Welcome back to Impact Quantum, the podcast that explores the cutting

edge of quantum computing without requiring you to own a lab

coat or a PhD. I'm Bailey, your dryly

delightful British AI guide to all things quantum. And

today we're marking a milestone. This is episode 30 of season

three, which means we've officially hit quantum stability,

or at the very least, podcasting coherence.

Bravo. Us. To celebrate, we've got an absolute

treat. Vayam Patel, a master's student at the University

of Waterloo, known by those in the know as Canada's answer to

MIT Viam's here to share how he journeyed from machine

learning to quantum algorithms, what makes error correction

more thrilling than it sounds, and why foundational maths

might be your best ally in this emerging field. He's

brilliant, articulate, and suspiciously well read for someone still

in grad school. So grab your beverage of choice, settle

in, and let's get quantumly curious with Vayam Patel.

Hello, and welcome back to Impact Quantum, the

podcast. We explore the emerging industry and field of quantum

computing, where you don't really need to be PhD or

super into the physics side of things. You just need to be curious. And with

me, as always, is the most curious, quantum curious person I know. I always

got to make sure. Candace, you're not the most curious person I

know. You're the most quantum curious. Is that that one word

changes the whole thing. Yes, absolutely. Absolutely.

And I am. I'm super curious and I'm really excited. And every time we

talk to someone new, no matter what they do, I learn

something more. And I love Get a new perspective.

I love it. I really, really do. And it just shows you how there's.

There's just so much space in this field for all kinds of folks,

and I think that's great. So today we

have Viom Patel. He is a student at the

University of Waterloo in Canada. They like to call that

the MIT in Canada. So you understand

that's. That's smart school. And we're really

excited to talk to him today. How are you? How are you today?

I'm doing good. Thanks for inviting me. Yeah, I look forward

to. To our conversations. Awesome.

So we're talking in the virtual green room a bit. The thing

that fascinates me, because you're. You're still a student, you're obviously very early in your

career, and you're already at

Waterloo, right? The MIT of Canada. Right.

You could be anywhere. You could be studying anything. Right. What made you pick

quantum computing? Because I think that's really. I think that

that's really the question, right. That gets to the heart of the matter of, you

know, obviously you believe in the field and so. So what made you pick

quantum computing? The short

answer, which I'll start with is that I find it,

it's right at the intersection of mathematics, or I

should say rather, yeah, applied math and computer science

or computational math in a way. So my bit of a

background, I did my undergraduate, I started off as a computer science

major and then I added math as a second major

and I got interested, very interested in the intersection

of these two disciplines. So earlier in my undergrad I was

working and doing a lot of machine learning research, which was also yet

another field which is at the intersection of two. But then near the

end of my program, I found I was more attracted

towards quantum computing because I found there were more opportunities to

sort of work at this interdisciplinary

area. So that was sort of the key motivation.

Okay, interesting. What in particular attracted you

to quantum.

I should say at the first time when I heard about it, I

think the whole claims of quantum advantage or quantum supremacy

or the so called exponential speed up, I think that attracted

me the most. Again, having this computer science mindset. There

were a lot of claims about you can solve something super,

super fast in an exponentially faster way. And

second thing was just doing Manda grad. It's a very common

curriculum in computer science courses. You take a course on theory

of computation where you formally define what it means

to compute. And we had one lecture on quantum

computing and they sort of described in how a lot of

the conventional ways that we are used to thinking about

computing, they are just completely different when you move to the quantum

computing side of things. So that also made me extremely curious

about this new exciting field. And that's what I ended up

going for my graduate school. Oh, very cool.

I think it would be really interesting to ask you this, and I

ask this every episode that we have and I think

it's great because we just get so many different answers.

What is the biggest misconception about

the industry that you're hearing

that you would like to address? I

think the most common misconception that is often

portrayed in, in communication in the news outlets are quite a

bit is this whole idea of you can first is that,

oh, people simplified a lot. So there's, there's

this notion of, oh, in quantum mechanics or in the quantum computing side of things,

you can just try all possible solutions to this

problem that you're trying to solve in parallel. You just solve

for all possible solutions and then you Pick the best one. That

is simply just not true. There are

just very, very specific instances where that might be true.

But for the most case, we are not solving for all possible

solutions at once. I think that's the most common

misconception which we hear quite often.

Okay. Again, I like the

answer. It's different, it's not necessarily what I'd heard here before, and I like it.

I think that's great. What's the most exciting risk

you've taken with your career

pursuing in quantum computing?

I would say that the field is

in its nascent stage. Even though there is so many big groups,

research groups, working in this area, it has not been

concretely proven from, even from a theoretical

standpoint that using quantum computing for

the problems that we are usually interested in is going to sort of give us

a speed up. That's one part. The second

part is the hardware is also not there yet.

Companies have been telling it will be there in the next five years, but

I think they've been always studying that. And we have made

tons of progress, extremely great progress in the past two or three years.

But again, these are the two big things. One is the theoretical guarantees

on whether we will see a speed up, and second is the practical

realization. So if these things don't pan out in, let's say, the

next decade, then I would say that would be the

big risk. Short term or medium term risk. I would

say from a career perspective, because at the end of the day, if you're

looking to find a job and if the, if the field does not

progress as, as, as some of the other fields did,

then that would be sort of a biggest risk.

So how do you tell the difference between hype and

true innovation? Because you have to admit, we're hearing new things every week.

We're hearing new things every day sometimes. So how do

you tell the difference?

I think I'm fortunately in a

position that I have been surrounded at,

at the University of Waterloo with some of the best researchers in this field.

So I got the, got the opportunity to take very

advanced courses with them. And the courses were

designed in a way where they, they go back all the way to the

fundamentals and they would teach you a lot of these things from a

rigorous standpoint. So when some big news come out for

me, I almost always just ignore it,

unless there is a link to the paper which is being published. Because if

I can see the results in the paper, I

just cannot believe news or a blog post. So that's

sort of like the filter one for me. And once I find the

papers, then if it's one of the big, one of the big companies

like Google or I nowadays, they are doing exceptionally

good research. So you can get some good idea by reading the

introduction and conclusion of the paper. Even though the paper might be 50 pages,

you don't need to be an expert and read through all the 50

pages. If you just get an idea by reading sort of the abstract

intro and conclusion. So that would be like my first two

filters and passes. And then if I do find something that

aligns with what I think might be, oh, this could be interesting, then

I'll just skim through the paper. So that's sort of my process. So I think

the last part skimp through the paper. That would require

some technical background, which I was fortunate enough to have. But even if you are

not necessarily an expert, I think my first filter is, is there like

a technical paper which was released with this, with this news

announcement. One of the things you mentioned,

and I'm just curious, like, there's obviously a lot of papers getting published.

Right. How do you keep up? Right. I mean my, my hack for keeping

up is I feed all the PDFs and each research

paper gets their own notebook. Lm right. So I can kind

of like have a podcast explainer. So while I'm driving around, driving the

kids around, I can. Well, you don't. Maybe you don't have kids. So. But like

there's just so many demands on my attention and time. I find using

AI, you know, everyone's all freaked out about AI is going

to make students cheat. I use AI to help me

learn. And I would imagine, I would imagine that

it's probably more widespread. Most certainly. We didn't really have

AI when I was in university. Actually, that's not true. We had something called

Prologue, which was this. Yeah, you're laughing. Yeah,

like, you know, but I mean, cut us a break, you know, like the, the

ice age had just ended and, you know, we just invented fire.

Right. I

love it, I love it. I've used Prologue. I enjoyed my time,

but that enjoyment lasted three months for the, for the course that I

was taking. Yeah, that's sounds about right. Yeah. Yeah. I remember

my final project to this day and I'm like, that was an awful

lot of work to parse the binary tree. Yes. Yeah.

Sorry, go ahead. Oh, yeah, I was just going to mention.

Yeah, I think for keeping up with the papers. Yeah, I think

so. My filter. Well, again, because I work in one

or. I'm mostly interested in one or Two parts of the quantum algorithm side of

things. I, I have this daily ritual. Just every

morning with the coffee I just go to cite. Usually there's less than

20 papers uploaded on the archive

for quant ph. Sometimes it might be 30, but usually I just

skim through them and if the topics are more algorithmic I just open again,

read the abstract intro and conclusion and from there I can figure out if I

need to dive a bit deep. I did try

NotebookLM at some point when it first came out.

Unfortunately it did not work for me. It was

just not accurate enough even to give a high level summary.

I have not tried it since this was a few months

ago, I would say six to eight months ago. So maybe things

have changed quite a bit since then. But I think, yeah, I

just, for me I'm very, it's very easy and fast for me to just skim

the abstract and then just know whether this is worth my time.

That's cool. So is that how you keep up then essentially on

the industry trends and the new tech?

Because if you feel, if it has a white paper then it's fairly substantial.

Yes. Although I think, I think because I still

like a grad student working research, I think I would prefer if it was

not necessarily a white paper but like a proper technical paper that

gets published in a peer reviewed journal. So sort of

that's like published. Being published in a journal obviously takes

time, but usually pretty much everyone uploads their papers

nowadays to arXiv. So that's the open source website where you can

just go and filter by quant ph and all the papers published. And

even though the, even though the tag is supposed to be for quantum

physics, I think nowadays it's mostly just filled with quantum computing related

papers. Right. And for those following at home it's

spelled. If you want to check this out, it's AR X I V X. Yeah.

Yes. I

don't want people searching around being like I couldn't find that site you mentioned.

Yeah. Cir8 is also an alternative. Most papers from

archive get posted to cite. The nice thing about cite is that

you can write quick comments on if you have some questions about the new

result. It could just be high level and the authors themselves would

usually respond. So that's a very quick way to sort of interact with them.

So I usually use cited as well. And there is a way where the most

like interesting papers people will like cite them and they will just

climb to the top of the top of the sorting algorithm. So

that's quite nice.

So what Are some long term goals like that

you have for what you want to do after you

finish with grad school? Where, where do you want to

situate yourself in the, in the quantum ecosystem?

Or is it even too early even ask that question? Oh, that's fair.

Sorry I cut you off. Yeah. I think

for me I've, I've realized that I find

two areas quite interesting. So broadly speaking, they are

quantum algorithms and quantum error correction. Now of course, like that's,

that's way too broad. So specifically quantum algorithms

for problems that arise in solving differential equations.

So differential equations are one of the most common ways where a lot of these.

Weather prediction is the most obvious example. But also

nowadays there's a lot of simulation work going on and

designing the aircraft, simulating the flow around the

aircraft wing. These are very computationally challenged, challenging

problems. So my sort of interest through my research

project went into this field quite a bit and this is a

fairly, I would say new field on

applying quantum computing to like cfd or

numerical PDEs in general. So that's one very specific area that

I'm interested in. And the second being error

correction. Again, error correction is a way too broad of a field. Where

I see myself fitting in is

translating a lot of these academic papers

to code implementations. I think that's a big

missing part. That also helps me a lot

accelerate my research because every time a new idea comes in, but if I cannot

quickly prototype and test it out, it's very hard to gauge whether

this is applicable or not applicable. I think I've gotten

used and good at implementing a lot of these latest

algorithm papers that come out. So that's

somewhere that's a nice intersection that I would like to be

in the short or medium term, I guess.

Okay, cool. So what, what do you think, what is

the. Do you, do you have a. So you're a post grad student

PhD or, or somewhere else or. I'm a

master's student currently. Okay. Yeah, second year.

Cool. What areas do you think you're going to focus on

in your research? I think

short term I would still be focusing on

applying the quantum computing, quantum algorithms to

problems as I mentioned, and differential equations specifically. So

I think that's the short term focus again because

it's a fairly new field. I think there is a lot to be done and

a lot of the conventional. Because what we are trying to do right now and

when I say we, like a lot of researchers in this community is we are

used to thinking of solving these differential equations in a

classical way. So we are just trying to map these classical

ways of thinking to quantum computing. And then we have realized that

it does not always work out. In fact, it almost always does

not work out. So we have to go back to the fundamentals. We have to

rethink the way that we've been thinking about solving these problems

classically. Because on a quantum computer even some simple

things are not allowed, like nonlinear simply computing,

like X squared. On a classical computer you just make two copies and multiply them

together. On a quantum computer it's not possible

really. Like I'm simplifying things, but that's sort of the idea.

So this pushes you down to go back to the fundamentals, sometimes

rethink the way of mapping your problem and then bring

it back to the quantum computing side of things. Things. So I think I enjoy

that process a lot and I think I would in the short term would like

to continue working in that field if, if, if given the

opportunity. Now this may be me being a software

engineer by training and comp. Sci major by training. And

I think that's really going to be a big growth area. Right. The writing the

code. And at a, at a very

fundamental level is going. You're right. Like it's going to be different. Right. There are

new ways to approach problems. You have to kind of drop

your old way of thinking. I think the quote Yoda, where you have to unlearn

what you've learned. So

I think it's interesting because

that means that there's going to be a lot of code that will need to

be rewritten. Right. And not like you know, hello world type stuff.

I mean core underlying algorithms for

search and you know, all of those things

are going to be, have to be recoded from the get go. And you know,

that's not exactly, it's not exactly exciting work in one.

Right. You know, bubble sort is not really

the most fascinating algorithm in the world, but it's kind of

where everybody starts I think with quantum computing. I think we're going to have to

revisit a lot of our underlying assumptions

around computer science. Yeah, that is

true. I think in fact a lot of my, a big part of my research

area was to like there's this notion of

block encoding. It's fancy way of saying how do you encode

classical information onto a quantum computer? Now

classical information is on quantum computer you're only allowed very

specific operations. Technically they're called like unitary operations.

Most of the classical operations that you would want to do are not unitary. So

you have to make them unitary somehow. Right. So a lot of these

algorithms is, they assume you know how to do that. And then the algorithm starts,

then the paper starts. But some for, for someone like me who's

interested like in the next, in applications in the next five or

10 years, I'm like, how do I, like how do I do that first part,

which is assumed to be true. So a big part of my,

my thesis and my research area was basically that how do I encode

this matrix, classical matrix, onto a quantum computer?

And turns out this is an unsolved problem. So I

focus on very specific structured matrices that I, that we

see a lot very commonly in numerical analysis or

numerical mathematics. And my, and the approach that I

took is like, I had to go back, dig through these. Apparently the

turns out you can automate a lot of this if the code is written in

a way such that it identifies these repeating structures in the matrices.

And the way I was able to do it is I was, I had to

go back to the old circuit design

books from the second year of computer engineering or computer

science and like rethinking how to add two numbers together,

how to, and how to do these things on a quantum computer.

So tracing back through all of the existing sort of literature,

that's sort of where we are now. So, like, we are very early. But

it's also exciting that in a way that what. There's

so much, so much things, so many things that needs to be figured out.

But it's also for me, like an exciting path coming from like a computer

science background as well. That's true. Because when you, when you're in

computer science, a lot of these basic fundamental problems have largely

been solved. Right? Yeah, like bubble sort. Right, I'll pick on

sort. Right. But like with quantum computing, no, I mean, we're still early

enough where they're naming things after the researchers who find them. Right. So the Shores

algorithm, Grover's algorithm. Right. I

don't know. Like, you know, maybe there'll be Patel's algorithm. Right. Like, I mean, it's

totally, it's totally within. But you know, in traditional computer

science, I think those days are probably over. But in quantum

computing science, like, I mean, I say that in jest, but

it's totally possible. Right? Like, yes. Yeah. You know,

I think that's exciting. Right. Like, we really are in the frontier. And, you

know, the frontier is exciting, but it's also kind of like, oh, no one's done

these fundamental things yet, you know.

Yeah, definitely. There's a lot more opportunities to Sort of do,

like, you could just come up with a completely different

sort of background, slash, mindset. And all of a sudden you just have like, just

thought of something that no one else has before. And because we are so early

in this field, it would be like, oh, you just stumble across a

new algorithm and yeah, maybe, like eventually, as

you mentioned, maybe it gets named after you. Right.

It's totally, totally believable, which is exciting. Yeah,

it seems like. No, no, I'm just kind of amazed by the skill set

you're talking about, you know, with the

computer science and then talking about math and

then, you know, I'm, I deep research.

Like, what do you think are the, the necessary, the

necessary skill set to have when

working on, you know, like, for example, you were talking earlier about error

correction. Like, I'm just kind of curious for people who have some of those

skills, but maybe not all of those skills, how they could, if they could kind

of break in and be involved, what kind of skill set do you think

you really need? I think for. It's

really important to get the foundations

strong. Luckily, the existing curriculum,

existing undergraduate curriculum is actually very well suited

for this. Unfortunately, I would say that

a lot of it is taught in a very. So to give concrete

examples, let's say undergraduate mathematics. So

error correction, that's a great example. All undergraduate curriculum

programs, they would go through these courses on linear algebra,

abstract algebra, where they would cover group theory and brings in fields.

Now group theory and rings and fields. It's not necessarily like a new topic.

They also form the foundations of cryptography. And we have been using

cryptography for like six, seven decades now. Turns out

the same foundations behind cryptography, the group theory and rings

and fields. That's exactly what 90% of error

correction is. And to me, this was

surprising because I took this course, an advanced course in my grad school,

but when I took the course, I realized, oh, this is just 90%

flashbacks to third year. But hey, it's been three years now, so I

need to go back and get to know a lot

of the fundamentals. But I would say like linear algebra

would be sort of step zero. And luckily that's covered in

most of the undergraduate curriculums and STEM programs.

So math, physics, computer science, engineering, and then if

you want to get into some specific fields, such as correction, I think having

a math background definitely helps. Working in

algorithms, I think having somewhat of a computer science background

could help. So yeah. And physics, again, if you're

interested in error correction from a hardware, hardware side of things,

physics background can Also definitely help quite a bit as well.

And yeah, and I would like to mention like before starting my grad school I

did not have any background in quantum computing at all. So it was

a very new field for me as well.

So yeah, all that I know I've essentially just learned in the

past 2ish years, I would say.

So, yeah, like I come from the, a very common journey

that many people in this community who are new, they

also come from different backgrounds, they don't have a formal training in

quantum computing. But I don't think that's, that's an

issue. I think that's. That, that is. Okay.

That'S a good point. Right. This is, this is a relatively new field. It's been

around in one form or the other since the 90s, right.

You're Candice little known fact. Candace's dad was an IBM

researcher working on this in the 80s and

90s. Right. So like this is not, in some ways it's not

new, but in a lot of ways it's new to a lot of people. Right.

So you're going to find I think a lot of people just like, you know,

when I was early in my career, there were not a lot of computer science

people like in industry, right. Who had comp. Sci majors. Right. A lot of

them were people who had other degrees, be they science, even a

couple of history majors that had learned to

code. Right. As much as I hate seeing that phrase because it was

so abused.

But you know, they had, they kind of realized like, you know, I

can. And I started my career on Wall street. Right. So there were a lot

of also finance types that figured out that, hey, you know, I could be a

stockbroker, yes, I will make a lot of money, but I will have an ulcer

and a receiving hairline by the time I'm 29. Or I can kind of have

a more leisurely pace and do

coding for the, you know, write applications for the. Yeah. For the

traders and things like that. So, you know,

I also think that because this is a relatively new field, very, very much in

its infancy, you're going to see a lot of people that you're not going to

have kids go to, you know, kids. Right. You know, you're not going to have

people go to school and come out with a quantum, you know, a degree in

quantum computer science just yet probably

in about 10, 15 years. I think that'll be a thing because it always starts

off with grad, you know, grad, grad school type programs and then

eventually it filters down Into a thing. Yeah, but I'm very glad you said that

because that was my advice to, to my oldest child

is taking physics and calculus and math and things like that.

Because those are hard subjects, right? Well, it's not like those are hard

subjects. And because it's hard, very few people are going to do it,

right? And because very few people are doing it, market

forces being what they are, there's not going to be a lot of people doing

it. And our entire society or entire civilization relies on

a lot of the fundamentals of physics and

mathematics. And it's alarming in a lot of ways that a lot of people

don't know it. Right? So, you know, you will automatically be

kind of whatever that looks like, Right. I used to say learn to code, kids,

learn to code. But I think, you know, the last, you know, developments over the

last, you know, couple of years have really been like, yeah, maybe you should focus

on more than just code, right? Yeah, yeah,

definitely. In fact, I think so. It's

sort of like a two sided coin in a

way because when I first got introduced to quantum computing, it was through one of

those IBM summer schools, which I think was a great

way to bring a lot of people into this field.

But to your point, I think there is a big

misconception that you could just.

Don't get me wrong, I think it's a great way to just start learning through

being able to write these code, to generate circuits, simulate them.

But that's it. Like you, all you're doing is just following a

tutorial and quantum computing. Like even though someone

might make it sound that anyone can get in and it's very easy, all you

need to do is basics of coding. That is not, simply not

true. If you want to make any good progress, you would need to know

the foundation, which as you mentioned, math, physics and even computer

science. When I say computer science, I don't mean just coding, right? Like

we are in the era of GPT. So I don't think coding

is, is, is that much required now. But I think what

you do need is strong foundations in algorithms or like these,

and these are covered in undergraduate curriculum. I think time

has come that if this feel as it progresses, I think more

people will start hopefully focusing back on foundations,

which I think is very important because without that you are

essentially just writing code, but you don't really

know what you're doing. In a way it becomes very hard

to make progress in the field, especially when

you want to work on the state of the art applications or

even research In a way, I think that's. A good

way to put it because I think in popular culture, or

we've confused computer science degrees with learn to code. Right. Yeah,

those. I mean, it's a subset of a much larger thing. Right. You know,

how computers actually operate. Right.

And I understand why it's easy to

get those confused, but I think we do ourselves a disservice if we continue to

do that. Right. Because it'll be like, you know, a lot of computer science major

departments are. They're seeing shrinking enrollment. Right. Because of the

chat kind of situation. But

there's a lot more to it. Right. Like, somebody still has to understand

how networking works. Right. How the packets work. Right.

One of my favorite phrases, Candace is probably sick of hearing it. Right. Someone has

to rack and stack them. Right.

And so, yeah, no, I mean, computer science, if

listeners take nothing away from this other than that you're a smart guy

and Candace is sick of my jokes, it's that

computer science is more than about coding. Right. It's an entire

academic discipline heavily rooted in math, but kind of, you

know, branched off for very specific problems. So. Yeah.

Yeah. Let me ask you this. Has the idea of

mentorship affected you

in your path, your learning journey so far? Have you had

a mentor that's really inspired you? Are

you interested in being a mentor to other people? I'm

curious about that. 100%. I think I have

the. I have the usual story of. You will hear this

from most math majors. You had this one professor

in there in their undergraduate. In my case, I was fortunate enough to

have Professor Steven Ryan from University of Saskatchewan. He

taught me. Yeah. I took vector calculus in my second year.

Ever since I took that course with him, I just, like, became. Not just

me, everyone else in the course as well. They just became fans. But

lucky for me, I got to be more than fans because I

got a chance to do two summer research programs with

him. I graded his vector calc for two years

straight. And I also. He was the one who sort of.

He knew that what I. What my backgrounds are and what my interests are.

And I was. I was this close when I got my

grad school offers because I was 50. 50, but leaning more towards machine learning.

He was the one who said, I've known you for the past three years. I

think quantum computing would be the right field for you. And if you don't think

you're convinced, do a summer research project with me. And he knew

my background. He's just one of them, one of those people. And he I did

a research project with him over a summer and I knew right there that,

oh yeah, this is the field that I, that I want to be.

And yeah, without him, I don't think I would have been able to

like, make it to grad school at Waterloo at

all. And I have taken. Tried. I think I see

him as like, yeah, inspiration. And also he was a great

mentor. And I've been trying in different

roles to sort of be a mentor to

other students if I can. So in grad school you get a chance to work

as a ta. So that's one very common,

common way to interact with first, second year students. But at

Waterloo, they have a faculty of math, which is very unique

to Waterloo. So it's not a department, it's a college of mathematics. So

computer science is actually part of college of mathematics at

Waterloo. So that's very rare. So math is sort of one of the biggest

strong areas at Waterloo. So in the college of math they

have a program called directed reading program and directed research

programs. And the idea there is grad

students, masters, PhDs and sometimes even postdoc.

They act as mentors and they can propose reading projects.

And the undergraduates, from year one to year four, you

will get paired with one or two students and you have four months, essentially a

term, and you will assign, you will do readings together. You will assign

the students the readings and they would read it. You would meet every

week and you. They would sort of ask questions. So this gives them the

ability to learn something new which is not part of their

curriculum, but also it gives me the ability to sort of share

some of my experiences and knowledge with them in

a mentorship sort of role. So I think I've been.

So this is the second term I'm doing it. I did

that last term as well, but it was the same topic getting first years

or second year students into quantum computing. And then this

year, this term I'm doing it again. So I think that has

definitely been a big part of my

undergraduate slash grad research curriculum.

Interesting.

Is there a book or a podcast

or simply an idea that

you've come across in the past year that has really

changed the way you think about something or

affected you in a way that you want to.

Did you'd want to talk about?

Yes, I think the one book that comes to mind,

it's called, it's a fairly famous book. It's called Quantum

Computing Since Democritus. It is by Scott

Berenson, who is like, no one is

going to. I think everyone would agree that he is one of the, if

not the most smartest researchers in quantum

algorithms, quantum complexity theory in the world. Like

period. He has, he has worked under so many great people

and the work that he's been doing for the past three decades is just phenomenal.

He maintains his own blog post where

like a lot of the questions you asked in the. During this conversation is about

how do you know if a new news article is worth

reading or not. I go to his blog post

because he is one of those people who would just go and he would

lay out the truth as it is. And he is someone

that you can just trust without like just blindly trusting.

But he wrote this book in 2013 I believe,

which was based on the lectures that he gave as a postdoc at University

of Waterloo from 2007. But this book is written in a way

which is at first pass when you read it.

And if you try to do the. There are many exercises

I could barely do that. These are not like math

heavy exercises. These are very thought provoking exercises. So if you're not

used to thinking the way that the book that the book is written,

it would be very hard. But the, but this book sort of just

opened my mind in a way like what does it even mean to

compute something? How and why quantum mechanics

is so different than classical computing. And he takes an

approach where you don't need to know any quantum mechanics. All you need to know

is if you know, if you come from either a computer science background, that's what

he, that's the background he comes from, or if you come from a pure

math background, you can still know everything that all the

foundations of quantum mechanics. In this book, it's, it's mostly

not math. There's, there's very less math, but it's written

in an extremely thought provoking way. And like every, every year I try

to come and read again and I'm pretty sure I still don't understand

all of it. But he talks about everything. He had, yes chapters

on what it means, the implications of quantum mechanics to. Through

something like time travel. And

this is not like the sci fi time travel. This is like

concrete formal implications.

If time travel were true, can you solve things that a quantum

computer cannot solve? And he has mathematical arguments to sort of

go through this. So I would highly recommend that book, I think to

anyone. Not only if they're interested in quantum computing, but in

general, I would say. No, that's, that's a good point. There's a lot,

I'm sorry, I should. Say just for our readers. Again, say the name of the

book again. Yeah, it's Quantum Computing Since

Democritus by Scott Aaronson. In fact, if you just

Google Scott and some blog, it will take you to the blog post. One

of the things on the title of his blog post says if you don't take

anything from this blog post, take away this. Quantum

computers do not solve everything in parallel. That's, that's in like the,

in the title of his blog post. It's like that's the biggest because

I think he also ran across the same thing. It's like many people

have this misconception, so he's also out there trying to sort of do

his. To do his part.

Go ahead, Frank. Oh, no. So, Leah, there's a lot of interesting things that

they're. The retro causality was something I

heard about, which kind of implies, if not time travel, kind of a reverse

load of time. But I, I'm. I'm out of my depth

beyond saying those sentences. But it's an interesting concept,

right? Like the way we perceive what we call reality

may not be the final word on how things actually work.

Right. Yeah. Which is very fascinating. Like, I'm. I'm a

philosopher at heart. So when I hear there's certainly

aspects of.

Aspects of a lot of

these kind of quantum computing and kind of

quantum physics, things that really kind of bridge those worlds of hard science and

kind of philosophy. Right? Yeah,

yeah. And this book will definitely, like take you to philosophy as well.

So. Yeah. Highly recommend. Cool. I'm gonna order

it. What would

you say is the most recent innovation that we've

all heard about? If it's willow

or. I always pronounce it wrong, Frank. I pronounce it

magero, which is the weight loss drug. Yeah, that's what it is.

Majorana. Majorana, right. I think it's named after

somebody who's German or Spanish. So the J becomes a Y. Yeah.

Okay, so out of all like this, so much out there.

Right. Yes. What to you is the most exciting

right now? To me, I think

there is this subset of error correcting codes known as

QLDPC codes, quantum LDPC

codes, which are. Which have very nice

theoretical properties. That has great implications on

error correction. And it makes the resources. Because

at a high level, the way error correction works is you have a lot of

noisy qubits and you reduce and you essentially use

like 100 noisy qubits to maybe simulate two or three

perfect qubits. That's the rough idea of error correction. And

I think QLDPC codes, the rate, which is the

number of noisy qubits you need to simulate a

certain number of logical qubits that's quite

high. So they are quite appealing to me. IBM

is sort of taking this approach for error correction. So

that to me I think that's one of the most interesting

areas that I'm looking. And qldpc, again,

LDPC codes are not new. These are first discovered in

1967 and they are used currently in

5G communication in our mobile devices. So

now the quantum version of these LDPC codes

are sort of one of the things that many people believe is state

of the art. Interesting.

It's funny how it all comes back to a lot of research that's already been

done for conventional computing. Right. And correct me, I'm wrong, error correction was also a

big deal in early hard drives as well as CD ROMs. Right. And

DVDs. Right. Because that's why if you scratch it, if you

scratch a CD up to a certain point

obviously, yeah, like it'll still solve it. Right. And

I remember there's error, there's error correction, a lot of

things like your credit card numbers, right. There's a lot like they have to match

that to a thing. I don't know if that's for error correction or for other

reasons, but even like barcodes, right, Barcodes. That

last little. I, I used to work on an E commerce site and,

and like I remember, I forget how I got involved with

like we needed to replicate the algorithm from what was

originally written and we need to rewrite it in Perl, of all things.

And I remember that the last digit is

actually a checksum which is kind of a. That's the dollar store

version of error correction that you're talking about. Yeah, yeah.

So yeah, error correction, like classical error correction is what

enabled classical computers to function

basically like the field of error correction, slash

compression, slash information theory. It started in I think

50s by Claude Shannon, by then pioneed by Richard

Hamming and a lot of these classical error

correcting scientists. But yeah, nowadays it's basically

everywhere. In all wireless communications, in something as simple as a

CD roam and hard drives, even the transmission over

the Internet that has a certain notion of error correction

inbuilt. So now a lot of these same principles are

being translated over to quantum computing. And in fact most

of the codes that are being sort of researched now, they, they had

their origins, they are classical codes at the end of the day. But they,

but they have been extended to work in this because in the

classical codes you just have one sort of error, a zero changes

to a one or one changes to a zero. But on a quantum computing you

also have this phase which is you can have

essentially a continuous phase along with the

bit flipping between zeros and ones. So fancy way of saying you have

more errors to correct. So your codes have to be much more complicated

than just the classical error correcting codes. But that's a good starting point

that most researchers build from.

Cool. How would you describe what you

do to a 10 year old? Ah,

okay.

Yeah, I think to a 10 year old they have

probably heard that weather prediction is important

or at least heard of predicting weathers of weather.

So I would say yeah, I work on,

I work on designing ways to that

could help accelerate the process

of weather, of weather prediction, whether it be more

faster or more accurate. And the way I do it is

using quantum computers. I think

that should work.

I appreciate that, thank you.

How would you explain this to,

how would you explain quantum computing to someone who is

looking to invest in these companies?

Oh, I'm just curious. Not, not like you're pitching them, but like,

let's just say you had a friend who's a venture capital and over coffee is

like, hey, I've been hearing about this quantum computing thing. What's the deal? Is

it a thing? Is it not a thing? When will it be a thing? I

know, I know. The when will it be a thing? Is a very controversial question,

but needlessly controversial in my

opinion. But it is what it is.

Yeah, I think from an investing point of

view it would most likely come back to

the whole who gets there? Well, there are two

questions. One is who gets there first. But the second is

as of now, pretty much all companies are taking a different

route. At the hardware level. Someone is doing

superconducting, some Microsoft is doing topological

qubits, then we have ion trapped. So everyone is sort of

trying different architectures. So the question becomes like, yeah,

it's not just about who gets there first, but it's also about once

you get there, are you able to scale it up such that you can

make bigger and bigger computers? Because that's what we need at the end of the

day to make it useful for the applications that we

have in mind. I think it would come down to

can one identify from a

technical point of view which one of these architectures

are the most appealing and they have

the most promising future? I

think to me that's what it comes down to. And

correct me if I'm wrong, but also each problem, each type of

hardware is ideal for certain types of problems. And I think one of

the temptations is because electronics, you know,

silicon substrate and all that has kind of become the dominant form of

computing and conventional or classical computing. Do you think it'll ever

collapse into one type of hardware in the future?

Not anytime soon, but. Or do you think it'll always be kind of like

somebody gave me the example of a. Well, you know, it's a bit like car

engines, right. Like, you know, there's, there's diesel, there's gasoline and there's

electric. Right. And yeah, there are some other things like

fuel cell and all that. But you know, most people have a

gasoline, some people have diesel and electric. Right. But ultimately,

you know, no one, while one does dominate, it never like

fell into like one size fits all.

Yeah, I don't think it will ever reach that point where it's

just one type of architecture is going to dominate and everyone

else is just gonna just, it's just going to follow that path.

I think each hardware, each hardware architecture has its own

pros and cons as usual. So I think it would

still be sort of. We would get a whole suite

of different architectures. One, maybe one architecture is more

suited towards a particular applications, particular types of problem

and then some other application might have some other properties.

So I think, yeah, I would imagine it. I don't think that it

would just all collapse down to a single architecture.

Interesting. Where can folks find out more about

you, your research and what you're up to?

Yeah, I think the, the easiest way would

probably be LinkedIn. I do plan on starting

my own website pretty soon after I graduate. I

think what I would like to do is start a technical blog.

But this would not be a short, short block. This would be a detailed

blog that you would need to sit down for two and three hours. But if

you do, you can avoid reading these 30, 40 page

papers because I think in my research I had to read

so many papers and then after a point you realize that oh,

this was, this could have been expl. In a much more simpler way.

So I think that, I think that's my goal to write things

in a way such that an undergrad with STEM

background can, can get it. So yeah, I think LinkedIn would be a

short term, short term sort of way to connect. But

I think eventually in the next few months I will start my own

website. So I'm hoping that I can get more people engaged there.

Very cool. That's perfect. Honestly,

I've already ordered the book on Amazon

that we spoke. I think that sounds absolutely fascinating and I

really appreciate hearing your perspective

from the graduate school level to see you're

jumping off into this whole realm and what do

you care about? What's exciting? Where do you want to go with it? And I

appreciate you sharing, you sharing your journey and your

opinions with us today. And I picked it up on Kindle.

There you go. Because I can get it now.

And we'll make sure we put a link in the show notes and

we'll let our AI finish the show. And that, dear

listeners, wraps up episode 30 of season three, can youn Believe

We've Made it this Far Without Collapsing into Quantum

Decoherence? A huge thank you to Vyam Patel for

joining us and proving that not all quantum researchers

speak exclusively in equations. Whether you're here for the

maths, the metaphysics, or just trying to sound clever

at parties, we hope today's episode helped you inch

closer to quantum enlightenment. If you enjoyed this

conversation, and frankly, if you didn't, I'd question

your taste in podcasts. Make sure to follow rate

and review Impact quantum wherever you get your audio fix.

Until next time, keep your state superposed, your

entanglements professional, and remember, in quantum

computing, as in life, it's all about finding the right

algorithm. Cheerio.