Quantum computing doesn't suit every

problem, right? You should have a supermassive to

work with that. It's not like that. If you can classically

solve a problem, you don't go for the quantum

computer, quantum computing and quantum

computers. Welcome to Impact

Quantum.

Quantum podcast, they're breaking the mold. Science has got

beats to fold. Hello and welcome back to Impact Quantum, where we

discuss the emerging field and entire industry that is quantum

computing. We don't need to have a PhD necessarily, although

probably helps. You just need to be curious.

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

Candace Cahootley. How's it going, Candace? It's great, Frank. How are

you today? I'm doing fantastic. I'm doing fantastic. It's,

um, we finally broke our ice, uh, below freezing,

uh, record here, and, uh, snow's actually melting.

Oh my goodness. Oh, that's just the beginning. How fantastic. It just snowed

another foot last night because, because I'm in Montreal,

so the temperature just hit freezing, and so I was like, oh, it warmed up,

and then it snowed a foot. So that's how— that's what happens here when it

warms up. But that's okay. That's okay. Today we have

a great guest. We're going to be speaking with Dr.

Helaina Bahrami, and she is a lady

of many different titles. She has her hands in a lot of different pies.

She's an AI and machine learning lead expert. She's an

innovator. She is an entrepreneur. She

works at the Oakland University of

Technology. Where she's doing postdoctoral research.

Helena, there's so much that you do, you know, I couldn't, I couldn't even sum

it up. How are you today? Hello, and

thank you very much for having me today. I'm doing very great,

especially meeting with you, very lovely

people, and I'm very excited to have our discussion.

Awesome. And you are in Auckland because I thought I

heard that as Oakland. And so this is Lord of the Rings, not

MC Hammer. Sorry, I don't want to— I don't, I don't like calling out because

I also have a New York accent. So yes, it's Auckland, New Zealand.

And as we said in the virtual green room, that it's already tomorrow where she

is, and we asked her how the future is. So

yeah, yeah, uh, please forgive my

accent, uh, so I'm not a native, but yes, you're right, I'm in

Auckland. Okay. No problem. We have accents too, just, uh,

we don't notice it. Um, so

how did you— this— you do a lot of interesting things. So, so what

exactly are you working on now that excites you at

the moment? I actually, I can divide it into

3 sectors, mainly focusing

because my main power is artificial intelligence and machine

learning. However, I'm very

interested and very

passionate about quantum mechanics, quantum physics,

and because of some personal and also

some kind of, you know,

I can say it like a mission, I'm trying to

innovate in the healthcare field to help people. So

to merge these three fields, quantum

computers, quantum computing, and also AI

and machine learning, and neuroscience to help

neurodegenerative— to help to provide some sort of solution for

neurodegenerative disease. I tried to

come up with some ideas to marry these

three disciplines. You may

heard about quantum machine learning, that it is right now a

new emerging field. So mainly it's like

that, using the power of quantum physics

and quantum computing to empower

the memory capacity and also

algorithmic power to address

or to solve an intractable problem.

When I talk about intractable problem, it

means that for classical computers, it requires

many years to solve a specific problem.

Drug discovery is amongst one of those problems that

classical computers fail to

properly address and find a solution

because of the nature of this problem, which is

combinatorial. Consider that you want to

find a drug compound. It has

a kind of different combination between molecules

and having constraints due to the

intercellular and cellular environment inside the

body. So that requires a lot of computation,

a lot of parameters to consider. Quantum

computing can provide a very

promising capacity, both in terms of

memory and computation, to address this kind

of problem. Interesting.

So where did you start? Like, how did you get into this? You went

from— because you do have a lot of things going on, and you

mentioned quantum machine learning, you mentioned quantum, you touched on

drug discovery, you also touched on

quantum biology.

Wow. That's all I got to say. I'm suitably impressed. But

how did you start in this field? Like, did you,

you know, how did you get started with quantum computing? Did

it start in— did you start in AI, ML, or did you start in

medical drug research or something else entirely?

So basically, if I wanted to give a little bit

background, I always, from early childhood, was

interested in quantum physics and

physics in general. But my path in

education education and professional life ended

into machine learning and AI, which I really love.

But for my PhD, I started

to work on a, you know, my PhD was

about a brain-like

computational model. It has a kind of 3D

structure that has

coordinates like 1,000

scaled-down coordinates of a real neuron in brain.

And this brain-like computational

model was very interesting

for me to explore, specifically with the application of

neurodegenerative disease like dementia. So when

I was doing my PhD at the early years, I

realized that there is a

computational kind of

issue for this huge structure. And when I

was doing some research, and because I was

interested in the quantum physics and quantum computing,

I thought, why not using the benefit

of both memory and computation

of the quantum physics and quantum mechanics?

However, it was at the beginning, it was like trying

to getting inspiration from quantum

mechanics, and I tried to improve that

framework by trying to

use concept of quantum mechanics and quantum physics.

At the moment, what I'm doing, I'm trying to

convert that concepts to a physical

circuit, adapting to the physical circuits of quantum

computers. So it is not just because when we talk about

getting inspiration, especially in the AI machine

learning. It means that we simplified some

of the concept and we tried to

benefit from the general idea. But right now I'm trying

with the help of more knowledge that I acquire

around quantum machine learning, I'm trying to converting

those models to quantum

circuits to be adaptable to

an algorithm that is compatible with quantum

computers. And because of,

you know, when you are starting to solving a problem,

specifically in the area of health, you

realize that it is not just one single

angle to look at. When I was

Starting my journey, I wanted to diagnose, I

wanted to predict the risk of getting

dementia, different type of dementia actually, like

Alzheimer's disease, frontotemporal

dementia, Lewy body dementia, all those categories

that the brain, the neuron are getting

affected by aging and environmental factors.

Then I realized that, okay, we

understand and we predict it. What's the next step?

So for the next step, it requires a treatment

plan. For the treatment plan, we need to look

at the medicine and precision medicine,

if I want to be exact, so that we can help a

person according to their kind of

biological signature instead of providing one

solution that one size fits all. So it was

the beginning of my journey and how it evolves to—

and I can say that although it's very interesting, but

the more I acquire knowledge,

the more I understand that I'm still in the beginning of the

way and there are a lot to learn and to work on.

You know, I'm a little curious to the culture that's going on

where you are. Do you find— are there a lot are there a lot of

women in the room with you? Are you one of few?

How is the gender, how does gender play

out in quantum for you?

It's, if you ask me like

maybe 5 years ago or maybe 10 years

ago, I would say that it is obviously

very dominant by male, not

female. But recently I can see a lot of

female are entering in these

fields, machine learning, computer science, data science, and

also physics. I know myself a lot of

very intelligent ladies that are working

in the photonic physics

and quantum mechanics. And also

it's still kind of, it's

proportional to, you know, it's more oriented

towards or more kind of

dominated by male. But I think that

little by little the balance is happening.

I don't generally think female don't

have capacities, but culture and

also the passion. So one

angle is that the society that you are living in,

what they are promoting for you as a lady. They promote you

to be going in a kind of softer

areas of science. But some

research, I think that I recently heard

that there was a recent research around the mathematical

ability of female

and male. And it was like that in our

countries, like I think that it was one of the Scandinavian

countries that female were

outcompeting male in terms of mathematical kind of

ability and power. And it says a lot. It means that

not, not, it is not something genetic, something related to your

gender. It's just how you are

cultivated, what, what, uh, the opportunity that your,

your country, your, uh, culture, your society provides for you.

Makes a lot of sense. I always thought that that was more less biology and

more sociology. Yeah, I agree.

You know, you mentioned quantum biology and Frank knows I

love everything quantum biology. Like it's a, it's a little bit, a

little bit of an issue for me now, but like I wanted to talk a

little bit of your, of your brain is a computational model.

And I, I wondered if— do you

see there to be meaningful parallels between

quantum systems and how the brain handles

things like uncertainty or ambiguity or

probability? Part of actually my,

my thesis, because, you know, my, uh,

The work that I've done in my PhD, it was around

building a computational model that

resembles biological brain. And at that

time, I read an article from Sir

Penrose. He's a, I think,

Nobel laureate for, I

think, quantum physics. And

he suggested that maybe consciousness

and the way that brain thinks can be

modeled by the quantum

concepts, quantum mechanics concepts. So, and they

tried with his colleague, tried to go to microtubule

to say that, yeah, there might be some

kind of quantum mechanical thing happening

there. But I want to answer you in this way.

Quantum mechanics is the fundamental rules of our

physical world. So our

brain is,

includes molecules, cells,

molecules have atoms, atoms have subatomic kind

of domain. And when we go from the

quantum realm to the physical or classical realm,

because of confinement with

many bodies, many other kind

of environmental association with other other

molecules, other atoms, the

quantum nature faded. Doesn't

go away, it faded. And it's like that you have a general rule in

the quantum realm. When you go to the classical realm,

it becomes more kind of a special case. So basically,

it is true to say that it is governed by quantum mechanical

rule. However, I, because I'm

very interested in consciousness and I tried to with a

very— at that time, I was a little bit ignorant. I thought that

we can computationally model consciousness

while we don't know what consciousness actually is.

So I think

we can't model consciousness at this

scale because it

requires not just considering the brain as an

individual physical system. Our

brain doesn't evolve just by

isolation, right? So you learn by having

social connection with others. Your

mind, your consciousness, shaped by

environments, by communication with

other individuals or other entities in

the world. So it is not an isolated model that you can

provide a computational, you know, mathematical model to

say that, yeah, consciousness arise from this physical

neuronal activity. If we want to truly model such a

thing, we need to model it

within this complex psychosocial

association with others, with the nature, with the

environment. And that is the correct way to

look at. And I think that But right now we don't have the capacity

in terms of computation to test

such an idea.

Wow, that's a lot. I mean, that's a lot to take in.

But you're right. Like, you know, if you want to use

AI terms for this, right, humans are not

neural networks that exist in isolation. They interact with

other neural networks. Um, yeah, I

mean, yeah, there's a lot, there's a lot to unpack there, and I just can't

imagine what the computational power to simulate that would be.

It's probably beyond, beyond our company, beyond my

comprehension, that's for sure. Oh, go ahead.

Yeah, yeah, if we want, because, you know, there was an attempt, I think

that's 4 years ago, to build a

spiking neural network like computers

that can handle, like we have

83 billion neurons in our brain and

consider that the connectivity goes to the roof.

So they tried to build such a machine. Even

such a machine requires a lot of

fine-tuning, a lot of energy to make

it run again. Still, there is, I was

looking at the success and/or

news about how they go on with that idea. Idea.

But you're right, the problem is the idea might be there,

but right now we are limited with the

maybe power or at some extent

knowledge of how to build or

manipulate those information. Also too,

maybe this will be resolved at some point in the future. I mean, I'm still

amazed at the fact that you're on the other side of the planet and we're

having a real real-time video conversation. Like, that wasn't that long ago that was

impossible to, impractical to, now it's an everyday occurrence.

I think it's fantastic having 3 separate countries on the call right now. It's not

even, it's not even an issue, right? That's fantastic.

So when you speak to organizations about quantum, what's the biggest

misconception you still hear?

One thing that is sad because

last year I attended a conference and I

was telling that because my work mainly

has a research nature in it. It's not like that

there is a solution ready, I'm just trying to build an

app around that. I was asking

that question, How I can build some

sort of research group that people

can help me to build this idea,

especially in drug discovery. You know, in drug discovery,

we have the problem of a huge

database of molecules and information

related to drug compounds. We just

scratched the surface, like 10%, and from that

10%, it takes like, uh, 10 to

15 years, or sometimes 20 years, to

do some sort of research on what compound

suits for this specific disease. And after that, going

to virtual screening, then going to test, uh, phases,

and going to regulation phases, then to, uh, release

to the market. So it is, first of all, a long, uh, kind of,

uh, period of time. And

also it costs a lot of money, billions and billions

of dollars. It's not like that if you fail the

first phase of research, it's just, just maybe a

couple of thousands of dollars. It's billions of billions of dollars.

And 90% of that's considered that I

mentioned, 10% of those information we are looking

at. 90% of those trials

fail at the earliest stage. Some of them fail

at the final stage. It means that you already

invest a lot of money during this research stage.

Then there is a potential. Quantum

computing can provide a potential to

solve a problem that a classical machine

can solve like in 300 years. Within

a couple of hours. Even though we are

in the noisy intermediate-scale quantum

era, it can be handled. Right now, I

think that quantum—

I think that it was Atom Quantum. Yes, they

propose quantum computing that can handle more than

1,000 qubits. When we

say 1,000 qubits, maybe it's like not

big enough for handling information. But if

we add the quantum physical,

quantum mechanical concept to that, it provides

us a huge space to store

and compute information, process

information, so that can help to reduce

the time and also

save some budget. If you

fail earlier, you can save a lot of budget. But the

problem is, first of all,

quantum computing doesn't suit every

problem. You should have a supermassive

to work with that. It's not

like that. If you can classically solve a

problem, you don't go for the quantum computer,

quantum computing and quantum computers, because

there are two angles. One of them requires a

specialist. It's a cost to build

such an algorithm. Those algorithms are a little

bit complicated. You need to both know

quantum mechanics rules. You need to know computer

science, and also if it is like

information technology, if you are working with some piece of

information. And the other fact is that

how many— because

right now there are a lot of companies that are using, especially

pharmaceutical companies, or this

cryptographies that they try to use quantum

computers, but it is not widely accepted, uh,

again due to the lack of, uh, experts in

this field. And also, uh,

quantum computers are expensive, not, uh, easily accessible.

Um, uh, although there are, uh, Google and

IBM and also, uh, another major

player that provides cloud quantum

computers, it still, it is not widely used. So

when I talk about, let's move to quantum

solution, there are 3 major problem.

One of them, can you find

the expert for me that can do that? Expert are

rare right now, but it is growing field.

How much should I pay for this kind of

supercomputers or computers, quantum computers?

And besides of the business value that you need

to convince. And also,

if I solve it with the classical computers, why should I

bother to go? And there is no kind of

justification to go there. But yes, it's

like it was difficult, and I, I thought that

possibly, uh, it requires

more kind of, uh, like

podcast that you are providing, more kind of, um,

public awareness in terms of technology and the

capability so that business owners are ready to

move. But again, it's, it's,

it's, it's, whenever we try to

provide a solution, we always say that

quantum supremacy over classical computers.

That's the most important part. Okay,

so what part of drug discovery, what part of the drug

discovery pipeline do you think quantum will impact first?

Target identification, molecular modeling,

or optimization? I think that all of the

areas. So the nature of this— then if

you look at the problem, the nature of the problem is quantum mechanical.

You are looking at, uh,

molecules, how they bind together, how their,

uh, subatomic level, um, tries

to provide the capacity of binding with, uh, between the

drug itself and also the

drug compound and target protein in terms of my, my

own research area, which mostly is

the protein and amino acid inside the brain.

So the nature is quantum mechanical. It's all

based quantum chemistry, quantum thermodynamics,

quantum mechanics. And from the

optimization, again, this drug discovery problem

is not, this is the place that

quantum supremacy plays an important role. You cannot

solve this problem with

classical computers. As I mentioned,

it is a combinatorial problem. So

you may need maybe 100 years to

look at not even on a specific

combination, of the drug molecules.

And also for the optimization,

we have quantum optimization that they can

find the equilibrium or the

lowest energy point for the

structure of the compound. Because it's not just, you know,

connecting atoms or molecules to build a new compound. You need

to look at the structure,

and it can define a lot about the

hydrophilic, hydrophobic, and

the kind of characteristic that can

contribute that molecules. Again,

it's because I'm more kind of

expert in my own area. That molecules can

pass the broad brain barrier

and reach to the specific

protein and try to, you know, solve

the issue. And besides of that, this

optimization is not just about the

constraints that we see in the

molecule compound physical shape. So

consider that Specifically for the drug that

requires to go through brain. Brain has a

kind of protective lattice. It's called a

blood-brain barrier. It doesn't allow everything passes through.

It has a filter. So first of all, you need to be

successfully passed through this barrier. And

also drugs are not just a kind of,

you know, a final solution solves the problem, right? They

have toxicity. They can alter and change the

cellular environment. They can help, but

there are always side effects as well. How to

increase the benefit and decrease

the kind of harm

that the drug can cause to the body is important. All of

those parameters It can create a

huge problem space that cannot

easily solved by classical computers.

Quantum computers can provide a

very big space. You know, theoretically, if you

heard about Hilbert space, if you heard about a

kind of multidimensional space that you can look at

possibilities and try to find

the best solution

quantum computers has. I think that ultimate

solution for this specific problem.

Interesting. I would say

anybody listening for at least 5 minutes understands how incredibly

brilliant you are. Yeah. Yeah. I mean, it won't take 5 minutes. Kind

of. I know, right? It'll take like exactly 30 seconds. So

you also are an entrepreneur. So

what kind of leadership mindset does quantum

demand? One of them is that

to be open to failure. It's not like that, you know,

every attempt is successful. The other thing

is that you need to— if

first of all, quantum mechanics is peculiar in

nature. Right? It's like different than

the classical tangible

environment that you're facing.

The other thing is that you need to be patient. The field is

growing, but slowly growing. It's not like that. So

when I want to write an algorithm for classical machine,

everything is already tested.

There are a little bit innovation,

although we saw a big

leap in 2017 with language models,

large language models. But anyway, it's like that

gradually adding to the already built-in

foundation. For the quantum mechanics and quantum

computers, especially quantum machine learning, it's still

new. 2019 quantum

computers being used, like,

widely accepted and used for research field. And

right now we are entering the era that

pharmaceutical companies and also finance

and also

companies that working on creating new

materials, new Finding new

compounds for physical materials. Consider a

scenario that you are able to

use this quantum computing power

to build a material that responds

to the environment, right? If you

learn the property of the

material, the molecule, you can build at

nanoscale, build a kind of material that can

respond to the environment, a physical material. It can change

the architecture and construction

science. And also what I'm working

right now, as I mentioned, it was part of the drug

discovery. I'm working on

building a kind of physical lattice

to play the role of blood-brain

barrier so that when we are doing some

drug virtual screening, before we go to the animal

test and, you know, human test or using

other models, we have a physical kind of

model that can show if this drug can have

ability to have

ability to pass this barrier. So generally,

it's like that. First of all, I

think being open to failure and know that the field is

growing slowly right now, but you

never know. There might be, once the

full error-tolerant quantum machines are

available, I think that it will be a huge

thing for humanity because it gives us a

full kind of power to use this

massive memory and also computation.

Generally, I think another

important aspect is that

we shouldn't think of science as silos.

One thing is that quantum computers quantum

computing, and also

machine learning. If you look at it as

silos, they can do a little,

but when you try to look at this as part of a big

picture, you can solve many problems

by just combining different angles of

this physical world together.

You know, one of my favorite TV shows of all time was this UK science

show called, um, Connections.

And it's really old, like it was old when I

saw it in the '90s, I think. But basically it shows kind of historically

how, um, things we take for granted today have a

connection, right? Hence the name, right? So one of them was the,

um, perfume from the gasoline spray,

this is like the 1700s, 1800s, to the carburetor,

right? Because basically you had to atomize the gasoline, right? So kind of like, and

how historically these people interacted

with one another from different disciplines, right? And I think that was, I

haven't seen this in a very long time, but that was

basically kind of the gist of the atomizer connection to the carburetor.

Was, you know, people were tinkering around with this, and

then they were— somebody was at a party, and I guess they

were selling perfumes, and he saw them pump the thing and spray

it, and he's like, that's it, you know, kind of like little

moments like that. That's cross-pollination is always fun like that, you know.

No, absolutely. So, hmm, so what kind of

advice would you give someone who's mid-career but

wants to pivot into quantum?

I think last year I had a talk at university.

One of the students said that because they were

doing quantum mechanics and quantum computing courses,

said that what is the future? What is the current market for

us if we want to enter? I think that one

important aspect is you need to

be, first of all, passionate about the field that you

are entering. If it is your passion and there is no

kind of capacity at the market at the moment,

you will make that passion

by thinking of using it in

other forms, not just directly, you know, going

to a kind of building infrastructure for

the whole pipeline of the quantum computers

and quantum mechanics. But the thing is that

if I want to make an analogy, when I

started my career at artificial intelligence, I

was in my own country and it was like 20 years

ago, it was at the beginning of

artificial intelligence. There wasn't

many places that use, actually no places, no company

or ordinary company were using

artificial intelligence. And I remember that my mother said that,

change your path to something that can

be used. You cannot find a job. And I said that,

no, I like this one. So Little by little,

by finding research institute that are working there,

I start to expand my knowledge work. And then

there was a time that right now every

place demands for AI machine learning

skills. So I think that first is that

make sure that this is your path. If you like it,

you can find Although at the moment it's not

too many, you can find places that you can

use your knowledge, but be aware of the

growth of this field. There is a promise of

within 10 to 15 years

fully tolerant quantum computers will be

available. And we are right now, we

achieved 1,000 qubits.

And when I'm saying about qubits

and these strange things,

mainly I'm emphasizing on the

capacity of storing knowledge, storing

information, processing them, and

extracting knowledge. So I think the

main you know, advice

that I can give to people is that

if it is your passion, stick to that. And you can

either by going to research

fields, research companies, you can,

you know, add more to your knowledge, help to build more

kind of steps to this ladder.

And don't be kind of—

don't feel like that if this is my

area, my field, there is not enough

places to work in. Just wait. It's like that

maybe within— even though maybe earlier, there

will be huge demand of quantum

computing skills and also quantum

machine learning. Alongside with

the quantum mechanical skills for building

such a machine. So for

quantum computers, we are not just,

you know, we need to combine 3 different areas,

quantum physics, nanotechnology, and also

computer science to build such a

beautiful, powerful machine, upon that we need to

have skills about quantum

information technology that by itself

categorizes into different kind of

classes, quantum computing,

quantum communication, and also

quantum sensing. So it's not just one field, it has a broad

use it. I think that mainly is,

uh, being patient and finding the correct

institution or a place to, to start with.

That's a good way

to put it. That's, um, definitely would love to have you

back on the show because there's a lot to unpack. We're going to need more

than an hour, uh, to, to go through it

because you have, you have your— you're one of the, I think, few people that

we've spoken to that has a very unique perspective on multiple aspects of

how quantum computing can assist in both medicine,

AI, and biology. I think it's

an interesting— it's an interesting take. And

I think you have a very unique perspective on this.

Thank you. Yeah, I'm

just— I'm just unpacking this. There's just a lot. So,

and I also like your idea that, you know, and you stuck with AI

way before AI was cool. You said 20 years ago, and I,

10 years ago, I made the decision to switch into AI. People thought I was

crazy, right? Yes, yes, I

completely, you know, I was, when, when

this AI machine learning hype

happened, a lot of people,

they jump to AI machine learning.

And I remember the time, and one of my friends said

that, you know, from

my own country, you know that everything has been changed. You

can find a lot of jobs here because when we started

together, it was very little places. And I said that, yes, I can

see everywhere in the world right now.

Market's demand for that. But at that time, it was like that.

It's like a kind of

something that you cannot use. It is just for research,

for libraries, but very, very

niche, very niche type of situations. Airline seat

optimization or logistic optimization wasn't

really mainstream. It was just starting to become— well,

20 years ago, it was a different world. But I mean, 10 years

ago, I think people started realizing like, hey, we have all this data and we

can start doing something. Now, the hype is very real

and everybody's an AI expert now. But I think

quantum is very much the same thing. You can validate this by looking for jobs.

There's not a lot of jobs, but there is a lot

more this year than there was last year. I suspect that there's going to be

more next year than this year. I think people forget,

people always look at the hockey stick graphic or an exponential curve, Exponential

curves during the first iteration or two

don't really grow much, and then suddenly they explode. Yes,

I totally agree. And the other aspect is that

the success of AI is

because of the growth of GPUs. So it was like

both Internet of Things, GPUs, the

computational power, and the data. When you have the Internet of Things,

we could sense the world and can

capture a lot of information. Social media, all of those things

create the opportunity for AI and machine learning. Right now

we have the same scenario. We

are advancing in nanotechnology. We are advancing

in error correction for the

quantum computing. So I think that you're

very Correct. And I think it's not linear

like AI. There will be soon a

huge jump to having very

capable quantum machines and quantum

computers that can help us to change the worldview

even much more dramatically.

Absolutely. So where can— I'm sorry, go ahead, Gannis. No, I

was going to ask, you know, If you could give a piece of advice to

leaders who feel that they're already late to quantum,

what would you tell them right now? Again,

it goes back to if the area

that they work requires quantum, even to consider that

we entered the area of

quantum, full error tolerance

quantum computers. If it makes sense, if

quantum provides a supermassive, if it

provides a kind of solution that benefits the

company, they need to

start looking for the option. So first of all, they need

to understand the problem. If the problem has the nature to

move on to this kind of

computational sector, I think

First of all, investing even in small amounts. Every

company, if they want to grow, they have a

budget for R&D. So they can

invest in the future, for the future in this

field. If their problem can be

solved by quantum computers, again, it's

like understanding of the nature of the quantum and

understanding the capacity and

capability of these powerful machines.

It's not like that everybody should jump to the, like,

AI. Maybe sometime in future comes, but at the moment,

consider that quantum computers,

although they promise to save energy,

but it's, if for a small

problem in compared to the classical machine,

it uses much more energy. So the problem should be

huge enough so that you can

benefit from the computational power of the quantum

mechanics and quantum computers, sorry. But generally, I

think that investment doesn't

harm in terms of R&D, research and development,

and also learning the

quantum mechanics and quantum computers

can be a little bit challenging at the

beginning, but it is like, as you say, it's

like the curve is like at the beginning is very slow, but when

you reach a certain point, it's very easy. It becomes

natural to you. That's

fantastic. That's good. So I think I cut off Frank, who was going to ask

you, so if people want to find out more,

about what you do, to follow and ask questions,

where would we send them to?

Both my company's

website and also my LinkedIn. I can—

daily I receive a lot of messages and I answer some.

Sometimes you get some interesting ideas, you get some

very passionate collaborators. Both my

heliumai.co.nz and

also my LinkedIn is

a good channel to connect and communicate.

Awesome. Fantastic. Fantastic.

And with that, we'll play the outro music.

They're breaking the mold. Science and sky pizza,

bold and it's

gold.

The multiverse is skanking, skanking in time. Black holes are

wailing in a horn line so fine. From plank scales to planets, they're

connecting the dots. Kenneth and Frank, they're the cosmic

hotshot.

Quantum Podcast, turn it up fast. Kenneth 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.