So that the copilot will take the entire protein sequence, map

it to a quantum circuit and then run and give you back the results.

So this, we did it within six

minutes. You can do a protein simulation on

a quantum computer or a quantum simulator under six minutes. When

breakthroughs move that fast, the future stops being theoretical and

starts getting impactful. This is Impact Quantum

podcast. Turn it up fast, Kenneth. Blowing my

mind at last. Hello and welcome

back to Impact Quantum the podcast. We explore the emerging field

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

a quantum physicist or really know anything at

all. You just need to be a little bit curious and a little bit self

driven. And again, the most quantum curious and self driven

person I know is Candace Gooley. How's it going, Candace? It's great. Thank

you, Frank. I have to say though, today is exceptionally cold. Everyone's going

through this like Arctic, this like Arctic temperatures. And even

us here in Montreal, Quebec, we are shivering. It is

really crazy cold. It's not much warmer

down here in Maryland. It's very cold.

This is usually January, February weather,

not pre Christmas weather. That's right. I

guess the Arctic blasts are going to do their thing.

Yes, yes. So today we're going to be speaking with

Adhisha Gamanpila and

he is the CEO and founder of

Feynman and we're really excited to talk to him today.

He's a lot of good things. He's a lot of really interesting talents and

I can't wait to dig in a little farther. Awesome. Now is it Feynman or

is it Feynman? I thought it was like named after Richard Feynman. It's Feynman.

Right? Okay, cool. Just check, just check it out.

That's okay. So he's checking on me, making sure. No worries. I

got your back, Candace. There we go. There we go.

Welcome to the show. So obviously you named your company, your company's named

after Richard Feynman. So I'm going to go out on the limb here and say

it has something to do with physics.

So who are you and what do you do? Absolutely. So I can

decide. Frank, it's so good to be here. And I think the climate

is changing a lot and that could also be a good

topic for us to speak about today. So I'm

Adisha, founder and CEO of Feynman, starting from Richard

Feynman, just like Frank mentioned. So pretty much

what we do is we help

anyone, even without zero quantum knowledge to use quantum

computers. So you don't have to have a Ph.D. you don't have to go through

the deep mathematics to to experience the power of quantum.

So make we make quantum easy to use. So

that's what we do at Feynman and we want to

put our tooling in front of every quantum computer that's out there

so that anyone could easily make use of quantum. So that's what we

are doing. Frank and Candice love to have a deeper discussion around

it and super excited. Well, let's just

go ahead Candice. Sorry. Before we dig deep I wanted to

take a little step back because I really like to understand where

people like they come to the information. So when you were

back and you were back at university, were you already into

physics? Were you computer science? Were you something else? What brought

you Tell us a little bit about your path. Absolutely,

absolutely. So I studied computer science along

with physics, the classical side of physics

as well as quantum side of physics. And

I wanted to pursue my higher studies in theoretical physics

study quantum physics. So while studying computer

science I wanted to do a practical application

with computer science and theoretical physics. That is somewhere around

2017 that's when IBM

also started giving out cloud access to quantum computers.

Then I wanted to run a

practical application on a Quantum computer in 2017.

So I did not realize how difficult it was back then. So I

took a paper some which is wrote which was written somewhere on 2000

early 2000 theoretical mathematical paper and I tried

to run it on IBM quantum computer. The running part was around

few hours but preparing to run it was like six months of work.

The transformation of transformation of the

classical models to quantum and then the learning curve mapping the

data set to the quantum circuits and

it was, it was really hard and it practically did not

run the larger use case but for a smaller use case

of a similar fashion was possible.

That gave me the understanding how difficult it is in a first hand

experience. And then I was also doing courses at MIT

via online. Then Covid came and I published my paper

and then a lot of reviews came across from the student community, research community.

How did you do it? So

that was a moment of realization to understand, okay, there

are people who need quantum who want to run quantum but they're

struggling and they give up on the idea of going quantum. And

there are engineers who wants to try quantum but they're interested in

results so they need quick access they not to go through all the science

behind it. So I started seeing all these variations

and last year we brought out our platform

with the APIs, the copilot where we have the learning

modules to build out this entire quantum

ecosystem. So that's how we got it into play.

Candice and Frank. Interesting.

That's a good point because there is that moment of my personal

first exposure to quantum computing was I was at a Microsoft

conference and I was so excited about this. I went

back to the hotel that night and I installed Q Sharp. And then I

realized what now I felt like I had

got a glimpse of this wonderful world, but then

I had, I suppose the tools. But like I felt like a caveman

banging, you know, banging with rocks, you know, like I held

this massive wall of like, okay, now what? Right? And I think ever since

then I've always been like, okay, now what? You know,

and I'm curious to see how your tools kind of address that. Right. Because

you know, I would imagine that I'm not the only

one that's going to, you know, has already. Has already had that experience or will

in the near future.

Absolutely. So that feeling is

feeling of, okay, you run your first single

qubit or one or two qubit. Okay, you run

it on a simulator or a quantum computer and you get the results.

You get 100 shots and you put these gates and okay, you

really come to a moment, okay, now what, what does

this really mean? What can I do with it?

So that is where the practicality of using quantum comes in.

Because if you really look at it, we are using gates,

we call it Hadamard gate and different other gates. In a classical

world we have similar gates like the N gate, the not gate.

You can't write a software platform with these gates combined.

You can't bring all these gates together and write it. That's

impossible. Theoretically you could, but no one will do it.

But theoretically possible. But when I talk

to folks about kind of these new gates and you know,

the I guess and an OR logic you do deal with in

programming on a regular basis, but the exclusive or like the X

naught like these are not things you normally normal day to day

enterprise or people in career would do. It's usually like you

learn it in your first semester of computer science and you never

hear of it again unless you do some kind of weird research.

Exactly. But those become the fundamental building

blocks. But there's so much of abstraction built

on top of it

over the years in the classical world where the programming frameworks like as

we don't have to talk about these gates anymore now what

we do is we build that abstraction through our

copilot

with the world with AI transformations coming in co Pilots coming in to

every avenue of things we do, from research to web development

to application. So we build this quantum computing

copilot where you as a scientist could put

your requirements in natural language. Let's say I'm a climate

scientist, I insert my data set

and I tell, hey, I want to run this, I want

to do this prediction. You click

enter, that's it. So the Agentix system takes out the requirement,

splits the requirement, looks at the data set, creates

the quantum circuit, runs it or creates a code,

creates the circuit, run it on a quantum computer or a

simulator, gives you back the results and then you can tell, hey,

okay, can we adjust this space and run back and

say and see how the results are going to change so

that, that, that is how we are solving this. We have created

this environment where you could tell your requirement and the agent system

will generate the code, run it on a simulator quantum computer and

give you back the results. So here you don't have to worry about

how quantum computers work. What is the architecture of the quantum computer, what is

the language, whether it's going to be Q Shop,

Qiskit or Open

Chasm. So you don't have to worry about it. You just need to pick the

infrastructure and just run it and get your results. You need to

know your subject matter only if you're into finance, no problem. You know your

finances, you, if you're into climate, you

have your subject matter around that. A great example,

a great example is that we integrated our platform

with Google with AlphaFold.

AlphaFold is a global database of proteins

and you just have to call

out which protein you need and then tell the requirements so that the

copilot will take the entire protein sequence, map it to

a quantum circuit and then run and give you back the results.

So this, we did it within six

minutes. You can do a protein simulation on

a quantum computer or a quantum simulator under six minutes

without tooling, without it, it's just months of work. I actually

did it by, in a airport during a transit. That is how we released this

feature to the platform. I was testing it with my team while I was

traveling and it worked and we pushed it. And it's

imagine you trying to map out a protein to a quantum circuit. It's

unimaginable. So this just six minutes.

I understand that most of you, your focus seems to be on

climate modeling. And so what

do you see as the strongest near term opportunity

that we're going to see in quantum and climate

modeling? Absolutely, absolutely.

Now, right now, if you look at the market,

probably let's say the European market, for example, we see a lot

of pharmaceutical companies are doing a lot of

research or research around quantum chemistry,

how molecules interact and in terms

of drug discovery, likewise. So fundamentally it's

around chemistry, I would say. So there are a lot of chemistry use

cases and these chemistry use cases also spans

into material discovery, sustainability, sustainable

materials, fertilizer research, which will impact on,

let's say ammonia production for climate,

likewise. But fundamentally chemistry has become a major use case for

R and D which will impact on

climate as well as other similar avenues like drug discovery,

likewise. And even like sustainable paints,

sustainable material for flights. So these are like very popular

use cases that are being industrially research,

not academically industrially researched.

And even recently I have seen content coming out

from World Economic Forum, Bloomberg, how these enterprises

have actually have

experience to a certain degree of

quantum advantage, like the signals of that. So

those are the real world use cases that are happening at the moment.

On the flip side, when it comes to

cyber security, that is on the quantum safety side,

the entire world is preparing when the hardware maturity comes in,

how our infrastructure will be secured. So the, the quantum

cryptography part is already there.

Nationwide, the preparations are taking place. Enterprise wide it's

taking place, but it's on another avenue of

quantum. But on the quantum computing field, quantum chemistry, climate

modeling, drug discovery, those are very, very popular use

cases that's happening out there. Yeah,

no, I mean that's a good way to put it. Right. Um, there's a

lot chemistry is going to obviously kind of the, the quantum

encryption aspect is, is top of mind for a lot of people

for good reason. But also the whole notion of the chemistry,

how this is going to revolutionize chemistry, medicine, material

science, better sustainable

products. Yeah. So

would it be fair to like give an elevator pitch if I had to give

an elevator pitch for your product that this is kind of like quantum Vibe co.

Or is that. You can say that. You can say that. Okay,

interesting. In a very, very generalized sense. So

when it comes to white coding, the thing is like for a

Vibe code, like if we have around 100 plus

scientists on our platform right now, pretty

much in the Europe, UK and US and they're

pretty much PhD students of

postdoctoral candidates, researchers. Right. So

it's very hard to imagine it's Vibe coding

because the experience that we see on the platform is they actually know

quantum to a great extent. It's a matter of saving time for

them and even picking hardware because the platform does it.

But in a very, very general sense, it's more of like a wide coding platform,

but it's,

we see different behaviors coming in with our different

user groups. It sometimes operates as a research assistant for them to

prototype certain research ideas faster before going and using it on

a supercomputer. We see that behavior coming and from

industrial users we see it more of like, just like you said, a wide coding

platform. They don't care about how the nitty gritty is bug. They need

results. So they want to prototype and see so

different sides of it. But yeah.

But I also like the fact that it abstracts away a lot of the harder

aspects of the quantum gates

and the quantum software creation. Right. I think

subject matter experts are still going to be important. Obviously your

company is probably filled with physicists, people who are experts in how these gates

go. So it's not just like your average vibe coding tool

that everybody and their cousin and their cousin's dog has out now, right?

Yeah, no, that's fascinating. I think, I think that'll, that'll help ease

the transition for a lot of folks into quantum kind of

the quantum space. Absolutely. And just to add to it,

Frank, we recently published a paper

where we compared our agentic system against

popular LLMs from Claude to Gemini to

OpenAI and we compared how the code was

generated, the quality of the code, like when our system

was completely outperforming the classical or the traditional

models. And Even with

Qiskit V2, most of these platforms are

popularly available. LLMs are not generating the latest code

or sometimes the code is wrong. So the

agentic system we have built with our fine tuned models or the

optimized model for quantum is directly outperforming

those cases. So that's where our value proposition really

comes in. Because existing models out there are not serving the community,

they have to keep on trying. Sometimes it does not work and sometimes it takes

the requirement in a wrong way. And then

these are like very

scientifically intensive things. Right. So the,

the, the model should be very much fine

tuned for that subject matter. So that is how

we have handled it as a separate proprietary

model of ours. So how do

you evaluate whether a problem is quantum ready

versus still better solve classically or even with

hybrid meth? Absolutely, absolutely. So

that's, that's actually where most of our scientists are engaged

with us in terms of breaking it. And there are different frameworks

to it, but in a, in a theoretical

point of view we have the NP hard problems, the ones that we can be

classical solvable, the ones that are difficult to

solve likewise. So

now if you look at the it actually goes to the

point of how we map this optimization problem

to like, for example, how we map this optimization problem,

whether it's classically solvable or it's. It's not. So

there are certain theoretical frameworks that are used in math and

likewise. So we try to map it to, against that

and then take out the ones that should go onto quantum and then

we take that part and then figure out which

quantum algorithm will best serve for this. It could

be like a variational quantum algorithm or

likewise. So that splitting takes place and that is where

more and more effort is. We also put with our scientific teams,

the advisors to better architect

splitting this. Now, in an industrial standpoint,

we call this quantum centric supercomputing, where 95

of the load is run on CPUs and GPUs and

the last 5% is run on a quantum computer. So I think

this word was even coined like one year ago, I suppose. So

it's a very, very novel area. So even cracking that, how to

which to go to quantum, it's not to go to quantum or classical. So it's

heavily scientific. But getting the agents to do it is

much more difficult. So that's, that's how we are trying to crack it.

Candice. I mean

earlier you mentioned ammonia production and

carbon capture. I believe so.

Do you think that quantum

simulations for catalysts like ammonia production or

carbon capture are closer to

feasibility than pharmaceutical modeling?

Okay,

it's, it's a little bit difficult for me to say which is closer. It

depends on how the scientists are doing it. Likewise.

But if you look at fundamentally why we

use quantum. Just quoting from Richard Feynman, that is

to simulate nature. And.

I, I even read this article on McKinsey, like how they have mentioned this.

If you look at nature, ammonia is produced through

microorganisms, through enzymes, without a very

complicated Haber Bosch process that's completed through chemical

reactions. How does that chemical reaction happens?

Still, it's not computable. And for

that only quantum is coming in to simulate nature. That's what Richard

Feynman said. So

the purpose of existence of quantum is to simulate nature.

A really great way to put it. Sorry, I

really liked how you put it that way. I'm sorry, go ahead. No, so,

so I think when it comes to medical

research, pharmaceuticals, it's fundamentally, it's again chemistry, right?

How the biological systems are operating.

So fundamentally it's just, it's, it's again the chemistry, right?

It's how, how the electrons are reacting, how the protons are the

the, the, the elements are reacting at a fundamental level.

So that's why I was looking at chemistry at a root level rather

than the application lay.

I think

this is my gut feel. I'm not sure. I think

there would be more

medical use cases coming in

compared to fertilizer research,

I think because of the commercialization and the intensive research funding

that's happening on that side. But it's

just a point of view. Yeah.

Yeah. I mean that's a big part of energy

production today is that it is creating fertilizer

for. Through the Haber process, which is named after some

German guy who figured out how to make

ammonia, which is crucial for basically any,

for a lot of industrial processes, but namely fertilizer.

But to your point, microbes can do it, right? Microbes don't

need an entire, don't need a lot of energy, yet they're

able to do it right. And there's just a lot that

we can emulate nature, but there's a lot we don't understand

in terms of how it just does it so efficiently. And this goes even to

our brains, right? In the virtual green room. We're talking about AI and my quote

unquote day job in AI. Right. And I was joking about

how I like to keep my office warm with this.

But all of our, the human brain consumes something like 25 watts

of power and it's able to do

what, you know, as of today, we'll

see the Deep Sea papers tend to come out around this time of

year. So we'll see. But what conventional hardware

and conventional AI researchers have not yet been able to duplicate,

which would be effectively AGI. Right, but

with data centers and data centers and nuclear power plants. Right. Yet we're

able to do it with a monster energy drink

and

a candy bar. And even then that's not really

good for you, Right? Exactly, exactly.

So when you're evaluating other climate tech

startups in the ecosystem, what signals

tell you that they're building something real.

When it comes to.

No, the climate climate

tech space is

huge. Like let's say data from platforms which

tracks, let's say

ESG metrics to where

they do very research

oriented products as well. So it's a, it's a,

it's a very broad spectrum. Now if you

ask me, when you ask me the question, how do you assess whether they are

doing something real? So

they do it because they believe it. Right, so and they do it

because they believe it. And even if it does not sound

realistic and

it's not my call to make whether it's, it's going to be real because, because

of the people who thought it's going to be real. Only certain things happen in

world. Even, even from the

computing to every other technology or the scientific discoveries that

we see out there in the world. Right? So it's. So when I

see a company who is doing something

like extremely challenging, I tend to get excited

and I put my whole heart and soul to wish them good luck

to make it happen. Because personally, deep down, like for example,

quantum for me, quantum computing or the co pilot that we do is it.

It's actually the starting point. So we have a big vision with what

we want to do in terms of energy to energy

teleportation with computing over the next 20, 30 years. Like let's say there are

concepts called energy, quantum energy teleportation,

which is actually done on a very, very nano level where you could

transmit energy from one

place to another at a quantum mechanical way. Now

imagine that you can do this at a atmospheric wave, like let's see, on the

space you can get sunlight and quantum energy

teleport to the ground station without

any loss. And that energy intensity would be extremely

high. So those kind of things

are like extremely theoretical, but which

we tend to do. So a person like me, when I see something

extremely rare to occur, I really

get excited. So I always

wish them good luck to make it happen.

Now when it comes to quantum and climate in our topic, which

we speak about today, how

I see it is

I always want it to happen right now rather than waiting.

That's my nature. But if you

look at all these reports and how the progression is taking place

in the next three to five years is more of like the roadmap for Quantum

provided by McKinsey to IBM. So if you look at a company doing

quantum and climate, how I would perceive it is okay. They're working on

the fundamental elements to make it possible

so that their maturity will come in that three to five years

or maybe later. And that is also something we also

experienced when we were trying to build our platform around

quantum and climate. But then we realized

what we're doing is it's actually a general purpose thing. It's not just

climate. You can do it from climate to chemistry to finance to everything.

So the last one year we opened up the platform to every user

and that actually democratized our platform into

quantum as well. Like democratizing quantum. So

I think to give a short answer,

how do I assess, I think it's based

on the probability of not happening is

Very high. I always just get

excited. That's how I would assess it.

Well, there's a lot to be exciting about. Right. Like, you know, we

missed out because of, you know, we were, you know,

born too late to be there when the transistor

kind of exploded in the PC revolution.

And this is an opportunity, I think, for, for people who are

in industry now or, you know, in university today to like

participate in kind of something that's going to be at least as impactful as

that on society and everything.

Sorry, sidetrack. But you know,

what do you think is the biggest misconception that

you're facing in quantum computing? With climate

in the climate ecosystem? Yeah. So

when it comes to the basis misconception is that

quantum will replace normal computers or classical

computers, I think that is the biggest misconception

we hear about and whether we will have quantum computers in our

household. So I

was having a meeting like few hours ago

prior to this event. It was a European

university I was working with and one of the students asked me whether we are

going to have computers, quantum computers in our household.

So I, I think it's,

it's, it's just the unawareness of quantum because it

has become a buzzword and if there's a lot of unawareness.

Right. So when it comes to quantum and climate, I think it's a

very, very niche audience who would

look quantum and climate together with that.

I think one of the biggest. Huh. I

think the biggest

misconception is

quantum alone could solve climate. Right?

Okay. Yeah. Because it's, it's

not quantum who will go and solve climate. It's, it's, it.

Quantum to classical to all this computing should

facilitate the scientists to come and make the discoveries.

Even though quantum is a reality, it will not come and do that magic. It

should be a conjunctive effort of CPUs to

GPUs to quantum, all working together with the scientists to make those

discoveries. It's not the technology that will come and solve. I think that would

be a point of view I would have. Yeah.

Interesting.

What advice would you give someone who is

building the community or content around all this climate

innovation?

Yeah, I, I think for this I would like to quote

Bill Gates. He said in his book

how to avoid a Climate Disaster. So in that

he referred that if it's not outside your

house, you will not know it. So only when things are

outside of your house, like let's say if there is a big flood outside your

house or in your neighborhood, then only you will know. Okay. There is some Big

issue is out there in the. But when it comes to a climate

aspect, it's those little, little things that happens in the

environmental ecosystem that will create this

big things. So I think for content

creators who are in climate who speaks about it, I

think they should

tell what like everybody

knows about climate global warming for the last few decades.

I think they don't know the current, the.

The probability of having a catastrophe. For example, in my

team I, I speak a lot about this planetary systems model

where there are nine systems and out of nine how many have gone into

the. To the red zone. Likewise. So

these things actually not largely spoken. Only few, few people

are speaking it out there. I think largely the content creator should speak about

okay, out of nine systems, seven the red

zone. And there is a high probability that we will have these kind of issues.

So this is what you should do as individuals

to make that change. Example, if you're a software development

company, go to green hosting.

How could you make your application green? So that is a very

segment oriented content creation but that will create impact.

Likewise and if you're using chat GPT okay, don't say hi because I

will just create another.

It's just even how to prompting is a good area to create

content around climate because those will actually create

tangible results out there in the market or in the world.

I just everybody knows, okay, you need to use recyclable

things. From the school days you have been taught to do that. But now

the systems are different because the world is different. You need

more day to day generation specific

content around climate, around awareness as well as how to adapt or

change. Okay,

interesting.

Do you think the climate innovation in quantum that's going to happen

is going to be paired with AI?

Absolutely, absolutely. Because all the. Because

if you look at how, if you look at how innovations

have evolved or how real breakthroughs happen in the world, it's just not one

technology. It's a combination of all these

technologies working harmoniously and

driving real change. So it's not just the

semiconductor that created the Mac. It's

the hardware, the wiring, the semiconductor, the, the.

The. The glass technology or the, the how the screen

technology all together created the map or the iPhone. The

revolutionary breakthroughs. So it's not just

quantum or any other technique. It's

most likely be all CPUs, GPUs, AI,

the mathematical models, everything. All working harmoniously

will bring solutions. And

I think AI will be a great catalyst

to speed things up. Okay, for

example this not yet built but it's in our roadmap.

Which will be released early next year. We are building

quantum research agents who will work or research

247 uncertain given projects run on

quantum simulations and give results. So this we actually.

So there is a new way of knowledge creation which was not

there before without AI. So

AI will be infused with everything.

But, but how will human

intuition or that wisdom

side of things will play out is

I think that will be the most

important part I guess in this whole transformation. Like

I don't think what Einstein found will be

discovered by an agent. By an agent or an AI.

I see what you mean. Yeah. But the next

Einstein will probably be helped by an agent or some kind of

agent. Exactly. Exactly.

Interesting. How can the

government. I mean Sri Lanka just suffered a devastating

cyclone. I

saw that on the news this, this past week.

How can the government better support the adoption of these complex

climate technologies without slowing down

innovation at the same time?

Absolutely. Great question. So. So

in. In Sri Lanka. Okay, I. I'll

take it in Sri Lanka the and the

global landscape. So in, in Sri Lanka

we got out of a certain crisis situation

and now we were back back on track and then the floods came in.

So in Sri Lanka innovation is

so we. We have a huge deep tech community in Sri Lanka

starting from biotech to like people

like us who are doing quantum and so much

more around and EVs likewise.

So the, the universities, the government.

So there's a massive amount of initiatives that's

going out there. I think it's more of like taking to a global stage

to

get the right investments to to let's say

having part global partners is more of like a current

initiative that is happening to make sure these technologies

scale because there's a lot of things that's happening

within the country that's not out there.

So the government's support is now

being put a lot to those kind of innovation. So this includes

climate solutions as well. Climate tech to different sustainable

solutions many others. Because

sometimes it's not just within Sri Lanka. These enterprises should also

go out to outside the world and then only these models will also

be better and better and become

much more production ready.

So there is on one end but if you look at the the

Europe or the US I'm sorry especially around Europe the European

Union is having a lot of CL up

modeling related fund related funding to grants games

like us. I think those collaboration is something

that is being encouraged at the moment.

Yeah, that's my take around

it. It's an exciting time to be in. This industry, isn't it?

Right. It is exciting yeah. And like I think

2025 was like, we'll look back at that

as a particularly

interesting year. Right. I don't know,

maybe next year will be more exciting and we won't even remember this year. Who

knows? Absolutely. Hopefully.

I believe that 2026 and

2027 are going to be extremely,

very interesting years because even in the quantum space there's

going to be a lot of hardware maturity coming

in. There's a lot of topics being spoken around industrial

applications, around quantum. So this will cover a lot of climate use

cases, practicalities and maybe

2027, 2020 onwards, maybe we will even

have more quantum advantage achieved use cases

at an enterprise level, hopefully. So 2026, 27s

are going to be very, very interesting years

to look out for and

we too look forward for some breakthroughs.

Yeah,

interesting.

Where do you see. I'm just awash in possibilities. I'm so sorry, I'm

just awash in possibilities. Possibilities. Where do you see the biggest

gaps between what energy companies need

and what the deep tech researchers are building right now?

Okay. When it comes to deep tech it's again,

it's a very broad spectrum. But I'll fixate into

quantum. I think

one, I think there are two areas. One is.

How exactly will quantum create

value for me is a

question that is asked by industry from quantum companies.

That is an awareness problem. And,

and then secondly, when you get past that state,

can it create value now?

Still the answer is no, unless you are preparing for research.

So that is what I've

seen publicly and as well as the certain interactions

I've had together with certain companies.

So mostly it's at a POC level, not at a production grade

limitation being the hardware maturity, the instability of

the, the quantum error correction likewise.

But at a POC level there are many, many use cases coming in.

Right. But internally, which is because all

these are R D. Right. These are research topics, you don't put it out there.

So internally there can be a lot of things that energy

companies are investing in quantum to keep themselves ready. Which is

not out there in the public or open out loud. Spoken out

loud. But those are probably a lot of. Secret projects

going on. Exactly. That they're not gonna particularly like. You don't know

what they're working on until it's a massive success and even then. Exactly. There's

no one unless there's some regulatory reason, they're not really incentivized

to help their competition. This is popular across

energy to pharma because it's all R and D. Right. You use it

for your next usp. But what I said

is something that we all mostly face, not only us, but

mostly many other companies when we do PoCs with

companies.

Did we lose him? No, no, we haven't. No, he's

here. We're just, we're coming up with our next. My mind is just like, I

know, same here. So many ways. It's so wonderful. Yeah, yeah.

It's an exciting field. And I think a lot of people are

skeptical of all the promises AI has made.

Rightfully so, because some ridiculous promises have been made.

I do worry that

Quantum can also eventually part of the hype

cycle, I guess, is ridiculous promises, right? But

when kind of the hype wave crashes,

there are still new possibilities. I mean, look at the dot com boom, right? Like,

you know, pets.com, right, is, you know, I don't know if you're, you may not

be old enough to remember tets.com but pets.com was like this. There were a

lot of crazy startups that were started in the 90s that

were way ahead of their time. Right

now it's fair to say Amazon kind of owns that space. Right.

You know, in most countries. Right. The world we live

in today with the, you know, the smartphone and things like that, these were all

things that were, I would say, promised in the

dot com era, but the underlying technology wasn't quite there to

make it practical. I

wonder what that will look like for, you know, the Quantum hype wave.

Could be similar. Yeah, could be, could be similar.

But when it comes to Quantum, it can be slightly different

because it's, it's mostly

with the scientific community. It's not like a dot com boom

where it's mostly accessible or spoken out loud. I mean, I

mean, like, because it goes with a lot of math and physics.

So I, it's not a general, am I? But what I was trying to say

was it's not a general purpose kind of general public kind of a thing.

You have to go with science and likewise. But when it comes to AI, of

course there can be similar patterns coming in and likewise.

But.

I think it's, it's, it's so

this. How do I put it?

You can. I believe that.

I, I actually don't believe

about that wave personally

because, because

deep down what we believe is to move the world forward.

So that's our underlying philosophy and that's

at an early stage that what I wanted to do,

what can I do to move the world forward? So that has been my

personal motto. And when I started off in

2014, we developed software that with that inspiration we actually did a

lot of things, did a lot of movement around

in our community with our customers likewise.

And what we believe with Feynman is

that over the years a lot

of studies have been there where

potentially how quant, how the branches of

theoretical physics could bring value to

people or to, for us.

And if we can take one part of theoretical

physics like quantum computing back in the days and now practically apply,

just forgetting about this hype curve and really make a breakthrough,

that's what we are heading towards. So personally

I really don't worry much about hype

curves, whether I just believe in the science and what we

can do, which is controllable for us.

And if things are not working, yes, we

pivot, but I think deep down

we look for that moment,

what we do right now, can we really move the world

forward? I think that's, that's our driving force. When

we have that framework in our mind, that hype

and those things becomes noise in a way. Right,

Interesting. It's, it's how I govern myself.

No, I mean that's a great way to look at it. I don't think. I

think it's a healthy way to look at it. Right. And it is

very easy to get caught up in the hype cycle and the hype wave. But

at the end of the day, I think I like your approaches, you know,

self governance. Right. Because

then you're not chasing what's coming next. It's something that you

intuitively say your gut feel everything all together.

It's not just a very logical thing or a market

reaction you are chasing. It's something that you envision

and that brings much more confidence, stability

in what you do, even in the most darkest

times, because it's a belief that you're chasing

and it becomes a reality.

That's a good way to put it. Is there any other advice that you would

give, let's say students who are in university now,

you know, let's say who are in computer science and they're trying to figure out

a potential path. Like what, what advice would you give them

considering where you are now? Absolutely. So

I think it's not at all related

to quantum or climate. So it's, it's, it's, it's, it's

purely about self

discovery.

It's, it's purely about self discovery.

Example, this book called Ikigai, where you

have this beautiful Venn diagram. But you love to do what the world needs,

what you can do likewise. And it's just one

model. But even if you just really explore yourself

and see what you are deeply, deeply passionate about,

deeply, that gets you into the flow state

that you can keep on doing. I think

being honest with yourself and figuring that out at the early,

early stage will definitely give you answers to all

the decisions that you want to take in life.

Because it's your journey. The decisions you take will not

you. It doesn't have to justify other people's frameworks

or what their thought processes are. So. And

you will have your own compass inside when you have yourself

cleared out. That's what Bruce said. That's what Steve Jobs Jobs said. That's what

any,

Any of these philosophers have said. So for me, I think early on

I met mentors and coaches who

actually helped me carve that picture out. For me, I think that is

the most important thing for you to understand yourself, self

discover and final remarks,

whatever you do, student, entrepreneur,

career employed, I think

whatever you do, it's all about you. Think big,

start small and work fast. So think big, start small

and work fast. Work fast. Yes. Okay, where can

folks find out more about you, about Feynman and

anything else you'd like to share? Yeah, so

I think you can of course have the web links and

all, but you can always catch me on LinkedIn x

any social media platform as Adisha or Disha Government pillar and

website is feynmanhq.com and

everything is pretty much connected so you will see the content. But I think you

can also share the reference links in your captions and

all. Fantastic. Excellent. Thank you so much

for today. I, you know, climate affects everybody and this

was really fantastic. I really appreciated your time on

this. And we'll play the outro music the.

Universe dances it snug as a

rotten podcast Turn it up

fast Kenneth and Frank blowing my mind at last

Quantum podcast.