In this episode, Candace Gillhoolley sits down with Harshitta Gandhi, a researcher at the intersection of quantum computing and quantum machine learning. Together, they explore Harshitta Gandhi's journey from her childhood in Delhi—where her passions lay more with theater, singing, and athletics than science—to pursuing a PhD focused on physics-based quantum machine learning at the University of Central Florida.

This conversation delves into how skills from seemingly unrelated pursuits can transfer into quantum research, the mindset shift required to thrive in the field, and the importance of imagination, resilience, and community support. Candace and Harshitta Gandhi also tackle the real-world challenges and opportunities of quantum machine learning, the evolving role of AI, the need for approachability in quantum education, and what it takes to encourage and retain more women in this cutting-edge arena.

Get ready for an episode packed with practical advice, personal stories, and a candid look at the future of quantum technology!

Links

• Harshitta's LinkedIn - https://www.linkedin.com/in/harshitta-gandhi/
• Watch this interview on YouTube - https://youtu.be/14htuPc-X9w
• Harshitta's on Impact Quantum Season 3 Episode 9 -https://impactquantum.com/podcast/superpositions-and-super-careers-harshittas-quantum-leap/
• Watch Harshitta's on Impact Quantum Season 3 Episode 9 - https://www.youtube.com/watch?v=hwD0TN7EvIE

Time Stamps

00:00 Balancing hobbies with academics

05:24 Importance of mental rigor in sports

09:23 Supportive community and mentors

12:03 Starting the PhD journey

15:17 Encouragement for women in tech

20:15 Studying time series data

22:01 Explaining quantum machine learning

26:38 Discussing different qubit types

29:01 AI assisting with information retention

32:19 Making quantum computing approachable

35:47 Balancing quantity and quality of qubits

38:28 Creating inclusive spaces in tech

43:48 Embracing a versatile skillset

46:23 Making quantum computing approachable

Trust me, if I could do it,

anybody can. You know, and I

don't think, I don't think you need to get

good grades. I don't think you need to, you know,

be, be a person or a person who's, who's, you

know, studying for hours on end.

I think what you need to get into quantum computing

is, is like a really good imagination.

Welcome to Women in Quantum.

Hello everyone, and welcome back to Women in Quantum, the

show where we spotlight the brilliant women helping shape

the future of quantum science and technology.

I'm Candice Gilhooly, your host and today's

conversation is one I've really been looking forward to.

Joining us is Harshita Gandhi, a researcher

working at the intersection of quantum computing and quantum

machine learning, and someone who brings both technical

depth and thoughtful perspective to where

this field is heading. In today's episode, we're going to

explore Harshita's journey into quantum,

the mindset shift from classical to quantum

thinking and where she sees real momentum,

momentum happening and real challenges, challenges emerging

in quantum machine learning. Hi, Harshita, how are

you today? I'm good, thank you. How are you doing?

I'm great, thank you so much. And to be

completely transparent, we, I had the pleasure of

interviewing Harshita on my, the other

podcast I do, which is Impact Quantum, and

honestly one of our best episodes.

The, the community really responded and it just

keeps on gaining momentum month after month in our podcast

archive. Very happy about it. And so I said

I have to have her back for, for Women in Quantum.

So let's start at the very beginning. Where, where were you born

and raised? So I was born

in India. Yeah. In Delhi specifically.

And that is where I was raised and

finished my schooling and my

undergraduate degree in computer science.

Okay, so when you were young girl in, in, in

India and you were, did you have a proclivity to the math and

sciences? Was it something that interested you? Was there something else that

interested you more? How did it go about.

Honestly, after

I, I don't think I was a very studious student.

I was more into theater and singing

and I was more into sports.

So I'm, I'm actually, and to the extent wherein I

competed nationally in speed skating and,

and, and I competed within the state, like within

the country in swimming.

So I don't think I was ever,

you know, attracted to math or

science particularly. Obviously they were, they were a part of my

curriculum and what I was learning. But

I wouldn't say that I was like a very studious student.

But looking back, honestly, now that I Am

doing quantum computing and quantum machine learning.

I think the skills that I used

in sports or the skills that I used

in music, in, you

know, more, More artistic pursuits in my life,

they are not. They are like,

they are transferable to what

I'm doing to. They actually impact how

I think in a very positive way.

I think I'm able to figure out the patterns

and things that people actually

miss when they come. When it comes to quantum computing and quantum machine

learning. So I wouldn't say I was a very student. I was

attracted to math or science per se, but I was, I

was more of a free spirit, you know, taking into

account everything that was offered. And I was like, oh, yeah,

I'm supposed to do this. So, yeah, that was, that was

my, my childhood growing up. Yeah.

It's interesting because I have not, I have not really

spoken to someone who was such an athlete who

then went into, into this field. And I, I think that's

incredibly exciting because. And I like to hear that there are

transferable skills, you know, that

someone can take from, you know, the

athletic arena and bring them into their studies.

Absolutely. It's not just, you

know, okay, a lot of people mistake

learning like doing a sport with physical, rigorous.

I think, see, you know, physical rigor is important,

obviously, but more important is the mental

rigor that you need to keep practicing the same

shot, you know, the same.

It's, it's really difficult. Difficult to keep going around in

circles in a, in a skating rink. It's really difficult to keep lapping,

you know, in a swimming pool and

trying to improve your, your posture while swimming, while

your posture while skating. It's, It's. It

takes a lot of the mental energy that you have, and

that is how you develop your physical

rigor. Honestly, coming.

Transferring from sports or like

visual arts or whatever into quantum

computing is also more about

the mental rigor because. Well, I started my PhD

at UCF University of Central Florida just last month.

And honestly, a lot of the things that I'm doing need

me to, you know, keep doing them in order to

figure out what works and what is the

best outcome for my, for my project, for my model.

Right. So building that mental rigor, building

that patience, building that, you

know, not giving up attitude or that mindset. I

wouldn't call it an attitude, but definitely a mindset is, Is really important

in, in science.

I like that. I like that a lot. I like the approach of

needing the. To strengthen, you know, your

cognitive ability to get you through moments,

you know, where, you know, you would be

Bored or you would, you would lose focus or, you know. That's

interesting. So what are three words you use to describe yourself?

Three words to describe myself.

I would say I'm a very creative person.

I'm a smart worker

and I would say

I'm an out of box thinker.

Okay. Yeah. So has

there been someone, a family member, a mentor,

a teacher that has influenced your path?

Interesting. Honestly, I

am blessed with the most

amazing parents ever. So

this is a shout out to them. I don't think I would have

reached where I am if it was not for my parents.

I, and so both my parents are from like a business

background or like a commerce background and

my mother is a mathematics teacher and

she did her bachelor's in economics. So not really

science. Science, math,

but. And I, I want to say that even though

they don't really understand what I'm doing,

I am telling you the patience that they have showed me when I

go on and on and on about something and they're like, okay, we didn't understand

a thing, but good for you. You know, so,

so that kind of support and you know, pushing, like sometimes

I am, I'm, I, I honestly I have given up like a number

of times and you know, I did not have, sometimes I did not have the

strength to start over. So I, they have been a pillar of

support who that has,

you know, got me to where I am and I

don't think I'll be, I would, would be where I am today

without their support. So definitely, definitely

a big shout out to them. They have been amazing.

Apart from that, in school I

did have teachers or like even in

college, even in, during undergrad I had profess

teachers who I would, you know, go and be like, okay, I, I

understand this concept but then if I expand

it to something, you know, like a little

bit intra extrapolated to some, some other concept, how does

that work? So I feel like the community of teachers

and professors that I grew up with or like I

was blessed with honestly have,

have been a really big I guess,

support. Yeah, absolutely. So,

so that, that I like. It's, I think it's a community more than a

particular person.

Yeah, so it's, it's basically community that I grew up with and

definitely my parents. Okay, so

what's a lesson you've learned from facing

some setbacks or failures?

Hmm.

It's not the end of the world.

It's not the end of the world. Honestly, when I was going

through those failures or those setbacks, I was

thinking it is the End of the world.

But, but turns out it's not. Again, all you need

to do is shift your perspective a little bit.

Stand up on the desk, stand upon the chair to get a

bird's, bird's eye view or something.

And, and it's great. I mean, you'll have to work for it, don't get

me wrong. But you'll get through it.

I like that. It's clear you've built

the resilience that you need

because not everything goes as planned. Right? Yep.

So that's kind of, it's so very, very

important. Resilience, I think, is a huge, huge deal.

Okay, so what were some of the biggest challenges that you faced

so far in, in getting your, your PhD?

I know you've just begun, but it's an interesting, you're,

you're, you know, you're at a very exciting moment in the, in your, in

your, in your education and your development. So have you faced a challenge yet

that you found pretty, pretty decent that you have to

rethink something or. Tell me, how's the beginning of this

experience going for you? That's actually a really good

question. So

initially when I started my masters, I

was working on computer vision

problems for quantum machine

learning. Right. But now since I started

my PhD, my focus has actually shifted from

computer vision and like video and audio to

thermodynamics of a building.

For example, the house heating systems and

the H Vac and like the cooling systems in states

like Florida. Right. It is.

Right now I'm working. I started working on

a house heating model which is on

quantum computing particularly,

which again, needed me to

shift my thinking

to a completely new space.

It's been really interesting to figure

out how quantum computing can

fit in. I want to say it's a very

old discipline. It's a very old discipline.

Like heating. Thermodynamics is a really old

discipline. So how quantum computing, which is just coming up and

everything fits into that and vice versa,

obviously. So that has been interesting.

That has been honestly a challenge from. Because I,

I haven't, I do know a little bit of physics, but I haven't, you

know, done thermodynamics or, you know, capacitors and

resistances in a while. So doing that

was like a little bit of challenge.

So again, again, I would say a shift in the thinking

of how to do things, how, how two things

interconnect. Honestly,

it has been, has been interesting. So

how would you talk about what you do right now to interest

more young ladies who are,

let's say, in high school. Right. You know,

and to get them to, you know, consider

quantum computing and quantum mechanics.

I feel a lot of people or a lot of girls

are hesitant,

honestly in going into such a rigorous field because

of the societal, you know,

I want to say prejudices, honestly,

but trust me, if I could

do it, anybody can, you know, and

I don't think, I don't think you need

to get good grades. I don't think you need to, you know,

be, be a person or a person who's, who's, you

know, studying for hours on end.

I think what you need to get into quantum computing

is like a really good imagination,

just thinking imagination. I swear I was. Yeah, yeah,

absolutely. You need, you need to know how to imagine

things. And I'm sure every colonel out there

knows how to imagine things, you know. Absolutely.

It's, it's something that is very, very

innate of people, honestly.

And I think you just need to imagine things

in a different way. And I

think that's all you need to get into quantum computing.

It's, it's a very, it's a very up and coming field

build. It's a very transferable skill set. In

one viewing, you can honestly transfer your skill set to

n number of things. I can guarantee that. Right.

So yeah, yeah. So basically you just

need to know how to imagine things is all I know, I

think, I think that, that really, you know,

having people listen, having people see

you, you know, hear what you're saying, I think that it's really going to

break some, some barriers to, to show women

they can go into something that maybe they're not

necessarily math inclined or super sciency inclined,

but again, they're adaptable and

they can imagine. And I think that

it's, it's the ability to accept

the uncertainty. Absolutely.

Yeah. Right. I think that's very, very,

I think that's very, very exciting for a lot of people. For sure.

Absolutely. So is there, so you said you're

studying thermodynamics, now you're working on heating systems,

H vacs, cooling systems, that kind of concept. So what is

the, is there a real world problem behind that that you

are particularly excited to try to

address?

Off the top of my head right now. So for example,

house heating systems versus the energy

they use, you know,

just, just house heating systems, consumption

of energy that's happening. How can we

reduce that in and in turn reduce the cost

energy basically to people?

So this is just off the top of my head, but then we'll see where

this research leads. Yeah, right. I Think that

also understanding thermodynamics and

maybe in a, in a bigger picture might concern like

climate prediction and, you know,

how all the energy that's going on right

now that is, is being sent up into the air. The, you know,

all the chaos it's causing, especially in places like Florida.

Yeah. You know, it's very personal. I, one of my best friends lives

in, in Miami and all we do is talk about hurricane

shutters, Right? Absolutely. Yeah. I just

moved here, but that's all I've heard about.

It's. Right. It's like, again, you know, I'm from New York and it's just not

anything that I've ever even thought of. You know what I mean? Now I'm in

Montreal, Quebec, again, not thinking about hurricane shutters.

Thinking about probably the best, you know, shovel and snow

removal concepts that they're out there that, that's what I'm facing. Right.

But, you know, Montreal's pretty much figured out that

pretty, pretty good, which is great. So this is

exciting. This is very exciting. So how does machine learning fall into all

of.

First of all, it's a lot of data. Okay. But it's a

different kind of data. It's a time series data that

I'm working on right now. How a

temperature goes from 8 in the morning to like 12 noon,

how it moves from,

like, what are the temperature ranges or like certain other factors.

Temperature is all I can think about right now. But there's a lot of factors,

like before a hurricane, how the pressure drops, how,

you know, wind speeds increase,

that sort of thing. That is going to be a

little more complex, a little more further down my,

further down the road in my PhD, but,

but something to look forward to. Yes.

So you're bringing in your machine learning

mindset into what you're doing.

I'm sorry, go ahead. So machine learning is

basically using that data, all

this, different variables, and trying to

figure out how a, a better prediction model for the,

for the hurricane season. A better prediction

model for how to, how to

save energy during that particular time, how to

get back on the grid during, you

know, after that.

Yes, that's, that's where machine learning comes in. Okay,

so can you explain to me what is quantum

machine learning then? Yes.

So taking the

concepts of superposition and entanglement

and applying them to machine learning

concepts like supervised machine learning or

prediction or classification. Right. Or reinforcement learning for that matter as

well, is basically the

amalgamation of quantum computing and

quantum and machine learning. Basically

what this means is this was, I think this

was very what basically what this means is

creating a quantum circuit to do

machine learning tasks. And this quantum

circuit does have the ability to

have the ideas of quantum, of

superposition and entanglement on

like in how the circuit is made or how the qubits are

made. So that's

what quantum machine learning

basically is. So how do

you decide when a problem truly benefits from

a quantum approach?

I think every problem is going to benefit

from a quantum approach sooner or later. I am

telling you, sooner or later. Quantum

is a different type of computation, basically.

Right. So what the machine. In machine learning, what you

do is like train the, train the model to take

one,

one episode or like one image at a time. Right.

In quantum computing, you can actually give it all the

instructions, all the images at once. And it does

hold the capability or it does hold the potential

for exponentially learning how to compute. Right.

Exponentially computing all those things altogether.

Right. So it's going to every. I think these problems

are going to benefit from quantum computing in cases of

memory, in cases of actual

computation, where it takes a lot of data. Right. So data

is again, a bottleneck for machine learning or

supercomputing. Supercomputers actually. Right. So

quantum is going to break that bottleneck in

particular machine learning tasks, which

is, which is actually really interesting to,

you know, see how that happens. And it is actually

happening because you can actually compute

more. You can, you can compute a larger.

Target space in quantum computing rather than in machine

learning. So it's interesting.

There's things happening wherein people are doing this

sampling, Gaussian, Boson, Gaussian

sampling. So that's another area wherein we take.

Wherein the sample size is too large and we need to take

samples from the data set. Right. So how to do that?

How. And then quantum computing is

actually benefiting from this

sampling as well.

So is there something going on right now, you know, in

the news with innovation that's happening daily

in quantum computing that

has you particularly excited because of your field?

Honestly, there's a lot going on,

right? I mean, you know, there's a lot going on. Yeah,

absolutely.

I attended a conference last month, the D

Wave conference in Quantum Computing. So

where there's this university in Florida called the

Florida Atlantic University or.

Yeah, the Florida Atlantic University. They're getting actually a supercomputer.

Actually, no, a quantum computer at their university.

That's pretty exciting. Honestly, there's

a lot of things happening in like the modalities

of quantum computing, wherein we're trying to make

qubits in different ways. And it's really Interesting,

because not every qubit type

is suited for every task that is out there.

There is, there's a lot of research in which is the best

qubit. Right?

No, no, what I think is there's going to be a best

qubit for a particular task. It's not going to be

the best qubit overall. You know,

that's, that's, that's my take. Honestly. It's not going to be the best cube at

all. It's, it's, it's fine. For every, every task that you want to do, that's

not going to be the case. So that's pretty interesting.

Yeah, yeah, that's, that's pretty much. There's a lot of

other things going on as well, but, yeah, that's, that's the highlight that I'd

like to, you know, put it out there. Yeah, yeah. Like, I think of it

a little bit of like Game of Thrones. Like, there's seven kingdoms. Like,

there are different kingdoms for every qubit, and every cube

qubit is like the master of their domain,

of what they can specifically handle. And, you know, and

it would make sense. Superconducting is for, is for one problem, you know,

ion trap is for another problem. Right.

You know, photosynthesis and, you know, these are for

solving different types of problems. So what do you think

about the role of AI in

accelerating quantum development?

It's, it's huge. Honestly,

The amount of brainstorming that I am doing with

AIDS these days is, and the

quality of answers that I'm getting, it's amazing.

I know there's, there's. I mean, obviously I have

to use my brain. It's not like I don't have to use my brain. But,

you know, again, getting, getting, getting something

of. Okay, I cannot do, I cannot do like hundreds of

papers in a week. Right, right. I

cannot read. I mean, it's physically not possible for me to retain all

that information. Where AI is going to help

is basically retaining all that information and honestly

getting, getting the information when it is needed.

Right. So I would know, I would know. I would have an idea

about, oh, this is, this is the, you know, main idea of

a particular paper. But then

to remember it at a given time and to implement it in a

particular project where it actually fits in is, Is

going to take a lot of people working on the same project. But

then if, if I can

brainstorm something of sort. Oh, do you

think that this particular thing is going to fit in with this particular

thing that is in this particular paper? Right.

How, what do you think, right. And what, how do

you prove that or how, what is the

logic behind it? So just these kinds of things,

I don't have to, I'm obviously I'm going to implement them

after I've brainstormed. Right. But that

brainstorming is really, really interesting and I'm actually

really happy to see that it's going in

a very positive direction. That's the first thing

where AI comes in. Right. The second thing

that, where AI comes in is coding.

I don't particularly like to code.

I know how to code. It gets interesting, but it's

very repetitive. It doesn't interest me

as much. Right. I know people,

all they want to do is code things, not me.

That's fair. That's totally a

talent. That doesn't necessarily. It shouldn't preclude you from

something else if you don't have it. And now

with, you know, the cloud and different,

and different eyes, you know, you can vibe code, which can

be very exciting, especially for me as a

non coder. Vibe coding is, is like

almost like a, a backdoor secret

entrance. Like, it's very exciting. Absolutely right, absolutely.

I don't, I don't question that. It's,

it's also very interesting to see

because a couple, not even a couple years back, one year

back, we were getting gibberish

answers. We were getting

not as good a code as I'm getting these

days, honestly. And I

think AI is going to have an immense, immense

impact on quantum computing as a field.

I think so, yeah, I think so. Especially with what you do with the data.

Like what can the date, what does the data represent? What is the story

that can be told to, so the masses can understand.

Yeah, absolutely. Also,

and I think we need more mass understanding

of quantum computing or at least, at least have that kind

of reach for, you know,

people to get into this field or like the field has to be

approachable. Sometimes I think quantum computing is not

approachable.

So this, this is actually being, be the field being approachable is

another point that I would like to make when it comes to

girls actually taking up quantum computing or, you

know, quantum computing reaching to people

who don't necessarily necessarily have the, you

know, background or, you know, background to understand

what it actually is. So I think it's really important for quantum computing to

be approachable and have the reach. And you're doing an

amazing job here, you know, with the podcast and everything. That's amazing.

Thank you. And then, and really that is, that is really the point is to

show the approachability to show the common data points between,

you know, young, young women and other women

who are doing it in different, in different

aspects, in different stages of it all. I mean, you are, right now, you know,

you're in your PhD, which is very exciting stage

of where you are. And I'm sure, like, as you

go through it, your mindset is going to change.

Absolutely. In terms of, in terms of like the practicality of it. So

can you explain what practical quantum advantage means to

you personally?

Practical quantum advantage,

honestly, in the both most

basic terms, it's, it's getting a better

accuracy on a particular task that I'm

running or, you know, better. Better

training time, less training time, basically.

And that is,

that is on my level. That is,

that is basically what quantum advantage is

practically right now. Computing

more data in less amount of time. Computing.

Computing in larger space wherein you can,

you can put more features or like more input

variables than the number of qubits.

Right. Because each qubit has

so like 2 to the n space

to actually learn how to compute.

So just doing these small things

to figure out how to, you know, and

like to figure out the advantages of quantum computing, that is, that

is where I think the practical advantage of quantum

computing arises. All right, so let's talk about. We're.

I was, I was asking a question about how should the industry Balance

Qubit Scale vs. Qubit Quality

Qubit scale? I think better quality qubits

are more important. And

so that's, that's probably a bias, honestly, but

how to balance them? I don't think having

One amazing qubit versus like

50 horrible qubits is going to, you know, help us in any way.

But the scaling of

qubits should also be a little bit

towards focus towards, you

know, having good qubits. But honestly, these days

I feel like the qubits are getting better

also, like the noise and error. There's a lot of work

to be done, don't get me wrong. But it's, they're getting better

without a doubt on different modalities as well. So

that's good, honestly. Yeah, obviously you

have to maintain a balance between the two, but

how to do that? Honestly, I,

I don't have a, I don't have a, you know, direct

answer for that right now. It's okay. That's okay.

We've talked about kind of the experience of being the only woman in

the room and wanting to, you know, get more

women interested. What structural changes would

most improve the retention of women in the

quantum ecosystem? That's actually a very good

Question.

Honestly, as you grow through life

also and as you grow through your career and you go

through a lot of things, and I

think one of the

most important skills to develop is to stand

your ground.

It has to be in a very polite, in a very,

you know, systematic manner, but

it, you have to stand your ground no matter what.

So I think that skill

has to start developing in women, in girls early

on. I think that's, that's an important skill to,

you know, have workshops on, have

like, you know, real world, real world experience

for girls, young girls.

Kind of like a resilience training. Absolutely,

absolutely. There's no other, you know,

there's no shortcut to this. Honestly.

Other things that can be done is

like, you are doing like a, a safe space for women to, you

know, come and learn more things, more

approachable, a more, you know, open space wherein

not only women but like people from all other genders who are like,

less represented in like quantum computing can

come and, you know, learn more about

quantum computing. I think that is really important.

Apart from that, I think

you asked structural changes, right?

I think, I think again, creating safe spaces,

creating more approachable spaces

is very important.

Getting to like, meeting up with other women who are

in the field, meeting up with women who are in tech,

honestly, is like a huge, huge, you know,

push towards more people, more women coming into

these spaces. So that's, that's very interesting. That's very important.

A couple of conferences that I have been to also have

like women in Tech nights and

like women in, you know, tech communities.

There's a lot of them. One of my friends, she is

an ambassador for women in tech in Brazil.

So it's, it's really interesting to see the work that she's

doing and the

number of people like, who are in that community is, is really good.

It's, it's amazing to see the,

the response that you get when you, you know, go to these community

events and have

like regular meetups, even if they're like online and

not like in person. So I think, I think that's,

that's very important to, you know, have

and like, develop in different areas

in quantum computing. I like that.

I like that. So,

and we might have already just addressed this, but how could allies in this

ecosystem show up more effectively?

Yeah, I think, I think creating a safe space, creating

a community, honestly, creating

accessibility is the way

to go. I

think that's fair. That's fair. So as

you go through your PhD,

explain the process that happens. Then you're going for your

Ph.D. and what specifically are you going for your Ph.D. in?

It's honestly a little too early for me to address that,

but a general direction is going to be

physics based quantum machine learning.

Okay. Do you think that you are the type

of person that would like to take that knowledge. I know it's early,

but take that knowledge into, into creating your own startup?

Absolutely. I think it's going to.

Yeah, yeah. Again, PhD is also not

about, you know, learning

just quantum computing or, you know, just the models. It's also

about making network, like making your own network,

connecting with people, creating your own ecosystem

wherein you can, or like working with people.

I don't know everything about thermodynamics or

the things that I'm trying to do, so I need people to

collaborate with. I am doing. And

as I, I think I've said this so many times,

I feel people are bored of this. But collaboration

is huge in quantum computing. I don't

know everything that there's to. In quantum

computing, there's, that there's need to know. Right. So

collaboration is the key to quantum computing. So I

would love to translate my

skills into a

startup into like my own

laboratory, even if for further

research. So. Yeah, yeah, absolutely.

It's going to be, it's going to be a process. Yes.

So how do you keep learning and growing when technology

moves so fast? That

is something that I was

also thinking about.

So these days the workforce

has changed from

knowing just one thing perfectly

into knowing everything,

knowing a little of everything.

So you have a broader scope, you have

a broader outlook on

multiple things. You know, the basics and the intermediate scales of a lot

of things. And if need be, you can,

you can actually brush up on your concepts and brush up on,

and move ahead on that one particular thing instead of

doing just one thing for the

entirety of your life or like the entirety of your career.

So I think instead of going

deep into just like one thing,

it's very important to take into perspective

things from a variety of different angles and like

have a different outlook on things.

Again, quantum computing is an intersection of a lot of different

things. So it's

important to, for you to know a little bit of everything

rather than just like one particular thing. And

that's all. Yeah. So I'm gonna, I'm gonna

ask this and then what's, what's a misconception

about quantum computing that

you wish more people understood?

I want to say,

But it's difficult. You know, it's,

it is difficult. It, I don't think it's. But it's like it's

doable. You know, you just need to put in a little bit of effort, a

little bit of, you know, I mean, but that's true for everybody. You

know, career out there. You need to work at it.

And I know people who.

Who have worked on things that I

cannot even begin to understand. Right. But.

But then that also makes

things less approachable. I think quantum computing

as a field is very, very less, like, it's not

approachable at all. Right. So I think just trying to, you

know, tell people or, like, communicate the

quantum concepts in terms that the masses understand

is very important. And I feel, I wish that

more people understood that quantum computing is

not some, like, mystery or, like, oh, you know what? Oh, we don't know

what happens. And, and we don't. We don't think

we are intelligent enough or, like, smart enough to do quantum computing or

whatever. I don't think that's the case. I think. I think

it's. It's supposed to be very approachable, and I want it to

be approachable. And so,

yeah, I think. I think I would love people to

understand that it's not as, you know, mystical

and magical as it's, you know, portrayed to be.

Okay. I think that's a great. A great place to stop.

I. I really enjoyed our conversation tremendously.

I really apprec your time and your perspective,

and I hope to reach more young ladies out

there to say, you know, you too, can be part of

such a dynamic and exciting future.

Thank you for having me. I'm really honored that you, you know, reached out to

me and I was like, oh, definitely. Yeah, absolutely. Thank you for having me.

Oh, my pleasure. My pleasure. And I will let the music take us out. It.