years, as quantum computing

scales up gradually, it will still be operated and

maybe forever it will still be operated adjacent to a very strong

classical computer, let's call it an HPC or something like that.

And so you could say that the combined hybrid system of

classical plus quantum working together, that is the true full

stack. So it depends on where you stop along the way. But

there are companies that are doing specific layers of the stack, for

example, doing only the quantum error correction

protocols, or doing only the electronics

that are driving the pulses that operate the quantum bits

inside. But companies like us, and there are a few tens

of companies like us, not many in the world in different qubit technologies

are doing the whole package, the hardware, the software and applications, all

under one roof. And it doesn't mean that we need to do everything and anything

by ourselves. If we don't need to reinvent the wheel, we'd rather collaborate and integrate.

And we're doing that extensively, both in our home country in Israel, and

also abroad in the US and Europe.

Interesting.

A lot of companies optimize only one

layer. Why take on the complexity of the

entire system? Yeah, that's a good question.

We started out based on 20 years of research at the

Weizmann Institute of Science. And the forte of that research

group was in coherent control method for quantum

systems, how to extract the most out of the physical system.

Okay. And this has aspects in the lowest level

of software or compilers that interact

immediately with the quantum bits themselves.

You know, in classical compute, you have computer

software engineers and you have hardware engineers, and they say hi

in the corridor when they meet each other, but they're not intimately connected.

In quantum computing, it's all still, maybe it will be different

in the future, but it's still now very tightly and intimately connected.

And once you have that layer of the qubits and the lowest

level of software, building this, the rest of the aspects of the quantum

computer above it is an easier task. And we can do that in

collaborations. And having a full computer puts you at

the top of the food chain or the value chain, if you would say,

because you can provide the full solution and then access

the best value or upside to the investor,

investment made by our investors, or to the markets that will

create revenue in this industry. So we thought that

when you have the heart or the core of the quantum computers

in the boundary between quantum hardware and software, this is enough

in order to build the rest around it and just go with it.

That's interesting. It's a full stack approach, which, you know, Candice is right.

Like, you don't typically see that. You typically see

people specialized. And I think maybe that's just. Maybe that's just

a function of how immature generally the industry is.

And I don't mean that as a bad thing of being immature. I mean, immature

compared to. It's the most mature it's ever been, but I mean immature in the

sense of. Compared to, you know, conventional computing.

Maybe I'll give it a little bit of color onto that. And that

is that because this is a very novel technology, it's

in an embryonic phase, if you like.

You'll get hurdles along the way. Okay? You'll get

unexpected stuff and bottlenecks and

challenges. And as much as you come to this

problem with multiple tools in your

toolbox of innovation and creativity,

you have a better chance of going over these hurdles

and on the way to the next challenges. And if we have

innovation that comes from both the hardware and the

software parts, it could serve in order to

overcome challenges in a better way and go faster. If you have

a particular problem. For example, we are tuning lasers that

are interacting with our trapped ion qubits, and you need

that particular system to be extremely stable. Any

instability translate immediately to lower

fidelity or quality in the logical operations and lower performance

because of that. There are some ways to solve this in hardware.

You're doing extremely sophisticated and advanced

optical design of your laser system. But maybe you could

relax the requirement on how stable you need to be from

a theoretical algorithmic approach or from their

architecture of how you build the QPU and

attack it from the algorithmic or software layers. So if

you have the capability to do both, you're equipped in a

very good way in order to Tackle the unforeseen challenges in the future.

So if someone only understood one thing about your architecture,

what would you want that one thing to be? Yeah.

So as we said, the challenge is scaling up. Today's

quantum systems are tens or maybe 100 qubits.

We need to get to thousands, tens of thousands and ultimately millions of

qubits. So how do you do that while retaining the best

performance in trapta and qubits? This is the qubit

modality or technology that has been around I think the most.

And it's leading in performance at base scale in all of the

major parameters like the coherence times and the fidelity and the

connectivity between qubits and so on. The shortcomings in the

approaches taken by trapped on quantum computer companies

are that the speed of operation of Trapton qubits

even at base scale are slow

relative to other qubit technologies. And as you scale up,

this becomes even a more complicated problem.

Secondly, there are

no good recipes on how to get to very large number of qubits before

getting to problems in maintaining the all to all connectivity and so

on. What quantum art is doing is, is using coherent control

methods of extracting the most out of the qubits in order to

reduce the time, reduce the footprint of the

qpu. Which means that for a given system size we can go larger

while improving the connectivity between qubits by hundred

folds. So we are tailoring an architecture that looks

at the best performance qubits and solves the

shortcomings on the way to scalability. Now that we are seeing

in other approaches, this is what we're doing.

So if your architecture is built around scaling, what's the

hardest thing about scaling that isn't obvious from

the outside? Oh, I think the

challenge is obvious. It's stability,

stability, stability. You need to have these things

extremely stable to have extreme well

controlled optical delivery system. I

described before that we have a laser beam interacting with a qubit, right? This is

a nice fun story. But when you have 100,000

qubits, will have 100,000 lasers, how do you stabilize

them? How do you control them? How do you make them

carry out the logical operations that comprise an

algorithm at that scale in a very good stable way without

needing to recalibrate like a billion

constraints all of the time. So the issue is how to

enlarge the number of qubits, but do it in a very, very stable way

so that we keep the base performance that we have. So

it's like, I imagine interference might be a problem. Interference is

interference a Problem? Interference with what? With the outside world,

different lasers and things like that. Like do the cubits interfere

each other or is that not. I think less so.

The laser beam diameter is about one micrometer. The size of

an atom is almost nothing. So we just need to take care that the

spacing between qubit and Next qubit about 5 microns. And

you're pretty much okay with that. Of course there are nuances

around this thing. I think it's

in, in the stability of the system, in the

overall operation, in crosstalks between

different segments in your quantum processor, that

kind of stuff. Interesting,

interesting. What about like,

I was talking to someone else and they had a very finely tuned,

like an earthquake basically meant they had to like reset everything. Is that something you

have to worry about or is that not a concern?

Well, it depends. It is a concern. We're working very hard to stabilize and to

isolate the system from the environment. Our qubits are held in an

electromagnetic trap in an ultra high vacuum chamber.

In order not to have, you know, we have, we're arranging in this trap that's

an electromagnetic trap that forces the ions in the simplest

of configurations to form a line of individual ions, one after the other.

An ion 5 micro distance, an ion 5 micro distance, and so on and so

forth, hovering in the vacuum. And the reason that we need

them to be in the vacuum is that we don't want to have, you know,

a stray atom coming from out of nowhere that's not related to our qubits

and impinge and collide with our qubits and force us to

restart the calculations or something like that. So our vacuum is

about 12 orders of magnitude less than the atmospheric

pressure that we know each other from around us in

normal environment. So we are investing a lot of effort

in stabilizing and isolating this environment and lowering

the electrical noise and laser noise

and phase noise and stuff like that. But

this is part of the job.

So you talk about going from tens to thousands and even

eventually millions of qubits. What actually

breaks first? When you're trying to scale

what breaks in what way? In terms of,

you know, I think in terms of, in terms of the hardware. Right. In terms

of, I don't know where you're going to say topology. Like what, what

is, what's your, I think really another way to rephrase this. What is the number

one challenge to scaling? Is that a better way to say an alternative

way to say it? Like, what's the number one engine? Is it an engineering problem?

Is it, is it A physics problem, or I guess they're kind

of related. But like what starts. It always starts

from the scientific problems or risks, and then goes to engineering

risks. But let's look at the basic way of

how quantum computer based on trapped iron works. Okay? We have this

string of individual ions. Each ion is a qubit.

We're defining the 0 and 1 logical states in

internal energy level pair inside the ion. And with a

laser, we can tune the the logic state to be at 0 or

at 1, or in a quantum superposition of 0 at 1 at the same time

and so on. That's the basic thing, operations on single

qubits in order to do logic, now we need to do

logic gates. This is an operation on one cubit that is dependent on the state

of another. So we have these two ions and there

are two internal energy level pairs and we need to connect them somehow. So we

need a mediator or a data bus in order to do that. And our

mediator is common motion. Like when I

pluck on a guitar string, I excite all these modes of motion in the string,

right? Then I pluck using lasers on

this ion string and induce motion. If I've had only

two ions and only one dimension, they could either move in phase like that,

or out of phase, okay? As much as

I add more and more ions, and I also work not in one dimension, but

in three dimensions, I add more and more degrees of freedom, okay? And

handling a lot of degrees of freedom becomes a serious control problem.

Now, for many, many years, all of the quantum industry, not only in

quantum computing based on trapped ions, but in all of the modalities, was

looking for what would be the simplest set of

logical operation or the simplest gate set that would

be universal, meaning that you can comprise any algorithm out of it, and it

turns out to be only operations on single qubits or

pairs of qubits. And with these two LEGO bricks, you can comprise any algorithm

from. So first, what we said is working with

the simplest set does not necessarily mean that you're making the most out of this

physical system. And so what we're doing, instead of working on

1 Qubit or 2 Qubit in series at a time, one by one,

we're operating large scale multi cubit gates,

which means that instead of shining our lasers on two qubits at a time, we're

shining our lasers on dozens of qubits at once. With a sophisticated

spectral engineering of the laser in all degrees of freedom, of

amplitude, time, phase, frequency and so on,

to essentially drive all of these Degrees of freedom at once to

complete one big fat operation that comprises

all of these possible pairs that we otherwise would have done in

series. So when we're doing that, we can

cram in one physical operation what we would need. A few

tens or a few hundreds or up to a thousand sequential operations. So

we save up orders of magnitude. And time is also relax the

requirements on error. Because we're doing just one operation instead of

hundreds of them. But this is nice only for

a time when you want to go to even larger number of

qubits. Then the number of degrees of freedom becomes a limiting factor

again. And then what usually people are doing is that they separate the quantum

processor into different interaction zones or different traps. And

they move the cubits in between them mechanically, physically. They

shuttle the cubits around. You can imagine an array like the streets of

Manhattan. And you're moving the cubits from one place to another in order to make

them connect and, and interact with each other. And this is a nice

idea, but it takes a long time because moving things mechanically

is a slow process always, certainly when you're moving quantum

systems, which are very delicate and prone to heating. What we are doing in

quantum art is replacing the cubit shuttling idea

by. By a sophisticated optical segmentation of our quantum

processing unit and then reconfiguring how we are

connecting the qubits together. So it kind of reminds of

how FPGA work in electronics, right? You have a certain configuration of the

hardware. Then you give a command and the hardware reconfigures.

So we're doing the same by having a long ion chain,

optically segmenting it a few tens of key bits at a time.

And, and then just reconfiguring where this optical segmentation

occurs. And this move the quantum information between

the segmented trap into the new configuration

without having to need to move any qubit around mechanically. So

we save orders of magnitude in time again. And we can get

extreme connectivity done in one fast

operation. And this will enable us to go and be very

modular, to go to very large number of qubits, which, while retaining a very small

form factor. This is at the. That's 80,000.

That's cool. I hadn't thought about that. Like that Actually physically moving

things, there's a cost to that. I mean, a time cost to that. Yeah, probably

an energy cost too. I mean, it makes sense, right? Like it's in time and

energy and also in. In protecting the information. Let's, let's go back a

little bit. Or maybe now it's Coming back to vinyl records, if you

had vinyl records and you had a scratch and you'd hear that scratch,

right? But when you move to DVDs, you need

that scratch to eat one hell of a scratch in order to listen to it,

to hear it. Because the amount of

data that you could cram into that could

compensate for errors that you had in a very good

way because you did things at the speed of light in very high

resolution. So our ideas go towards that kind

of way of thinking. And instead of doing things very slowly

that take time, that you cannot put a lot of information

at a given footprint or area

because you have to have these mechanical grooves of the vinyl

records in that analogy and you're moving to things that move at

the speed of light. You don't need to move things mechanically and you can more

easily go into protecting the information and doing more

information activity operations per unit time or per

unit area. I guess it's kind

of, it's kind of like solid state drives versus you know, the spinning

disks. Right. At a much larger scale or smaller

scale, but the same idea. Yeah, maybe that could

be. It's a good analogy or maybe a bad analogy, I don't know. But.

So you're currently building in a space with some very large players.

What's your unfair advantage?

Yeah. So specifically in trap time. Quantum computing is

a quite small, tight knit community. Most of the

companies came out of NIST in Boulder,

Colorado, where my two co founders, Amit and Louis came from

in the early 2000s or from the University of Innsbruck in, in,

in Austria. So everybody knows everyone, it's tight knit community.

Everybody is coming from the same kind of

origin. I think what we bring to the table is

two things mostly. The first thing is the innovation

and creativity in how to do the scale up. No one in the industry is

doing our multi cubit case, no one in history is doing our optical

segmentation and so on. And we're not treading on the same

beaten path, so to speak. And we did that

both on the basis of working in the trenches in academia

for 20, 20 years and establishing these coherent

control methods, and also by being a second

generation company. We had the privilege of looking at what are the

other people's approaches and trying to identify where we

think that they have shortcomings and provide solutions to them. That's

one thing. And the other thing is that we're

Israelis, we have chutzpah. We are used to work

in fast, efficient processes.

Everybody knows everyone. You went to the army with the same people.

You worked with in the university and then came to the industry. And

so doing things fast and relatively lower

cost and innovative and creative is something that

Israelis have in a very good way. And I think this is part of

what we bring to the table, at least in the technological

phase at the beginning. Interesting.

I grew up in New York so I knew what chutzpah means.

So did Candace. Ken grew up in New York too. So. No,

I mean it's, it's. And I think one of the things I remember reading

like the, the everybody knows. You bring up a good point. I think one of

the reasons the startup ecosystem in Israel is so good is because you

have that kind of that mixing pot of different people who come

in and they serve together for, you know, I don't know what the requirement is

but like, you know, whether it's a year or two years, but like you kind

of build people, they use that as an opportunity to build out their network. I

remember reading this whole thing about startup ecosystems and things like that.

It's really when you, you get really innovative companies when people that ordinarily

wouldn't mix or meet do meet and they

exchange ideas. That's just. Yeah. When I was in the Ministry

of Defense and we had joint collaborations with people from

the US services we always had these kind of jokes that

the people from the army and the people of the Air force said if the

Israelis would not bring us to the room together, we would not know each other.

Right. So that's part of it. But I think it's not the only thing. I

think that there is

necessity and there is also

the fact that people are coming from the military is that they're getting

a lot of responsibility, huge responsibility at

a very early stage and they mature and they're not

afraid to do things that require

a lot of guts to go ahead and do that.

And if an 18 or 19 year old

woman or man in the intelligence

units of the IDF are doing amazing,

you know, 007 kind of fantasy

work that we've seen in the last month or in the last two years

around the globe. And these are the same people that they're

doing that at age 19 or 20 and then they go out to

the world and they bring the

courage to do stuff. The ability to drive processes

that are very meaningful plus the innovation that is coming

from necessity being the mother of all

invention. And then they can have a good time

in driving this to applicability. Plus if you're

an actual life threatening situations, the idea of

risking everything and starting a company is Pretty low

on the adrenaline list. And I also think too

that, you know, one of the things that militaries tend to excel at or good

ones do is leadership training, right? Leadership discipline, that sort of

thing that I think really sets apart or sets people up for success

post military, right. To start a company, to do this. Right. And again, like,

you know, if you're doing life threatening neighborhood.

So I don't know about discipline, but. Yeah,

but. Oh, I mean, I think it's,

it's an interesting, it's an interesting melting pot in terms of like, you know,

ideas training that these kids get right at,

you know, early in their, in their, in their lives. Another thing,

another thing and, or rambling on this, but another thing of

the, the ethos, the ethos of Israel is that

Israel is, is like an island country, right. We're

surrounded by unfriendly neighborhood. Although I

hope that we will be able to be smart enough to have more friendly

neighbors and be friendly to them as well.

And the country has until recently

had no natural resources. The only thing that we had is the

brains and the people and heads. And

so I think that's also part of it. And the

forefathers of the country understood that they need to invest

in academia and in industry. The first

classical computer in Israel was built just 10 minute drive from

here at the Weizmann Institute of science in the 1950s. Right. That was less

than 10 years after the Holocaust. The country was just

established, people were getting food stamps and a group of

people was building one of the first 10 computers in the world.

So people had vision and we're standing on the shoulders of

these giants. I think that's also part of it.

And this also serves the fact that the Israeli government knows

how to do short cycles of transitioning things from

academia to industry and pushing the industry forward. We have a lot

of support from the Israeli government in doing that. Then you see a lot

of the smaller countries around the world or a lot of the smarter smaller countries

around the world do this, right? Singapore, right. They. You said something that a

Singaporean government official once told me a

long time ago that, you know, we didn't have any resources. We were an island.

They, in that case, they were the word literal island. Right. So they realized they

had to develop their people. Right. And you look at the success of Singapore, I

was listening to a podcast. They have discipline. They do,

yeah. Discipline is an important thing. Right. You know,

Taiwan as well, right. Like, you know, speaking of disputed territories.

Right. We're hitting them all today, Candace.

I know I know we're not gonna, but you know

TSMC was founded by, I forget the guy's name,

but he was a well established guy in Silicon Valley in hardware

manufacturing. And, and they basically really Taiwan

leadership realized that they needed to, they

needed to build out a much smarter, more intellectually focused

in country manufacturing. Right. And, and you know,

you know it's a legendary now tsmc. Right. So it's an interesting,

I mean. All right, I think, I think investing in people is, is,

and if you do it smartly is always a good investment.

Sure. Everything starts and ends with people. Yeah,

yeah. For now, until the AI robots take over. But you know, then

we'll have other problems. Do you think the industry

will converge on a dominant modality or will multiple

architectures coexist long term? So I think

depends on what long term is. Certainly now we

have a few tens of full stack companies and we have over a thousand quantum

companies in general. This will scale down and

converge I think to a handful of companies.

I think that in the short and medium term there is no reason that there

will be a single winner takes at all. There is

a big enough blue, enough ocean out there,

but there will not be multiple vendors of each modality.

For example, look at the aircraft industry. How many aircraft manufacturers you

have in commercial aviation you have a few but not many.

Same in classical compute you have a few but not many. So we will get

there. Will it be only one technology? Could be,

but in a very long term, I think not in the foreseeable future of the

next one or two decades because we are still in the

ramping up. And while we are ramping up,

although quantum computing is a universal platform, it could solve

any algorithm on the way there.

There are, you know, nuances and there are,

there's a grayscale and advantages and disadvantages, different

modalities for specific applications. And I think this is the era

that we're getting into first.

That's a good way to put it. I, I think that eventually I think we're

really in this. You know, you mentioned the early computers when there's only like maybe

10 or 12 of them around the world. Right. I think we're not

quite that early on, but we're definitely in the transistor ish

era. Like yeah, that's a good example. If you would take

a few years before the transistor and you look at the ENIAC, for example,

that had, you know, vacuum tubes and if you would

bet on a company building the world's best

vacuum tubes, what would have Happened.

Okay, so we're not there yet. We are now at the. There

are few technological alternatives. There are

pros and cons. Ours is the best. So if you

bet on anyone, bet on us. But we need the

technology to grow up a little bit more and start to converge

before we anticipate a winner. And even if we have

a winner, in the transistor era and the integrated circuits,

you had CPUs and then GPUs, now TPUs and so on.

And that also evolves. So I guess that we

have a little bit of ways to go before we see

convergence to fewer modalities.

Interesting. I'm sorry, Candice, I don't want to hog the mic. It's all

good. When you're designing architecture, how do you decide what not

to build? What gets intentionally left behind?

Yeah. So nothing should be left behind if you're doing

the full step, Right? Okay. But you need to be very focused in what you

need to do in order to show the added value that you

are bringing to the table and not do more of the same that other people

are doing. And then you're not special and you're not bringing value. So

we're focusing on the core of our products in the

coherent control methods that I described before. And so, for

example, when we're now about to launch our cloud service

soon, there are a bunch of people out there who know how to establish

this cloud layer of interfacing and authentication and billing and

security and stuff like that. I don't need to do that by myself, although I

can if I have to. Similarly,

there are very deep

technological problems in certain layers of the stack, for

example, quantum error correction. So we are doing quantum error correction

internally for our specific architecture, but we'd rather

collaborate with companies like Kidma in Israel or

riverlearn in the UK or others that all what they

do is specialize in that particular layer of quantum error

correction and bring that and integrate into our solution. So you

need to balance between what you're doing in house and you're not letting anyone else

share, and what are the things that are totally

technical and you'd rather completely outsource if you can, and what

are the things in between that you'd rather collaborate and tap into the

best talent in the world, rather to build a very sophisticated

muscle on your own. And the distinction between these three

things is something dynamic. It evolves in. In time. Right. At the beginning, we've

done only the core. Now we're doing more than that. At the beginning, we

like. We like to do more collaboration. And we were dependent

on collaboration. Today we're much more independent. It thinks that evolves as

the company matures.

Interesting. I know we gotta kind of wrap this

up soon, so. So where can folks find out more about you,

what you're up to and. And where. Where you're going? Like what. What. Where

can folks find out more about. Unless, Candace, you had any other questions?

Oh, no. I mean, no, I. I want. Where can people find out more?

Yeah, let's find out. So Quantumr

has a Wonderful website. It's

www.quantum

art.tech, which is kind of

long, but you only need it once. We have their

explanations about our technology and our architecture and our scientific

publications and our press releases. It's all in there, including our job

section. We're growing quite rapidly, both in Israel and

abroad. So you're. You're more than welcome to

check that out. We're also on LinkedIn. We're also in conferences.

I think the nearest one that we have a booth and

presenting is going to be at the end of June in Boston, the Quantum Tech

conference there. So you're welcome to shoot us an email

or come visit us. The best thing is to come visit us and

see the labs in action. So if you're in the Middle Eastern

neighborhood and you're not afraid, then you should definitely

come and we'll be happy to host you. There's some good deals

on hotels and travel these days, right?

Not on airfare. Okay. Prices

are out of this world.

Fantastic. That's fantastic. Thank you. Thank you so very much.

Anything else that you want to add? No, I'm just fascinated by this. Like, it's

a. It's a definitely. I definitely want to take a closer look at your website

and kind of see what sort of text, because it sounds. Sounds like you've got

a lot of things already figured out in terms of. It's

a long. It's a long path, though. Yeah. Yeah. Well, and also too, you

know, you have, you know, whenever your company was found, I forgot the exact year.

But like, you have. People have been doing this since the early 2000s. Right.

This was not but a gleam in some people's eyes, you know, more than

a decade ago. And, and you know, my wife actually works at nist,

so it's kind of cool. You mentioned nist. Oh, which of them? In. In the

one in the one in Maryland. In Maryland. But in the Quantum stuff in the

joint. Quantum. No, she does it. Security infosec.

I saw that.

Yeah. No, but it's cool. The very hard to work from.

From the outside with them. Right. That's cool.

Plus, Candice's dad was a researcher

at. IBM working on early. Yeah.

Yeah. So kind of when it was just on the chalkboard, Right? Or whiteboard.

Yeah. So cool. Well, awesome. With that, we'll let the outro.

Quantum podcast? They're breaking the mold? Science and sky Beats

is bold and it's gold?

The multiverse is skanking? Skanking in time? Black holes

are wailing in a horn line? So fine? From blank scales to planets? There can

be connecting the dots? Candace and Frank? They're the cosmic

hotshot?

Quantum podcast? Turn it up fast? Candace and Frank?

Blowing my mind at last? Quantum podcast? They're breaking

the mold? Science has got beats? It's bold

and it's gold?