Tales from the Quantum Industry

rc3 preroll music

Herald: And welcome back from our studio
live, as you could see in Halle! laugh The next

talk will be Natalie Kilber. She will talk
about tales from the quantum industry.

Natalie works since many, many years on
quantum computers to make them real and

useful.

Natalie: Hi, I'm Natalie, and I've been
talking about the progress, the prospects

and the poppycock, the nonsense of quantum
technology, or you could also say tales

from the quantum industry. A little bit
about me. I'm a prehistoric creature that

has been there since the field emerged.
I'm the masses of stories for you today

and you might ask yourselves, but why are
you so gung ho about quantum computing,

buddy? Well, if you look at the Moore's
laws trends, then you know, already that

since 2000, the clock speeds have been
kind of stagnating and we're going smaller

and smaller, and IBM is going to fabricate
a chip of about two nanometers in 2023.

The problem here is the smaller you go, if
you go smaller than one nanometer towards

sub nano meter scales, then you go into
the quantum regime. And if you have a

single electron transistor, you already
have a quantum dot. That means that's a

qubit, that's part of a quantum computer.
But it's not reliable for classical

computing, for any classical computation.
So. Well, there we have it. We already are

at a quantum regime if we want to go into
the future. So why do I want quantum

computers? I'm gung-ho about speed. I'm
gung-ho about premium power. I want more

juice. So first, look at your PC, now back
to me now back in your PC. Sadly, it isn't

an eight Core i9, or maybe it is so yet.
Are you happy about your wiring? Well, I

don't know why, but this fellow's also
happy as a muffin about his wiring. This

is a quantum computer in Google's lab, and
you can see the wiring is not trivial for

this. And this is just one little chip. So
a quantum computer is an accelerator, and

you need a co-host CPU to to do any sort
of meaningful computation with it. And if

you look at the wiring here, there's a
different type of quantum computer. You

see an optical table with optical
components on it. I think this one is the

QuEra startup, and this guy's not that
happy about his wiring. You can see why.

There's lots of other examples that look a
bit difficult. And here specifically, you

see a lot of controls that are sending
signals into the quantum computer. And

this one again, is QuEra with a bit better
wiring. This is specifically a trapped ion

quantum computer, so they use trapped ion.
Quantum computers don't come in one

flavor. We have different flavors with
different bases of fundamental technology

that we use with different types of
components. So in trapped ions that use

photonic components, there are photonic
computers in itself. And for example, this

one is a huge cryogenic fridge. So you go
up to mini sub Kelvin stages right at the

bottom and the first picture you see in it
without his clothes, without the enclosing

the enclave. And then again at the top, the
massive wiring for just one chip. Then you

have other examples like, for example, AQT
Alpine quantum technologies over there

in Austria, and they know how to stuff
their cables really well and well. You

might wonder why am I not talking about
quantum inhalers? And well, if we define

them, if we define quantum computers, so
legs then a solid is a quantum computer

too, you know, why a solid any sort of
type of plant uses quantum phenomena as

well. So because of photosynthesis, the
light tries to travel as fast as possible

to the side, and they do it through
quantum tunneling and it solves it pretty

fast. Yeah. So no quantum inhalers. Then
look back at your PC, can it stand five

gigahertz? You think you're unhappy about
your clock speed? I think I won the

Complaining game a quantum computer can do
no more than 100 kilohertz, and that's

twice the speed of the ENIAC back in the
day. But then again, don't be so harsh in

your setup or on the quantum computers. We
still tinker with them with the

capabilities we have. You've seen in the
pictures before, there's a lot of wiring

there, components that are quite big, that
haven't been invented yet. So. The

bottleneck component of a quantum computer
of any set up, the slowest component is

your bottleneck clock speed or a
bottleneck in your clock cycle. And that's

why they're so slow in quantum computers,
they have digital signaling processes.

That means you have to convert digital
signals to analog and analog signals to

digital signals again. And we have that
everywhere in our phones and our cameras.

Imagine just sound that is analog, that
has to be converted into digital signals

or, you know, literally light photons. If
you take a photo in two digital signals,

that's a analog to digital interface that
we have. And here, because we're like

shooting microwave pulses and, for
example, superconducting computers and

qubits, that's kind of difficult to do.
So, yeah, you might say, but they are

parallel and they do everything a little
bit different. Yeah. For algorithms, when

you have such slow clock speed rates, if
your time to solution outlives you, that's

a problem. If you don't live to see your
solution, that's too slow and. You might

want to listen to your computer, listen.
So 30 dezibels or 40 decibels? This is

what economists. You can hear this.
Yeah, that's the

Woman voices speaks "Welcome to sound of IBM.

truth of that sound, but you needed it.
That's so annoying. One tempers at the.

You get into this by this. Then you have
this wonderful. The nightmare is now. It's

a quiet place. But one thing is always the
same. We always talk about size and with

size, I mean, we talk about qubits. You
might read in Wired or Spiegel or wherever

you want and hype articles or just
articles talking about the advancements in

quantum computing, how many qubits they
they could instantiate on a chip. IBM

released about 127 qubits in QuEra bit about 200

years. The trapped ion one and IBM was the
superconducting one of these cylinders and

the cryogenic fridges. But here you have
to discern a lot of physical qubits is

good, but a logical qubit is what you need
for computation. We have a high error rate

for just one physical qubit because of the
noise. Because of temperature. All types

of noise. All types of environmental
factors that you can't eliminate yet,

because this is this is quite fundamental
research how you can control these things

and how you can adjust the parameters so
noise stays low. And then again, we have

these types of signals and in our normal
classical devices where we need parody

checks, where we need error correction and
so do quantum computers. Error correction

was a huge field that needs to be
advanced, and we use things that are

called surface codes and these are error
correcting to get one logical qubit. So we

have reliable computation. We need a lot
of physical qubits. So you could say

there's a lot of overhead for those error
correcting code and parody checks. So if

you hear about those, many Qubits have
been have been accomplished by a company.

It's usually physical qubits, but then
another factor of 20, that's just one

logical qubit that you can use. Yeah,
that's difficult. And there's a famous

physicist that said, Well, he's still
alive, so it's actually on Twitter. And he

said, Well, Qubits are like children's
better to have a few high quality ones

than a bunch of noisy ones. Yes, I agree.
And John John Prescott has been at

Microsoft before, and now he's at 8WRS.
But at Microsoft, we witnessed

Maironascandal. Well, we thought we can
have a topological qubit that has no

noise. That means if we have one qubit, we
don't need these many physical qubits to

have one logical one because it is a
topological one with no with entrenched

error correction. One could say by the
physical nature. So you also run a blood

cells. Fittingly, it was called the
elusive marihuana particle because yes,

we've been waiting for 10 years for this
sort of maiorana qubit. But there was this

scandal. The big maiorana qubits
wasn't the big one. After all, they had to

retract the paper that said they found
one. So we're still looking for it. Yeah.

But then again, it is better to have a few
noisy physical qubits than none at all.

So, yes, quantum computing is full of
challenges. You've seen the wiring.

Getting so many wires into one of those
cryogenic fridges is very difficult. So we

have to find new ways to get see my those
those control those little controllers

into that fridge. So we have to reduce the
wiring, for example. And that's not a

trivial task because you get a lot of
resistance when you go colder for four

cables, for example. We're advancing
microwave technologies with quantum

computers. And one thing that kind of
worries me the most is that we don't have

quantum memory yet. So cue run from random
access memory because at the moment, a

quantum computer is just an accelerator.
So it's a read only memory. So everything

that is on the chip or on the qubits, on
the setup that is read out like that you

can store them or you can do any more
meaningful computation. So that's a huge

bottleneck. Another thing is the ethical
dimension we have to use in

superconducting quantum computers. A lot
of helium and helium has supply

bottlenecks, with just two companies Qatar
Gas and then one Northern Texas company

that supplies helium. That is not really
the problem, though, because we need even

something else. We need three helium,
which is an isotope, and that you get by

as a nuclear byproduct for tritium. That's
not something I want to I'm going to count

on, especially because these are limited
resources. And sometimes the components in

quantum computers themselves. They're also
rare earth metal. Those are also limited

resources. And then people keep talking
about democratizing quantum computers. Yet

you have other problems there first. Not
everyone needs access to something that

doesn't, doesn't solve a lot of things
yet. And to be honest with the security

controls in place, it's kind of an open
system already. But yeah, when we look at

quantum, we have to think about the
references. Which specs do you have to

look for? And the magic here is common
sense. I've shown you compared to what,

you know, the components that you know.
And again, the magic is common sense. And

quantum computers are very specific that
quantum technologies, the component of a

quantum computer, the sensors, single
electron sensors that we did. We use an

MRI that we're using spectroscopy for
microscopes and yada yada, even more

things and quantum communication types. So
semiconductors or or something else,

semiconductor components or just our
infrastructure and communication. They can

be part of the quantum technologies as
well. But you have to be also careful.

Everything is quantum now. It's it's quite
the hype. So finance is doing somehow

quantum. I don't know what other companies
think. Well, the buzzwords cyber isn't

enough and used two buzzwords: quantum and
cyber. I'm very curious what they do. Then

there's quantum transportation. I'm lost
here. I don't know what they do. I don't

want to know. And here, I mean, I'm sure
that is pain free. Yeah. You can also have

to be in and actually I really wanted to
find this in April 20 20. Yeah, so quantum

computing is claimed to solve a lot of
today's problems. Some companies claim

they're battling climate change, that
transforming the pharma industry to

transform the finance industry into the
break all encryption in the future, as we

know. So quantum computers will break the
internet in the future. Yet again, looking

at the reasons estimations, not including
the clock speeds or the actual

performance, that's difficult to claim.
But then again, looking at the references

and the facts and the specs, not all claim
these weird things, but the reference

facts like see lobs from IBM. These are
advancements that are meaningful. But then

again, we have this flood of references of
qubids of we're advancing this and that

complexity theory claims. But how are you
going to test these complexity theory

claims? Well, because we don't have the
Quibits, are simulated on a fantasy

machine. And if anyone like this old chap
here had to deal with theoretical

complexity resource estimates a.k.a.
fantasy language. Well, welcome to

imagination land this town. This town is
not a nice place for little fillies all

alone. There are lots of twists and
corners that could lead to the unknown.

Let me guide your way, and I'll be sure to
help you through. You could really use a

friend of the , and luckily, I've picked
my three favorite corners for you. Well,

quantum applications were applicability is
optional. So come on, let's start with the

very well-known topic of optimization and
the beginning of talks. I want premium

power. I want maximum juice. So VSLI
design, what is a VSLI? It's very large

scale integration and it means you need to
partition these little chips that we have.

And the first chip that we had back in the
day was an integrated circuit to help

people hear better. So it was a hearing
aid that Jack Kilby in 1958 designed and

this thoughtful design thought were the
basis of it is the basis for our

technology everywhere, and it's not a
trivial task to design these chips. So you

don't have a lot of waste and we can pack
more and more components on these little

chips. So integrated circuits. If you
don't know anything, IoT is that. Another

problem? The mathematical basis for this
problem is the same for network design or

less waste and manufacturing like stenting
or lasering, even flight scheduling

between cities has this mathematical
problem. Some might know it as bean

packing, max card or multi card problems.
You either seek to minimize or to maximize

an objective. So those commentary problems
are really one of the hardest one to

solve. And I like to call them
combinatorial black magic. These levels of

hard to solve are classes in themselves,
and this is actually a real graph. This is

a Peterson graph, and you can tell it's
it's black magic you might think. This is

not that hard, but I'll show you a
benchmark of max card problems. This one's

NP hard NP complete. This is one of these
fantasy language classes. It just means

that no polynomial time algorithms for max
card in general graphs are known. That

means, again, your time to solution
outlives you, and it's a problem if you

need to wait until your solution comes and
you die before. Or maybe it needs be a

couple of hundred years. I don't know how
long you live, but some say it's almost as

hard as beating cut of meat and dark
souls. But yeah, you don't live to see it.

That's the that's the drawback of this. So
yeah, you might think optimization, it's

black magic, it sounds weird, but you have
heard these terms before. I will

specifically be gung ho and talk about
nature inspired once the physics inspired

algorithms. But, you know, neural networks
you probably know to boost surge linear

programing, mixed integer problem
programing with a branch and cut, you can

see the max cut promise that's in the in
the brown part. And then, of course, other

nature based methods like bad surge,
genetic algorithms of small methods where

it becomes quantum is. And that's that's
what I like about the nature inspired part

the nature inspired optimization
algorithms. For example, they minimize the

Hamiltonian of an icing model. So whatever
mathematical but mathematical basis you

have, you minimize and maximize your
objective. Hamiltonians are something you

use in quantum computing and the icing
model I can explain later free up a little

bit more time. So what we need here to
with classical and quantum computers is

benchmark, so we can compare apples to
apples because classical computer and

quantum computers is more like apples and
bananas. So we need a common ground. And

if you want standardized benchmarks for
such problems, you can Google Chuck SHOOK.

It's a it's an open source benchmark suit,
and you probably see it in the slide. Good

old Professor Katzgrabor. He has written
this benchmark suit and he's gung ho about

cats, so please spare him of cat content.
So, yeah, I told you we'll get into the

max cut benchmarks. This is from a paper
of Cambridge, I think Cambridge quantum

computing and these little circles, these
little dots, steel nodes. And you can see

they have done they've done it on a
quantum computer for ten nodes. And it's

very complicated. Yeah. And the problem
here is when they went to 13 or twenty

three qubits, logic of qubits, they had to
simulate it. They had to put it on a

fantasy machine and classical hardware.
And yeah, that's that's also one algorithm

they used. Vicki variational quantum ion
solver and Qrolla, both of these are

approximate algorithms you can think of
very noisy, annoying quantum computers

that don't spit out results. But if you
run it 100 times, the majority of it will

be towards the correct regime. And yeah,
that's that's how you go about it. And

this is a relatively new paper, and I have
to say these resource estimations, these

are amazing results, and I'm not worried
about the algorithmic advances in quantum

computing because we have smart people and
I want more smart people. So if you want

to, you should get into it. So, yeah,
that's that's not what I'm worried about

yet. I don't want to solve something for
ten qubits or sorry, ten nodes on a

quantum computer, yet we can solve
something bigger. So this is from another

paper from a nature inspired, physics
inspired algorithm. Some already call it

quantum inspired. These are 100 nodes, but
at the lowest, you can see the physics

inspired GNN and Pi G, and they managed to
do it with a ten thousand nodes. So on

classical hardware, the quantum the
quantum algorithm put on classical

hardware to overcome the cube hardware
limitations by treating these physics

algorithms as optimizes. So from a
business perspective, if I want to have

maximum power and maximum Dru's, I would
use classical computers and use heuristics

from quantum and classical until the
quantum computers are ready. So, yeah,

neuro, I'm sorry. Nature inspired
optimization with quantum algorithms.

That's like putting neural networks on
steroids. Quite like that. This is the

paper for it. But yes, we've been far deep
into one corner. So I'll drag you back

here and I'll show you another one. Some
companies claim we were solving climate

change with it. We're transforming pharma.
And yeah, this comes from from ideas of

physicists. What I said. Well, the nature
is quantum mechanical. We might as well

need quantum phenomena to simulate what is
right. But yes, it's not that easy. This

physicists played bongos and strip clubs.
He's a real hero. Many of the physicists

he's known for that are talking about
chemistry. Here's ammonia. You don't think

this is difficult, but ammonia is used for
a lot of things in the world who use it as

a base, if there's something acidic, you
use it as a fertilizer, you use it in a

lot of things in chemistry and even raw
latex is has been transported with it or

anything that has an acidic nature. You
get it by a very difficult process. Well,

it's not a difficult but energy
expenditure high one. So you need high

temperatures and high energies to put it
into the harbor Bosch process, and it

accounts for two percent of the global
energy expenditure. It's a very famous

problem that quantum physicists wanted to
solve because it's really useful stuff

ammonia. And if we can cut two percent of
the global energy expenditure, that's a

good thing. It's not trivial, though,
Richard said it. It's not an easy thing to

do here. You can see just the active side
of an enzyme where you can produce ammonia

without high temperature and high energy.
Bacteria can do it by room temperature,

ambient temperatures. There's algae.
That's all types of bacteria that can do

it, and the active side is called from
FeMoco. You can see the resource estimates

for half of the sides, for the for the
energy to simulate, to see how this works,

because bacteria can do it. We don't know
how they do it. That's why we use so much

energy in temperature. The enzyme and the
material looks like this. And then again,

look back at the computer for both parts.
We need over 2000 logical qubits. Now,

think back, physical qubits are by a
factor of 20 or 100 more. So we're not

here yet. Then again, classical computers
can simulate it either, and we will

probably simulated that on quantum, but
we're not there yet. And to put it into

perspective, to the far right the orange
little molecules to form local bits in the

whole enzyme. And you might wonder what is
the THC cost while that's tens or hyper

contraction, so you algorithmic
advancements, I'm not so worried about.

We're pushing, we're pushing the frontiers
there. So yeah, but but the imagination

land, the most powerful magic is common
sense, and you should read it. So what do

you think? Do you want to use a quantum
computer or intermediate steps to find out

what we need? Well, what people do these
days is they're bit smarter and they do

simulated. They do use some digital parts,
but it's mostly haptic. Haptic means they

simulate a little bit and they tested in a
lab and got it tested in the lab. They can

funnel down what they need to simulate.
The paper I'm talking about for the

smokable and theological cubits is a very
recent one, so it's just a couple of days

it's been published and I think this is a
preprint even. And if you want to know

anything about resource estimates and
quantum computing for chemistry,

specifically Nathan Vibha and Ryan
Burbuja, a good place to look for. Then we

are still a quantum applications for
applicability is optional and it has been

true so far, hasn't it? Let's move to a
corner that hits closer to home,

cybersecurity. We have to be specific
here. I know a lot of companies claim

there won't be any type of encryption as
we know of in the future, because quantum

computers will break it off for once a
year to fifty sixty five fifty six years.

As bad as 256 bit mode can be broken by
quantum computers and symmetric key size

symmetric encryption methods are known to
be quantum secure the specific key size.

So not really. What people usually think
of as asymmetric encryption. So, yeah,

these are some resource estimates to look
out for. This is a Microsoft paper not too

long ago, and they said through a punch
line, it is easier to break elliptic curve

encryption than RSA. Then Google, not too
long ago, came up with two million noisy

qubits or physical qubits to break RSA
2048 bit in eight hours. And then also the

news paper saying that factoring a 2048
bit RSA integer can be done in one hundred

and seventy seven days with about a little
bit more than 13000 qubits, but with a

multimodal memory that does not exist yet.
These are incredible results over the

years in resource estimation numbers. Yet
again, let's put it into perspective. So

2012 he said, it's a billion in this year.
2021 isn't over yet. This year, Google

came up with 20 million noisy qubits and
then Gaussian came up with a little bit of

thousand or more, but let alone any
workable implementation of curium as a

purely theoretical nature as of now. So
we're still in imagination land when it

comes to breaking the internet as we know
it. It's time to leave Fantasyland, or you

might say, hey, but we did factor
relatively high numbers back there in

2013. You've heard this in the news. Well,
yes, we did. But if you know the base

beforehand, so if you know that with
thirty five, the number thirty five, you

can divide by five or seven if you know
one base, that's a really easy thing to do

and you can do that classically as well.
So IBM had to counter published that they

were oversimplifying quantum factoring,
and the algorithm you use for it is

Schwar's algorithm. It's one of the
purebreds quantum algorithms out there.

And then again, another one pretending to
fact the large numbers and quantum

computers. So no, we haven't been able to
break it so far. Another one in 2019, and

this is in very, very interesting one
because IBM goes close to these problems

and says, yeah, well, I want to test it. I
want to simulate it. A sorry, not

simulated. I want to test it literally in
quantum hardware. And they did so, but

they failed to factor just the number 35.
So I think we're safe for some time. You

have to think of quantum computers not as
a quantum threat, but more as a quantum

advantage. If someone knows how to steer
encrypted data and store it about 20 years

to decrypt it, you know, get it now and
decrypted 20 years later and stored

somewhere, they probably know where to get
it unencrypted as well. They're more low

hanging fruit for them, and I don't think
they will wait until the quantum computer

comes into fruition to do these sort of
things. So let's put the quantum thread

into perspective. Quantum computers are
logical extensions of Moore's law strand,

and quantum computers are tailor made for
simulating the behavior of quantum systems

like molecules or materials, and whether
they lead to breakthroughs in cryptography

or optimization problems. That is less
clear yet, but we're we're pushing the

boundaries. If anything, components of
quantum computers are pushing the

boundaries for us literally now, if we
have better seeds like quantum random

number generators for short Q, R and GS,
that is very useful. We need seeds that

are truly random. For example, in places
where we can't use true random number

generators that use entropy to generate
the random numbers because in a data

center, you don't want a lot of entropy,
so you don't want temperature diversity,

you want it to be cold and stay cold, or
sometimes you don't have the possibility

of having this anywhere where it's just
not there. So we do make things smaller

with it as well. You've seen the wiring,
so we have to design microwave technology

or any type of cabling, any types of
chips, um, pre processes that can go into

smaller and smaller spaces. So yes, we do
need quantum computers and the research

around it. We don't need it in business
settings just yet because they're not

ready. This is still very much fundamental
research, and we should note that so

mathematical concepts are more useful to
find. Also new ciphers when we're talking

about cyber security. And I'm not talking
specifically about peak. You see, there

are other mathematical mathematical
concepts for asymmetric and symmetric

encryption that can be that can be used.
But for now, let's leave imagination land,

and let's think about how quantum
computers interface with the world. Well,

I've shown you before that quantum
computers sometimes have a crude and

fridge, so if you look at the cylinder,
you see the the enclosure of it. So this

specific example, I use a superconducting
computer for now, I've told I've told you

before we need a host CPU and then a
control system. Lots of peripherals and

wiring to get into the cryogenic stage and
the enclosure. And there we usually have

an analog to digital digital interface.
And at the bottom where it's the cold is

the qbu. So you can think of it as, yeah,
a huge system. So this is an example of

Google's setup. And I think the key
concept that needs to be highlighted here

is the quantum computers are merely core
processes. And as such, they depend on

traditional compute environments to host a
quantum processing unit, a cube you

require as an analog to digital interface
to to convert those signals back and forth

and in turn, the application logic in the
host CPU. You may connect to a network

may. Some people think if I have it in the
lab and it's not connected to anything,

there's must be air gapped. But then
again, you know how loud these devices

are. So you kind of want RTP so people
don't become death and we've corona, you

kind of want people to work from home as
well, so they won't be arrogant. For the

foreseeable future, I guess we're for the
next year at least. So the issue of cyber

security and mass and quantum computing
resources that is rarely discussed, these

systems are and they will be hybrid
systems for the foreseeable future with

those CPU hosts with cloud based or
managed APIs. And we need reliable

services and secure services and
architectures as this arises. So

subsequently, the critical applications
and data these systems will handle and

store if it's the knowledge and the
algorithms, how to how to simulate for

Mocko we can produce the ammonia with less
energy expenditure if we design new

batteries. These are probably patents, so
we want to secure the data behind it and

those algorithms. So this means that all
classical security best practices hold for

quantum computers. So this example, the QC
lab at Google, sees enterprise system

constituted of a mix of Windows, macOS,
Linux, maybe Azure, Adi, SAS network,

containers, whatever platforms. And
they're part of these industrial control

systems and programable logic controllers,
pulses or discrete process control

systems. You know, anything in ICS, Escada
that is rarely air gapped or physically

means physically separated from any
network. So we need API hardening. I see

our security is not a big topic in quantum
computing yet because it's still just a

system on the internet, and it's not quite
ripe yet. People sell it and companies put

sensible data on there. So if this is back
in the day got infected with the MIMO worm

that was considered air gapped. No, I CS
system is truly, really arrogant anymore.

So before we offer quantum computing as
breakthrough accelerators, we need to make

them safe to use. So if you want to join
me, let's protect quantum computers from

getting pond. Thank you for listening to
me. That's talk.

Herald: Thank you so much. Um, we have
some time for questions. So, uh. Audience,

dear audience, please ask some questions.
The hashtags are on Mastodon and Twitter

hashtag RC3Chaos Zone, and the I.R.C. room
is the Channel RC three Dash Chaos Zone.

All right, and I will watch the questions.
All right. We have some questions already.

Herald: What do you think about rolling
out so-called post-quantum cryptography

now?
Natalie: Oh yeah. Post quantum crypto, I

know it's been. It's been a useful concept
promised and they have a never specific

problem in mind. And this is for the
national security and probably the

government, for infrastructure and in the
U.S. specifically. But they're thinking

of, along lived systems, the pig. You see,
you have the problem. It's highly

computationally intensive. So a lot of
infrastructure can't, can't cope with it.

So we need to deploy other infrastructure.
And if you're worried about your data,

you're in the intelligence behind your
data being stolen and then, you know, say,

for 20 years. Not many companies have
secrets that you can store for that intel

isn't that specific data that data steal
and store that is useful. So if you have

data, doesn't intelligence lie for over 20
years yet? It's useful if it's a

government side of, you know, it's a
nuclear bomb placed side or something very

critical. Yes, you have to think about it
now, and we do need time to implement the

infrastructure. And I mean, the hits close
to home. We've heard about crypto agility

to think that we would like to have, but
it's it's not the reality. We just have

legacy systems. We have to keep them
running. And especially if it's critical

infrastructure, you can just turn it off,
build something new and turn it all and it

has to work throughout. So you see is
useful for some problems, but not for all.

It's not a one fits all glove.
Herald: All right. All right, thank you.

The next question is, you talked about the
current number of qubits and how no

practical problem, a lack of the difficult
problems that the people are hopeful for

quantum computers to solve. The technology
isn't there yet due to the low number of

qubits. Would it make sense to serialize
the problems and run them on low qubit

count quantum computers? Does that work?
Natalie: I think I might not understand

the question fully, but I assume you mean
you package these little programs and I've

shown you the algorithm, the THC, the
tensor hyper contraction algorithm that

the chemical guys have used where we do
these sort of things. But then again, one

qubit you can think of roughly as one
transistor and you just need a couple more

than five or 10 to do meaningful
computations, as you've seen. That is a

very good question that we do package
these problems into smaller bits. And if

you go back into the slides or look into
the the the paper of Nathan Vibha and

Rayen Babbush around because you see that
you need still about more than two

thousand logical qubits, so you're spot
on. This is the direction that they wanted

to go and we have to go and there to try
to. Unfortunately, we still need more than

a couple of hundred.
Herald: So are there any current quantum

computers that are programable to do
something useful?

Natalie: I mean, it depends really useful.
It's very educational to use them. If you

want to have a have a workforce in 10
years that knows how to use them, you need

to do. You need to have, you know,
postdocs or master students who know how

to program these things. We need to know
how to write better compilers. What are

the what are the bottlenecks, how we can
swap gates, quantum gates? Some of these

are operations on a quantum computers. So
how we can swap these things and there

that's a useful thing for them to do in
any stage are workable quantum computer.

Just a few qubits is still needed to
advance the field and to advance the

workforce. So for me, it is still useful.
Herald: All right. Yea, it makes sense.

What do you see as candidates for earliest
productive uses of quantum computers?

Natalie: Oh, so you mean the question of
the killer application for quantum

computers? That's a difficult one. So for
cryptography or for optimization of I've

said it before, it's less clear. But for
chemistry, once we hit those 20000 or more

logical qubits, we'll see advancements and
catalysts. You see it from local molecules

to active side for the nitrogenous to to
get ammonia at room temperature. And

that's where I see the advancements for
four small catalysts for get alloys and

metals to find better storage batteries.
There's there's still a field out there

that we have that we couldn't simulate on
classical because it's quite intractable.

But we're pushing the field and I think
chemistry could be one of the first ones

that's just not there yet.
Herald: All right. Do you also think

that'll be the earliest one's chemistry
applications?

Natalie: Small molecules for catalysts?
Yes, they could be. I mean, the smarter

people than me out there might have better
ideas. Maybe design a completely new

battery storage or I mean, ammonia is
being used in fuel cells as well for

storage. Maybe they'll simulate how to get
ammonia, cheaper energy expenditure wise

and then use it to store, have better
storage and fuel cells yet. I mean, there

are some quantum computing services out
there that are kind of interesting depends

what you're looking for. Yes. In
Cambridge, quantum computing offers a

three qubit encryption suite if you want
to do QCD. I mean, it's a fun toy game.

I'm not sure if it's very business
relevant, but if you want to look at your

current infrastructure could hold it.
That's an interesting one. Quantum

communication components, especially in
that part of the quantum tech world, is

more advanced and more ripe. So a lot of
devices in quantum communication you can

use now already. So it's just about your
risk appetite. Do you want to, well, spend

a lot of money on it? Do you want to
invest into it and try it out? There are

some test beds in Berlin and Paris where
they're trying out QKD networks yet.

You know, this is telecom. This is not
quantum computing, but it would be the

backbone if we want to have a quantum
internet where then again, quantum

computers are useful. So everything is
useful because it's it's an intermediate

step towards something you would like to
have. But most of the things in quantum

computers, they don't fit classical
solutions yet.

Question: All right. You talked about the
attack vectors on quantum computers and

dramatizing this a little bit. And what is
the worst case of the quantum computer

getting on?
Natalie: I mean, worst case is some

company has their sensible business data
on it, and they harvest that. I mean,

because they're not, you know, they're not
critical components as of yet. And there

are a lot of down times because they have
to recalibrate them, you know, get them

off the grid, see if the fridge works or
do some sort of maintenance. You don't

have to use usually SLS with them yet, but
think about all these companies that don't

know what they're doing, and they might
have, you know, the critical data up there

in the cloud pushing it there. And if the
API isn't, isn't hard and if it's, you

know, open access for everything, they may
just have low hanging fruit to pick out

their.
Herald: Thank you so much, Nacho. This was

tales from the quantum industry. Bye
Nacho. Thank you. Thank you. All right.

Our next talk will be at 17:30. What is
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