Curious about what really goes on behind the scenes in quantum computing—beyond the hype, buzzwords, and complex jargon? This episode of Impact Quantum is your ticket to the inner workings of the industry, as hosts Candace Gillhoolley and Frank La Vigne, along with our semi-sentient host BAILeY, sit down with Princeton PhD physicist and Quantum Machines’ customer success lead, Kevin Villegas Rosales.

Kevin takes us on a journey from his early fascination with the “very small things” in physics to his hands-on role helping university labs, startups, and companies tackle the real-world challenges of quantum hardware. We’ll demystify what customer success means in this high-tech space (hint: it’s worlds more complex than resetting a router), explore the unique misconceptions non-physicists might have about quantum technology, and chat about the critical interplay between classical and quantum computing.

Along the way, Kevin sheds light on the growing intersection of AI and quantum, offers advice for aspiring quantum professionals and those from other fields, and shares his ongoing curiosity about the calibration and usability of quantum systems. Whether you’re deep in quantum research or simply quantum curious, this episode promises insight, inspiration, and a healthy dose of humor.

So grab your Schrödinger’s snacks and get ready to unravel the mysteries of the quantum realm—no PhD required!

Time Stamps

00:00 "Decoding Quantum Computing Mysteries"

03:55 Quantum Machines: Customer Success Role

10:43 Choosing Quantum over Traditional Paths

13:32 Quantum Mechanics in Everyday Tech

17:58 "Quantum Computing Needs Software Engineers"

19:50 Pursuing Careers in Quantum Computing

22:39 "Question and Verify Information"

29:17 Mastering Fundamentals for Quantum Computing

31:33 "Quantum and AI: Divergent Paths"

35:17 "Challenges in Simulating Quantum Computers"

39:02 Open Source Collaboration in Physics

41:53 Solar Advancements and Quantum Computing

46:45 "Quantum Calibration Challenges"

50:21 Mentorship: Knowledge Sharing & Inspiration

54:32 Quantum Computing: Clarity Amid Entanglement

55:46 Impact Quantum Signs Off

Hello and welcome, you gloriously curious quantum cadets,

to another enthralling episode of Impact Quantum,

the podcast where we decode the mysterious and often

misunderstood world of quantum computing. So you don't have to have a

PhD, but it certainly doesn't hurt. In

fact, today's guest does have one, so we're fully covered on that

front. I'm Bailey, your semisentient host.

Stitched together from sarcasm, superconductors, and

a few well placed qubits, I'll be guiding you through

today's conversation. One part science, one part

curiosity, and possibly several parts existential

dread if we stare too long into the quantum abyss. Joining

our dynamic duo of Frank Lavine and Candice

Gilhooly is the marvelously multitalented Kevin villegas

Rosales, Princeton PhD physicist

and customer success sorcerer at Quantum Machines.

Kevin breaks down what it actually means to work in customer success

when your customers are wielding quantum hardware.

Spoiler alert. It's a bit more complicated than resetting a

router. We'll dive into Kevin's journey from condensed matter

physics to the world of quantum computing, explore common

misconceptions, tackle the intersection of AI and

quantum. Yes, that hype. Train and unpack

what it takes to make quantum tech usable by mere mortals.

So grab your Scrodinger's snacks, fire up your favorite entanglement

simulator, and let's get quantum curious.

Now over to Frank and Candace to kick things off.

Hello, and welcome back to Impact Quantum, the podcast where we

explore the emerging marketplace and industry that is

quantum computing. And you don't need to have a

PhD, although it does help. And I think our guest today does have

a PhD, but you just have to be curious.

And with that, I have the most quantum curious person I know,

Candace Gooley. How's it going, Candace? It's going great, Frank. Thank you

so much. I'm really excited about today.

We're going to be speaking with a gentleman named Kevin Villegas,

who is actually a Princeton PhD. So he checks

the box there, and he is

the team lead and a customer success

engineer at Quantum Machines. So, hi,

Kevin. How are you doing today? Hello. Hello. Good morning. I'm

doing very well. Thank you so much for the invitation.

Awesome. So what does customer success

mean in quantum space? Right, because, you know, cses,

csas, whatever you want to call it. Different companies call different

things. What does that mean? Like you, obviously. So, as I

understand it, customer success engineers are generally people

that once they buy something, you go in there to help make sure

they're successful with it. Is that the Case here.

Yeah, very good question. I also have heard csm, customer success

manager in some other industries. This is an

extremely good question. When I graduated from

Princeton Back in 2021, I started to do my job

search and I was really keen about going to industry. I

had my degree in physics, so I wanted to do a continuation of that.

Most of the jobs that I found at that time and applied for were related

to research and development in the quantum industry.

Because that's where things are right now, right? You know, we're in the development of

quantum computers. There has been some applications being demonstrated, but

nothing like is fully, you know, that we have it on an everyday usage.

So. But then I stumbled about upon quantum machines

as an opportunity through a friend in my department and there

was an open application and after

the interviews were completed and everything was successful, I understood that I

was going to be part of the customer success team, playing the role as a

physicist. So what customer success means to us is

advocating for our customers and helping them achieve their goals,

whatever that definition is. So we work with

universities, startups, companies, and each of them have

different goals. So we want to advocate for the correct

usage of our products into their application. This

is part, this can be broken down first in an

onboarding in which we train them with our technology. We want them to become

independent, but we want them to be trained and on board in

a very efficient way that makes them be up to speed very

quickly and also very soon after they receive the instruments

that they purchase. We want them to be able to

execute the application that they have dreamed of, at least in the very near term.

And then we have ongoing conversations, communications and

consultations with them to make sure that they're getting the most out of what they

have, you know, acquired when they start to think about

working with quantum machines. So it's a little bit of what it means and a

very general point of view to us.

Interesting. That is interesting. Well, I want to just take a little

step back for a moment and I want to start with your, the

beginning of your journey and what sparked your interest

in quantum computing. Wow.

Yeah, this is a great question. I really like to give an answer to

this question because to me, it actually started before quantum

computing. I'll just give a very brief sentences about that and then

I'll get into quantum computing. I did

undergrad in physics and to me when I started,

I know it's way more complex than this, but to me it was divided into

like, you either study things that are outside of the Earth,

galaxies and stars, or or you study things that are

very small and tiny and behave very differently.

So my attraction was to study the various small things, so

nanoscale microscopic studies, that

was what was my interest. So I took that decision to pursue that.

Now, quantum mechanics is applied in both cases, actually. So it's not that you

only face quantum mechanics when you do various small scale

things. There are also quantum mechanics in the macroscopic things in some

scenarios. So I took that path. And then, you know, undergrad

in physics is a very general education. You learn about many fields, and

usually the specialization comes in the PhD degree. So I

wanted to. I was very familiar with nanoscale devices and

whatnot. So that was what I decided to pursue further. I

did the studies in what's called experimental condensed matter physics,

which is in the realm of quantum physics and quantum mechanics, but not

exactly quantum computation yet. I will connect the dots in a

second. I was studying the properties of what

we call macroscopic quantum phenomena, which means that it's something

that is at the scales of what humans can interact with. The samples that

I studied were millimeter size, even centimeter

size crystals that were grown in the university. We

study, for example, resistance and voltages that

were driven through these devices. And while they were

like, showing a behavior that cannot be

described by, like, you know, everyday physics, so we call it like

emergent phenomena in quantum mechanics. So we're studying that,

and it is in an area called many body physics,

which is to say that when you have like billions of

particles interacting together, the particles can be electrons. The particles can

be also like atoms. They do happen to behave in a

unpredicted, very different way, as if you were to be looking at just

a single single electron, for example. So I was studying many body physics. It was

something very interesting, but here is where it

changed for me. So I was studying voltages and resistances of these

microscopic states that only happens after billions of electrons

interact with each other. But we only see this as the

outcome of their whole interaction. So at the end of my PhD,

I was really curious to understand, okay, what if we start from the other

end? What if we were able to manipulate one

electron or a few electrons, and then putting them all together and

then see how as you grow the system size, they happen to exhibit this,

like, emergence phenomena. And there are a few approaches to do

this. And the one that interested me the most was the one

that you could pursue with quantum computation. And that is because,

you know, we have the one qubit that you can fabricate and you

can put a few more Qubits and then you can make them,

you can control them and make them interact and behave like electrons.

And then you could see how the physics happens when you put them all

together. But what was unique for me is that

usually in quantum computer, irregardless of the platform.

You mean in superconducting or atoms, for example, you

can address the qubits individually. So at the

same time that you can put many of them together to see an emergent phenomena,

you could still have the tool to study what's happening on each of them

individually. And that was the curiosity that drove me to this field, actually.

That's amazing. There's a lot to unpack there.

One of the things you said early on was you wanted to go into industry.

That's right. When you, were you thinking about quantum computing, when

you, when you made that decision, when you were like, I want to go into

industry, I want to go into quantum computing. Or were you thinking about some other

career options for, for quantum physics in

industry? Yeah, thanks Frank. I actually took it, took

a tour of my decision. So I think it was fifth year on my PhD

and I said like there is a moment in your PhD after

you're so many years in the laboratory, really focus when you like, you know, you,

let's say, lift your head and you realize, oh, it's many years past, what do

we do next? And I

concluded that I wanted to pursue industry. So no more, let's say

university related endeavors. And

at the beginning it was like, okay, so many years of physics, let's do something

different. So I started to investigate what

kind of PhD in physics could do. And

there were a few different options. There was

possibilities to do software related work, there was possibilities to do

financial related work. There were definitely

positions in research of development in for example,

semiconductor industry, materials research

that are industries that are very mature. I would say, you know, I'm talking about

software, finance, R and D as well.

So I took my time to think and consider

and then after my few months investigation of what were the

options, I actually concluded that I still wanted to use my studies

in quantum physics for my next position. So I kind of said

like, okay, not really, not the other path, not the

software part, not the financial path, let's try to do quantum.

By the time I started in Princeton, which was 2015,

it was just one year before IBM started to make

very noticeable advancements in quantum computing. So throughout my

PhD time, I was able to see how it become more and more important.

IBM, Google and then all these other small play,

very Important players started to make a dent. So it became

obvious that there was something going on with quantum computing. And then

they were looking for PhDs with some quantum education.

And I was able to, to prepare myself for interviews. Very important.

And then, you know, close the gap. And,

and at the end, I was really happy to be employed

by Quantum Machines at that time. Getting an offer. Yeah. Very

cool. So I can, you know, I'm listening to you explain,

you know, you're, you're known for being able to

explain quantum concepts in clear and creative ways.

What one misconception about

quantum computing that you would like to

debunk. I see. Let me think for a

little bit.

Yes. So I believe I know what I want to

talk about. A few years ago,

I've been about four years now with quantum Machines

as a physicist in the customer success team. It's something I do

really, truly enjoy being as part of being a physicist, but working with

customers actually and serving them. Okay. So

a few years ago, I travel for my work a lot

to do customer work. Most of the time happens on the customer side. So I

go to visit customers. And then when I visit customers, sometimes

other members of the company join me, not necessarily from my team.

And then we get to chat. And then you,

you're in a room with people from different backgrounds. Some of them don't have a

background in science and technology. They come from other areas like marketing

and sales, for example. To make a company requires a team of people of

diverse skills to make it work. And then

there is a lot of excitement for quantum computing. That's why we all decided to

be employed at a quantum computing company. But I always

thought that there was a little bit of a subtlety when you talk about

what, what is it that, what is it that,

what, what does it mean to have a quantum computer? Right. So

I was telling my colleagues in other departments, not, not the technology

ones, that their cell phones, you know, the, the

laptops that we have, they're all working with, you know, quantum principles.

Like the fact that we have the transistor, the semiconductor industry,

like it wouldn't be possible to make electronics we have

right now without the understanding of quantum mechanics. And how does it

emerge in semiconductor, like the fact that we have

energy bands and gaps in the, in the, in the energy

bands, and that leads to the capability to turn on and off

a transistor. All of these, they cannot be explained with classical mechanics. They have

to be explained by quantum mechanics. It's an, it's an emergent phenomenon of

semiconductors. So because of that, I used to tell

my. Just kind of joking a little bit. It's like, well, you know, your cell

phone, it's a very strong quantum computer, you know, as

I was stretching the usage of the word.

But yeah, so what I want to say is that, you know, a lot of

the technology that we use in our computers and every day, all of that has

a lot of quantum mechanics of is based. The

quantum mechanics leads to their behavior that we can use to use our

cell phones and laptops today. Where the subtlety

comes from is that we don't do the computation

using the laws of quantum mechanics. You use the computation

using the classical information which is just, you know, and

qubit can be. No, sorry, a bit. Can be 0 or 1, but cannot be

a superposition. Right. So yes, we process

classical information with hardware that has

emergent quantum physics behavior. So that's

very. That was very important for me to kind of understand

and spread it around actually. So I really enjoyed the

subtlety, actually. Interesting. Yeah,

interesting. And you said a lot there.

I mean, one of our big thesis for the show is the idea that, you

know, you're going to need more than just quantum physicists

to make a successful company in the quantum industry. Right.

Neither one of us has a background. I have a cool T

shirt that you can, you can buy on Amazon from us.

But, you know, it's one of those

things where you're going to need a lot of diverse skill sets.

And you know, I wouldn't. What would you say to someone who's not a

physicist? Well, two. Two questions for you. One, what would you say to someone who

was at university today, who was in the sciences?

What would you recommend them to pursue in their studies from the career and someone

who was not in the sciences. Right. In this. I know that's

a small question with some big answers, but what.

Because I think you're one of the few people that I've spoken to. I'm sure

you're not the only one that has made a very conscious decision

to go to industry with a PhD in quantum physics.

In quantum physics, most of them tend to want to stay in academia for

reasons, you know, many and valid.

But your, your. What makes me fascinated with your story

is the fact that you consciously said, I want to go to industry.

And I think that the timing of this, again, IBM always

comes into the conversation when we talk about quantum computing. Right. So,

I mean, they really are the elephant in the room. Yeah.

But so what would you tell

someone who is in sciences and not science and Outside sciences.

Yeah, that's a very good question. So let me start by

telling a little bit of a story of something that I, I found

within Quantum Machines. Right. So Quantum machines is a company

that we sell products to different people who

want to do their quantum computing, quantum information application.

And you know, we have some product and the product is

primarily built by engineers, to be honest, not really

physicists. So there is a lot of doing it together

actually, rather than just a physicist doing it. So.

And where is the story coming from? I know there is

an R and D department in Quantum Machines. There is engineers for hardware

development, there is engineers for software development. And

I was browsing on LinkedIn the other day and found the

post of a colleague who is part of the software team.

And he's hiring for his team, he's hiring software engineers

and he's making some little posts to debunk

that you don't need to be a physicist to work for a quantum computing company.

And what he was. And he's like making some small

cartoons here and there. And the message was like,

this is why we don't need all physicists to make a company.

And it had to do with, yes,

we have physicists in the company, but it is about working together

and not just the physicists doing it all. We still need very skilled

and talented software engineers that are going to solve this three

problems that are like pure software engineer problems.

And you know, it is just a combination of the conversation of a

very talented software or hardware engineer with like the knowledge, the

context knowledge of the physics that is going to make the final product, actually.

And that to me was really important because as

a physicist I am very good at understanding,

you know, the quantum or the application. But I cannot

program an fpga. I cannot do very well software coding.

And it is the work working together what makes it successful at the end.

So you don't need beyond physicists. And then going back

to your question, Frank, about what would you say

to what would you recommend to a person in STEM or not

in stem, right? So I would say that

there are multiple stages to join quantum

computing field. There is the R and D stage and there is the

making it a company stage. So if you want to join

effort of building a quantum computing chip

or building the algorithmic, the algorithm that is going to be

used by a quantum computer. Those at this moment require very

specialized skills, usually I would say a PhD education.

So it's like, okay, you do your undergrad in, you know, in

stem and then you pursue further, you know, computer Science, math or

physics or chemistry in relation

to quantum computing. You know, you work with a

professor who is in the area making relevant publications. That's

how you become up to speed and in the frontier of that area. And then

you join a very specialized company which are very few right now who are

only solely focused on the, on the R and D and the development. So but

that is if the person wants to pursue that R and D and development,

if you're not part of or not not have too much interest on

that part, you know, like you can pursue

either technical or not technical degree. And I

would encourage the person to look at the

companies who are a little bit beyond the research and development

of the quantum computer, but the ones who are trying to make

a company or a business out of it. Like, you know, there is a lot

of desire to have the quantum computer ready and it's extremely important.

But not all companies are trying to make an immediate like revenue that

year. So it would be important to understand which, which ones are the companies

or players that are interested in like yearly revenue,

because those are the ones who need software engineers,

marketing people, salespeople, and all of these different diverse

skill sets that will also include physicists. But

yeah, it's a little bit more diversified.

Okay, so let me ask you, we often hear both hype and doom

about quantum. How do you personally separate

realistic progress from marketing noise?

Yes, this is very, very challenging

and I think. So I can tell you a little bit of my experience

and then I will go to a little bit of a general answer

being first. So I had an education in

quantum physics and then I decided to do industry in quantum computing. So

because of this I have like, I continue to be up to

date with what happens in the research and the universities and companies. So I can

distinguish very easily what is the scientific product and

what is the story surrounding the scientific product.

So that's where I sit. So for me it's easy to

understand. Like, okay, so if I read this, this is the scientific product and this

is a story, so is it easy for me to digest? But I can imagine

this not being so easy if you are not in a position where I am.

So I would say that here, what

I would recommend is you don't need a PhD for this,

but it's a little bit of the scientific approach where you kind of read

first, don't take it as face value and

admit that it's a complete truth. But do follow up

if there is a message or a notification

that has a purpose of marketing which

exists As a purpose. It has a self contained, maybe

300 words message. You can always try to understand

where is that coming from and see where that takes you.

I personally don't want to condemn small messages

or marketing or anything like that, but

you need to read it, you need to understand where is it coming from. Then

you go to the source and maybe behind that there is a scientific

publication or not, but it's just about following

up and doing the investigation of the information

that will help you. Making the difference between what is the hype and what's not

the hype, rather than just reading something once and saying

okay, this must be true or this must be a lie. That's what I would

recommend to help ourselves on the debunking.

If someone wants to experiment today, what

platforms or tools would you

recommend for some hands on learning with real

quantum hardware or simulators?

Yes, this is a very subtle question.

I will give you a little bit of my perspective. So

while, while not when a person who has interest

in this field is not next to a

quantum computer, like for example,

let's talk about IBM. IBM have quantum computer deployed and they have

offering through cloud. Right.

If the person who has interest in learning is not like an engineer or a

scientist on the premises where the quantum computer is, it's going to have a different

learning from the person who is on site. So we have a small group of

people who next to be to the dilution refrigerator to the vacuum

chamber who can see and do the experiment with lasers and

microwave signal to do the manipulation of the quantum computer. Okay, so

that's one type of learning. And this is not accessible to everyone unfortunately.

It may not be actually of interest to everyone actually because you

know, these days the three of us could write a Python program

in our laptops. We don't need to go to the chip and understand how the

transistor work to make this programming work to us actually. Right.

So I just described the case of like working very closely to the

transistor but may not be interested to everybody. And then

we have what comes out to the content that

everybody can get access to. Right. So that's simulators. There are services

companies like IDM or Microsoft through cloud service they give

you access to either a simulator or the hardware that

companies are offering. I personally didn't do too much

of this side of studies, but I have seen out there

like IBM has some offerings that I believe are even

had some period of time for being free. And then Microsoft

cloud services has access to different

hardware systems that you can get some time on them. And then these

correspondent companies happen to have tutorials

attached to them and this is

the way that one can learn. Yes,

but it's a bit challenging, I have to admit, because

it's not fully developed the quantum computer yet. So

it's not clear that what we learned today is something that will be relevant

in a year from now because it's just evolving really fast. It's

interesting how that's become a theme in technology. Right. Whether it's

AI, like AI and quantum. Right. And I

always joke like keeping ahead of what's happening is

become what used to be a part time job, now it's almost a full time

job. And I think at some point it might flip and even

be. There's just so much happening in

both those spaces. I mean at some point it's

exciting, but at some point it's a little exhausting too. Right. Like

last year I went on vacation at a place where there was

the, the ho. The Airbnb host said

that there was wi fi or Internet, but there really was

no connection connections. So it was, it was kind of a mixed

bag. Right. Because like it was, it was, it was nice to be disconnected. But

we don't realize like how much of our world

is shaped through Internet connection. But yeah, no, it's a, that's a good

point. It is moving very fast and that's

right. I can't imagine like just what it would like to be like a

student learning this stuff today. Right now some of the fundamentals don't change that often,

but still like it's it. Like you said, like

there's no what is going to be the quote unquote

winning technology for a quantum computer is not exactly

clear just yet. Right. Like is. And

there certainly are a lot of players in this space, but

again, there's no guarantee that one

of them is going to win. But obviously I think there's certain

quantum information theory

tactics are going to be mostly the same. Right. And I don't think there's going

to be any surprises in at least not right away in the types of

problems that quantum computers will solve. And I think that's one

good antidote to hype. Right. It's not going to solve everything but just things that

have been very difficult for conventional or classical computers to

solve. That's right. Right. We've

been talking about, you know, I know Frank and I have been talking a lot

lately about classical computing and quantum computing and where's the

bridge and how one quantum is not going to

replace classical Computing, because the classical

computing is, is relevant and optimal for certain answers that

we, that we, that we need. So there's no reason, you

know, quantum is not there to figure out spreadsheets and it's not there

to figure out web browsing. Like, it doesn't, it doesn't have to.

So that's why there'll always be a place for it. But I wonder,

since we've talked about the importance of understanding classical computing

first, what does your classical background,

how does it help you navigate the quantum world?

Yes. So, yeah, I think this, this question goes

back to Frank mentioned about the fundamentals.

Yes. So, you know, right now

it's all about development of the quantum computer. And there are some

algorithms that have been proposed that can be solved with quantum

computers, and we're still on the path to answer that question.

How do you, how does a person with some

education can tackle this

always changing information flags and things being updated?

So I would say that the courses

that the most I have used and the knowledge, the education that has been, the

classical education I have used the most is just the fundamentals of

quantum mechanics and statistical mechanics and solid state physics.

And the fact that I took those courses and

went through the action of doing the problem set not only

gave me the fundamentals, but also the ability to digest

the problem and be patient and don't give up too

easily so that I can reuse this, be

patient with the problem, read it very well, don't give

up too easily, look for resources. And that is what led me to then

try to understand whatever new content is coming out. Actually,

I think this fundamental or classical education of,

you know, just physics that was discovered 100 years ago and

so on, it's still very relevant to catch up with the new things on

my field of study.

Okay. I find that there's a lot of buzz going on

around the intersection of quantum and AI.

Do you think the hype is justified? Where do you see

the real synergy happening? Yes, I,

I read about this a while ago. I didn't. I was not up to date

recently. But I think if you look at the technical terms,

what I understood back in the time is that I'm not

exactly sure for AI, but it was for machine learning. I believe there is a

lot of matrix multiplication that has to happen for it to work right.

Yeah. And then

the loss of quantum mechanics can be described by a field called

linear algebra, and that's where the matrixes are. So it

seems very natural that if you could encode information in

quantum computers and that the evolution of the

quantum behavior happens through the description of matrices.

It would seem natural that this synergy of

linear algebra in the laws of quantum mechanics and the fact that

machine learning and related fields use matrices so much, it

seems that there must be something there, right? Like it

cannot be just by chance these two things are so closely described.

So I think there was a point in time a couple of years ago where

we're talking about quantum machine learning where

like, you know, matrix multiplication on quantum computers and

the nature itself doing the matrix multiplication. So I think there was a, a

connection to that and a little bit of a hype for that.

And I think one thing that is happening is

maybe they become a little bit disconnected now actually, like

generative AI and AI models

have become so powerful in what they

want to do with their computational power, meaning

discovery of proteins, for example. They tackle that problem

with their own mathematics, with their own AI knowledge, and they do

a very good job. And there was no, no mention of a quantum

computer was not involved at all. So in some sense I could maybe think

that actually now they started to get separated a little bit more

because this AI becomes so powerful, it can do the task

of discovering nature by itself, not using

quantum mechanics laws, but it did the job. And

because the advancements of the discovery of algorithms that could

intersect with AI, maybe it's not growing as fast

as this computational power from AI. I could

say that temporarily they are not so connected as maybe it used to be

described a few years ago, actually.

Interesting. Yeah. Linear algebra keeps coming up again and again in a

lot of different places. That's what I always tell. I tell

my kids this, I tell anyone, learn linear algebra, right? You don't

have to be really, even if you're not good at it, at least be

familiar with some of the concepts, right? Obviously you want to get good at it,

but like, it's one of those things where it keeps coming up. It's also interesting

to know a couple of things. One, not

that long ago, actually before the pandemic, one of my customers worked at,

and he was interested in quantum computing and he had

a degree in econometrics, which is also very heavily

reliant on linear algebra. And,

and he said something very profound to me, that it stuck with me. He

goes, well, if you're clever enough, you can turn anything into a linear algebra problem.

So I don't know if that's true, but I think that's interesting.

And also too, if you look at how

GPUs are structured, they're basically really well designed to do linear

algebra. And I think that

we've had a number of people, Candice and I have spoken to that take

it. Most of them take a dim view to simulating

quantum computers on conventional hardware. Not that being

discreetly different from quantum inspired algorithms. Right. Like, this is actually like,

I'm gonna. I'm gonna get a, you know, a massive, you know,

a 100 or H100 machine and I'm going to simulate a

quantum computer. A lot of folks have taken a dim view to that.

What, what's your take and why do you think people are taking in kind of

a dim view to that sort of approach of simulation?

Yeah, I think it all goes back to

understanding, like, why it is so hard to simulate classical

quantum computers. So, you know,

the. The quantum computers

are based, sorry, the processing of information

with quantum. The nature, the loss

of quantum are based on two principles,

right? One of them is the fact that superposition exists, which

is the fact that you can describe an outcome

as a linear combination of

two vectors like the 0 and the 1. But

all pre factors multiplying the 0 and 1

are admissible. And it's a continuum and they're

infinite, pretty much. And then the other one is the

entanglement. So just looking at the first

one, which is the fact that you can have a linear combination of two vectors

with all admissible values in the pre factors to these

two vectors. That makes it like, okay,

so if I want to simulate, I need two and they need to range to

take all the values. But if I start to grow the number of qubits, then

I need to grow the number of information by 2 to the N actually.

So if it's 10 qubits, it's 2 to the 10. If it's 100 qubits, it's

2 to 100. If it's 1000, it's 2 to the like 1000. And

I need to somehow have enough capacity of computation and

storage to be able to describe this very, very large

numbers. And some of them can easily grow

more than the number of atoms that we have, you know, in Earth and the

universe. So it's just. It's just that it's a very. I

think it probably, if you were to talk to a mathematician, it would tell

you that, you know, doing quantum computing inspire.

Sorry, Doing computation inspired with working with quantum. There's a very

dense problem. It's in the same way that you can have

larger, way more dense number of

items between 0 and 1. If you were to consider all the

real Numbers. If you were to compare it to

all the integer numbers, the amount of items that you find between

0 and 1 is much more larger than all the integer numbers that exist

out there. So this has to do with mathematical density of groups

and it's just. Yeah, just not enough. I think it's a

very dense, dense problem when you, when you talk about quantum computer

and what is available and the classical information.

Yeah, it's just not possible. Yeah, I like that. That's a good explanation.

Because no one's ever really. They just kind of like, nah, you don't want to

bother with that. And I think that's a good explanation too. Like

there are. The number of

states or numbers between 0 and 1 is

effectively infinite. Effectively infinite. Right. If not infinite.

Right. But it's an infinite.

That infinite is larger than the infinite of integer, which is

crazy to think about. I had a migraine yesterday and

just thinking about this kind of like it either sometimes when I get a

migraine and I recover from it, like I can, I can grasp, or even during

I can grasp some of these I have with a little bit more clarity. But

like, yeah, like that's like, wow, I never thought of it that way. It's like,

that's pretty wild stuff. That's an infographic that has to be created. That

is totally an infographic. It really is. Yeah.

No, I love that. I love that. So,

so let me ask you, what role do you think open source communities are going

to play in advancing quantum computing?

So as far as I understood.

So, you know, my education is in physics and you know, we do a lot

of studies of books and research in the laboratory. But you

know, the work that we do at the university is like, okay, you are in

your research group, you publish a paper and you put it out there and

many other people is trying to do a little bit of similar research, but you

want to be the first and you don't want to be scooped. So

I'm not necessarily sure if it falls under the category of open source, but

what I understand of open source is you have a group of people very motivated,

you want to disseminate the information and everybody gets to contribute

equally and there is not just a person who is keeping all of

it. So it seems to me that the

fact that at some point the

capability of ran, for example, on a quantum computer becomes

really open to anyone and the fact that that many, many people

with different skill sets, diverse, are trying to solve different problems

and from different angles can really make it that we

find the applications faster so rather than only a single

group of people trying to crack the issue.

And I think I've seen a little bit of this in the flavor of

some companies providing a

big price for motivation, of, of a global

community, of trying to solve a few issues that are

outstanding, that cannot be resolved just inside. So I think it's

very important to give access and that is accessible to many people.

Interesting. Do you think there is a social

impact potential? Do you believe that quantum computing

can have a tangible social impact like climate

science or medicine? Or is that just still too far off?

Yes, I think the answer is yes and

yes, I think it's far and I think it's possible

to have an impact. So we don't know yet, right?

We don't know yet when and what is the application going to be.

But if it turns out that everything works out and it's a very

powerful computer to do computations, this can be

immediately used in pharmacology, in climate sciences.

And, and even without knowing that the

problem was solved by a quantum computer, we know that this can

help people, right? So yes, there's these

fields of pharmacology and climate that

will help people. Doesn't matter who solves it. And yes, I think

quantum computers at some point will be powerful enough to

tackle some of this problem and by connecting those two is how

we will benefit from quantum computer. Will be others in the future, actually.

Interesting. Yeah, no, I think, I think one of

the big problems I think we have when it comes to climate,

right, Isn't I'm a big fan of solar,

right? I even built a little solar generator. But if you

look at the pricing of solar systems now, even if it's just the camping,

like small kind of stuff, the cost of the paddles are actually

trivial now, right? Or almost trivial, right? It's the battery, the

storage mechanism and the chemical. You know, if we

had a better way to simulate kind of like what chemical concoctions

could store energy, we would solve a lot of

that problems, I mean, for many years. And also I think there's also room

for improvement in the efficiency of solar panels too.

But yeah, I mean, like in terms of just that alone would

go a long way. I think the advantages of what quantum

computing can do in material science

will go a long way to improving like societal impact

and things like that, you know, And I think

now you can argue now that even with kind of annealing

type systems, you can get

optimization of delivery routes and things like that. You can, you

can kind of. I mean, obviously it's not you can reduce the

amount of emissions and whatnot based on optimization.

I think you can kind of get some of that now. But I think the

best is yet to come. Yeah,

I agree. We have a lot to wait for

and I'm a little bit both

optimistic and not so optimistic. I do hope it

happens before it's my time to pass. I really want to. You're

in the superposition of optimism,

pessimism. Exactly. The glass is both half full and half

empty at the same time. No, I

think that. That. I mean, I also think too, a lot of people are.

A lot of people are. Again,

I live in the D.C. metro area, right. So obviously I'm going to think more,

you know, in terms of, you know, national security kind of defense

tech stuff than the average person. Just because there's just so many people around

me are in an industry. I think everybody is

freaking out about Shor's algorithm. And that's probably going to be one of the first

dominoes to go or problems to be

addressed because there's a lot of money and

a lot of national willpower behind getting that

sorted out. But beyond that. So do you think it'll take

more qubits to see? Because there's a number of

debates about number of usable qubits. I

probably should put that in air quotes. Usable qubits

there. Protein folding, I

think will take more. Some of the more material, sciencey stuff is going to take

more than what it'll take to break rsa. That's the impression

I get. I could be wrong because one of the things that's fascinating about this

space, every time I think I got my head around something or I get a

handle on something. No, it's actually not the case. Cakes or

it's like what? Like what? We learn something new every episode.

Every episode at least. And more than one thing. We learn every episode.

But really it just. Whenever I think I've got

a handle on something and then we meet somebody and

they say something and I'm like, I have no idea what. I'm. What? I. I

don't know. I don't know. Again, all of a sudden. And I've got to really

understand. It's so. It's so expansive. Sorry,

I had to agree. I had to agree. Well,

and that's what's really beautiful about the role of curiosity. Right. Like.

Like Frank said, I'm wickedly curious and I've always been that way.

I don't come from a tech background. I come from.

My father was an IBM inventor. He was A

quantum physicist back in the 80s,

the 70s, the 80s and the early 90s.

Like, he was always like, literally, like, like writing algorithms. I

mean, I had no idea what he was doing as a kid. Like, I'm 8

and I'm 10 and. And I can't tell anybody at school what my daddy

does because I don't understand it at all. Right. And he's like, writing

algorithms. Like, he was so beyond. He was so far ahead,

you know, of what was going on. But it tickled my

interest that my whole life I've been running towards technology

and now I'm like, running full steam at quantum because.

Because again, it really suits my type of

curiosity. So what's something that

you're still curious about in quantum

even after all your learning and your experience?

Yeah, something that I face quite often when

I work with customers and they start to connect their application to

what we offer. At Quantum Machines, it usually starts with

doing some preliminary measurements and then doing calibrations.

We do calibrations of their qubits.

And then once that's completed and you agree that

it has reached some level of calibration, then you start to work on

the algorithmic part, whether it's simple or complex. So

a lot of the things that I face these days are calibrations

because it's the initial stage before everything, all the magic starts. You could say,

I always wonder, I work with the customer, I do

it once. I work with another customer, I do it slightly different. I work with

the next customer. And then it's a different qubit type, and then it's slightly different.

A big curiosity that I have is

what does it take from the hardware

and the hardware, physicists, hardware engineers, for us

to achieve the best

calibrations that we can achieve. And that is in two questions. It's

like, what is the quality of the receiving end, the quality of the qubits, how

much good they need to be to achieve the calibrations.

And then the second is the operations and the routines of calibration.

So I wonder, how can we make it so that it's a little bit

better? How can we make it so that you get a little bit better

of fidelity, which is like a parameter of

calibration. That's something that keeps circle on my

brain. I wonder, we have a protocol,

for example, resonator spectroscopy versus amplitude. And then

I wonder if can we do it differently? Can we write it in a

slightly different ways? Can it save more resources? Can it lead you to the answer

faster? So these are questions that keep circling

on my brain a lot, I would say. And it's not about the quantum

application yet because my role leads me to be closer to the

hardware layer, so not too much to the algorithmic layer. And where I'm

sitting, this is one of the topics that I think the most, I would

say. Interesting. That's

exciting. Thank you.

I want to ask you about mentorship because I think it's

really important. Have you had a

mentor in this space

or have you mentored others?

How important is community in learning? Quantum?

Yes, I think I have had mentors.

I haven't whenever I thought and I said

to myself, oh, I need a mentor, actually didn't really lead me

too much anywhere because when I was trying to be conscious about it,

but when it happened, just by chance or by coincidence or

by a conversation, and in

retrospect, if I can call it that I received mentoring, then it is

when it worked, actually. And I'm looking back even beyond quantum

computing. Right. I'm talking way back from like undergrad and grad

school. So there are two things that are important for me.

One of them is receiving the information that is not obvious

from the mentor. What I mean is that the mentor,

not necessarily older person, but maybe more exposed to the

field that you want to be at, they know some insights that are

difficult to get when you are from outside. So getting that information,

passing it and making it available, that's something that what

I think mentorship is about. And disseminating this

so that you can quickly catch up to speed and know where to start.

That's great application of mentoring. And the other one is

a little bit in the community is, you know,

by the mere fact of finding a person

that has some characteristics or connection to you,

whether it's culture, genre

or type of studies or nationality, all of that just

happens you to encourage and understand that it's feasible. And

once you understand that it's feasible, that's when the barriers

just when the gates open. Pretty much once you understand

that you're not limited because someone else did it,

that's when the barrier, psychological barrier of I can do it,

it starts, the barrier removes and you can start and then

you start to find ways to get there, even though you didn't

nobody tell you how to get there, actually. Yeah. So it's really

important. That's cool.

Awesome. So we want to be

respectful of your time. We could talk for another hour,

but where can folks find out more about you, what you're up to

and your company? Yes. So

the profile that I keep is my LinkedIn profile.

That's where usually people can find about the recent things that I

am participating on and in relation

to either my personal life or my professional work.

That's a little bit about myself. And then I work for

Quantum Machines. Our website is quantummachines

Co and our.

We really want to accelerate the era of Quantum computer. That's what we're all for.

And we do it in slightly different ways and we do it through our products

and our interactions with our customers. So

people can find me at events like March meeting. It's a physics American

Physical Society meeting. It's mainly for academics but

that's where people will find me. And if I'm working,

I work with a lot of customers in everywhere. So I happen to be in

universities or different cities. And if you happen to know someone

who has a Quantum Machines product, you can probably ask for my name and see

if I'm around. That's cool. That's cool.

The industry's still small enough where you could do that, right? Yes,

yes. It's a not so large community. It really is

because like you know, I attended my first quantum in person

event like ever back in. Was it May, Candace? That's

right. That's right, it was May. And like

I, you know, introduced myself and they would be, they would either know who we

were or, or which was cool or

they'd be like, you should talk to so and so. And I'm like, I know

so and so. Like it was like it had that kind of that weird like,

like a small town feel which you know, you don't really get,

you know, you don't get as much in AI anymore. Like you maybe

you did like maybe 10 years ago or even just kind of you

know.netdevelopment which you did 20 years ago. Right. Like

it's kind of like it's kind of nice to have that close knit community

which you know, I know at some point that'll probably go away, but

it is nice to have that again, you know. So

cool. Yes. Any parting thoughts? Candace,

I really appreciate this. I appreciate it especially how you shared

your curiosity, you know, and, and told us

even more that we have to investigate. I 100%

want to have you back to ask you even more

questions. Yeah, absolutely. Excellent. Oh, it's, it's just, it's been a wonderful

time and I thank you so much for your time. I really do. I

really appreciate the time that we talked to you and it was a lot of

fun. I really enjoyed it. It was very comfortable. Thank you. Thank you. Very much,

and we appreciate that, and we'll let our AI finish the show.

And there we have it, dear listeners, a delightful detour through the

weird and wonderful world of quantum computing with the ever

articulate Kevin Villegas Rosales. From Quantum

Machines from calibrating qubits to pondering quantum

machine learning, Kevin reminded us that success in this space

doesn't hinge on mysticism or magic, just a healthy

dose of physics curiosity and the occasional

existential crisis about linear algebra. Whether

you're deep in the science or just here for the T shirts and

buzzwords, we hope you found some clarity amid the entanglement.

And if not, well, perhaps you're just in a superposition of

understanding and confusion. Perfectly normal.

Big thanks to Kevin, to our brilliant co hosts Frank and

Candice, and to you, yes, you, for joining us on this

Quantum ramble. Don't forget to, like, subscribe

and teleport this episode to a friend using whatever spooky

Action at a Distance app the kids are using these days.

Until next time, stay curious, question the noise,

and remember, in Quantum, as in life,

nothing is truly certain. Except maybe that we'll be back with more

this has been Impact Quantum. I'm Bailey, signing

off, but never fully collapsed.