In this episode of Impact Quantum, we chat with Michael

Magid, a doctoral candidate at Binghamton University

who's knee deep in the wild world of quantum AI. From

Norwalk to near zero temperatures, we cover everything

from how quantum computing could revolutionize medicine to why you

probably still don't understand what a qubit does. And

that's okay. If you've ever wondered what system science

is or thought quantum curious sounded like a

personality trait, this one's for you.

It's not just Scrodinger's cat that's confused.

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

emergent field and ecosystem of quantum computing.

And you don't need to be a PhD to play along. We believe

very much that all you have to do is bring some curiosity with you and

maybe a math textbook or two. With me is one of the most

quantum curious people I know, Candace Kahooly. How's it going, Candace? It's

great. Thank you so much. I'm very excited because today we're

talking to someone. We just went down a little memory lane back to where

we both grew up, and we basically were neighbors. It was very, very

exciting. A little bit of Westchester county love for those. That's right.

Which is funny because IBM, if memory serves, is headquartered in

Westchester county, and I think they're super awesome.

Quantum lab that I'm trying to get a tour of is up there.

I went to university just south of Westchester county

in a wonderful part of the world called the Bronx. And

the boogie down. The boogie down and. Yeah,

so I'm somewhat familiar with Westchester County. Well, who are we

speaking to? We know where he's from, but we know who it is. Right. So

today we're talking to Michael Majid. He is a doctoral

candidate at Binghamton University, and we're very

excited to speak with him today. Hi, Michael. Hi,

Candice. Hi, Frank. It's a pleasure to be on this program. Thank you both for

inviting me and thank you again for this opportunity. Awesome.

And what is your PhD in? So my PhD is in the

field known as system science. And within that field, we're able to

do a significant amount of work in different data science related

fields. System science, in the end, is just the science of systems,

which is a bit of a weird way to

explain it. But if we're going to be talking about any

in parts that are interrelated, that can be

itself a system. So what we do is we take a look how all these

parts are interrelated and find how

they're interrelated and why they're interrelated. My specific work is in

quantum artificial intelligence as well as quantum information

inspecting how different quantum networks, as well as how

we can use system science techniques and data science techniques

for developing quantum algorithms.

That was described so beautifully. I just.

Bravo. Like, I. When you.

In this world, in this world, like, you know, in the quantum

world there, it's really, really hard to,

to, you know, make these positions that people

have understandable to people who are outside of the field. But just

listening to how you were describing it, I was. I was with you

all the way. And that was. That was fantastic. I appreciate

it. Could you do me a favor and hold those thoughts and give them to

my professor so he also knows that I can do it?

Well, I think that's important. Right. There's a lot of people who are in the

quote unquote, hard sciences or even the soft sciences. Right. They just

can't explain it to like the layman. Right?

Yeah, it's. Yeah, go ahead. I didn't mean to cut you off. Oh, no,

no worries at all. It's the difficulty of

zoom interviews is this the

teaching itself? And understanding how to connect with people

in itself is a skill. And I'm very lucky to have the

advisors and professors that I have because they are

the best teachers I've ever had. They have

shown me not only what it means to teach, but also to love

teaching and to help people understand which is the core of what teaching

should be. It's not just, here's a textbook, let me throw some stuff at you.

The idea is, why should you know this? How is this related

to what you know in general? How can we help you understand this a little

bit better rather than trying to figure out what is the best way for me

to disseminate this information quickly?

Interesting. Such a spokesman for it too. Like, I

could see how you could really communicate with,

you know, college kids and to really kind

of explain to them, you know, why, you know,

what you're doing is exciting and, you know,

actually. And to that point, like, what would you want to kind

of advice would you want to give if, you know, these kids want to

get involved in the quantum ecosystem?

So for, let's go with both kids and basically anyone who wants to go into

the system, not having that much background into it. So

I started this coming from a biomedical engineering perspective.

My previous master's was in biomedical engineering, so I already

had a science background. I already had some quantum understanding because of the

chemistry classes as well as the chemistry that I would do

Part of my degree and part of the jobs I had as a biomedical engineer,

the main thing to understand with

quantum is that the best way to explain it is a.

Prof. There's a. I believe there's a viral video of a professor who's teaching

quantum and he basically said,

right now I don't know anything about

quantum. And by the end of this course, none of you will know anything about

quantum. Which is a

beautiful way to put it because quantum in itself is a

different mindset of trying to understand how this stuff works.

What I'm saying is that if you don't, if you feel like you don't understand

it, you are learning. If you feel like you understand it, you are

ignoring something. And this is a good idea with a lot

of higher level math concepts that I found. And I'm

saying this not as someone's like, oh, I know this stuff. No, I don't know

this stuff. I went through the struggle, so learn from my mistakes.

This. There's a lot of high level stuff that is

in quantum and data science and all this. It takes a long time to learn

and it takes a long time to truly understand it. Never be afraid to ask

questions, reach out to people, reach out to professors, reach out to me.

If people don't respond, people are busy.

Sometimes the second message would be good if people

don't respond, maybe just because they

are too busy with everything else. The. But my

point in saying this, professors love to. To teach.

Professors in this field love to share their knowledge about this. They may not be

good at sharing knowledge and it's going to be something that

you want to be patient with them. Not every professor knows exactly how to

teach you. So you need to help them on what your

learning style is and how to. And do your own work on how to

ask them the appropriate questions, which is more of a general question for anyone

going into academia and interacting with academia. I

digress. There's so much in the quantum space

that allows you to start from nothing. There's books

on it, there's textbooks. The one that really worked for me because I came from

an information theory background was Mark Wilde's book

on From Classical to Quantum Shannon Theory. But again, I came

from an information theory background because of my data science background and that really helped

me. A lot of other books that there's several professors at

Cornell and Yale who have really good introductory textbooks to quantum

mechanics and those are helpful. I spoke to one

of them about it, saying that it's written. Some of them are written to the

point that Anyone who hasn't had any

chemistry or any quantum physics or any physics background

can start from the ground and go. There's always resources to go.

As long as you keep asking questions. As long as you keep being quantum curious.

I couldn't have said it better. Well, we could have said it better ourselves. That's

awesome. We definitely will talk to your professors because

there's our new tagline, Candace and I'm glad you mentioned

asking questions. Right. And

it's definitely a field where I don't think anyone

really knows exactly. Like is it Richard Feynman had said the famous

quote, like, if you think you understand quantum computing or quantum physics, you don't understand

quantum physics. Physics. Right. Like, and he was a pretty smart

guy. Right. Like he was among, I think he was

on the Manhattan Project at one point like as kid or whatever or pretty early

in his career. But

I also see you've done a lot of AI

and LLM type research. Do you think that

LLMs could help people learn this sort of thing? Like

I use a lot of AI based learning tools myself. Right. So Notebook

LM probably the most obvious one. Right. Do you think that

you think those are good tools to help people learn?

Yes, but you need to remember it's not a professor, it is a tool.

Tools have limitations, tools can break and tools don't get the

job done exactly as you want it unless you design it to be. So

when you let's go with the ChatGPT, because we all

know ChatGPT, we will have some information about that. You ask ChatGPT a

question about quantum computing in general, because of the generality of the question,

it's going to give you a. It can may not give you the exact response

that you're looking for. And as you're continuing to ask questions, it's going to get

more and more towards what you're thinking. But if you don't know which questions to

ask, it may go into the wrong direction and give you the wrong information. It

may also be starting to make up information and going into a logical in

and of itself. Because at the end of the day,

are you guys familiar with what an NLP is?

So what I like to say is an LLM is three NLP in a trench

coat. It's still just a processor. It's still just trying to understand

language. And if you're giving it the wrong language and the wrong concepts and you

don't know how to communicate scientifically to an LLM,

it's going to give you maybe not wrong responses but more

improper responses and trying to understand which ones are proper

and which ones are improper. It can be the difference between understanding a

concept and not understanding the concept and then disseminate.

And if you're going to be talking with other people about it, you could be

disseminating that information incorrectly as well.

Yeah, I often wonder about that. Like how do you know?

How do you know if the LLM is hallucinating? Because LLMs

are really good at

being very convincing of when it's wrong. Yeah.

The good news is a lot of LLMs now have web access and even

on the base level. So you can ask it to provide a source, go back

to the source and then go and double check to make sure that source first,

first of all exists. If it doesn't exist, well, some of the information

may be wrong. And then if you find a source and it

has that same information and agrees with that information, which is the important part,

because even if you can read an entire paper and

at the very end of the paper they said these results are not statistically significant.

And if you just miss that one bit and the people didn't write the paper.

Exactly, the LLM and everyone else is going to miss that part.

So what I would recommend first before

trying to educate yourself on any scientific topic through LLMs,

is to have a education on both

prompt engineering as well as a basic understanding of

scientific literature and scientific reading. Because

that's what happens a lot is the misrepresentation of it. And it's not,

it's not always malicious. And when I hazard to say

misrepresentation because it comes off as malicious thing, it's mainly just people

misread something because they're not familiar with statistics,

significance, they're not familiar with the statistical tests. Maybe the way that

some people did a certain paper was to prove one point and then somebody took

point B from that. All of that has to do with

backing in scientific and quantum literature. And that

again, that teams takes time. Don't make my main point

with all of this. Don't be in a rush. Quantum is new,

Quantum is growing. And there is a lot of things that we need

to get underway, a lot of things that we need

to keep building as I'm sure we're going to continue to discuss.

Right? No, absolutely. I know Candace has a bunch of questions.

Well, yes. So what is, do you think is the biggest

misconception that people have about quantum

computing and what it's going to. Do for

all of us that quantum can solve

everything? Quantum is

going to do Three specific things. It's

going to solve problems that we weren't able to solve before. These are known as

either NP hard or variations of

something hard problems that are computationally difficult for us to solve

right now because we have the math for it, but

it's just going to take so long for the math to happen that

we can't do it on the classical computer. There's some

problems, it's what's known as non polynomial time.

It's not necessarily that we don't have an answer for this. It's that

the answer in itself is going to take so long to solve because there's so

many ways that we can do it. Excuse me, that's not the way

to say it. It's going to take so long to solve that in the way

that we have right now, that

quantum itself, because it's able to go through all the states

simultaneously, as well as entanglement principles and so on and so

forth, that quantum speed up is going

to allow us to solve those problems. So things like

AI may have some speed up, but it's not going to be as

significant as it would be with something that is an NP hard problem. And

that's the origin of the whole. You

know, this would take the lifespan of the universe several times

over to solve this problem. That's the origin of that. I don't want

to call it a meme, but that idea, we can say meme. It's. Okay. Okay.

Wasn't sure if that would qualify as a meme, but. Yeah.

Well, my classification. We're not doing humor

classification yet, so we'll discuss that on the next interview.

The. But yeah, that's the main. The second point in which

quantum is going to help is there's a lot of problems that are better solved

through quantum. A couple

discussions I've had with other people in the field is regarding chemistry.

Chemistry is by nature quantum. In order to have

the data to go into a system, we have a lot of, and

I'm speaking this time as a biomedical engineer, that

the data itself needs to be converted into classical data for us to understand it

and interpret it with our systems. But because the

chemical data by nature is already quantum, we can have a quantum to quantum

interface, allowing us to. To have that problem solved

directly without having to worry about converting into classical and then

reconverting classical to quantum, which is one of the main issues with

quantum right now. But the idea is that there's other things that are

quantum in nature and we're still trying to understand what Is by definition

quantum in nature. And then quantum computing

is better handled to do so. And the third

is to a point overall, speed up.

The issue that we have right now, let's go with AI, is that it

takes a lot of time for big models to run. It takes a lot of

time for different, a lot of

data centers and servers. They take up a lot of power, they take up a

lot of energy and so on and so forth because they have so much that

they need to run quantum. Because of the nature of

the multi states and multi state

connections as well as the entanglements and

many other factors that we can talk about later. I don't, don't want to

get too much too into the weeds with that allows

the speed up to be more significant than it would

be by just adding more servers and just adding more classical computational

methods. And

those three points would are the mainstays of how

quantum. Quantum will be more important.

Now I'm. There's also cryptography.

I specifically don't talk about cryptography that much because it's not my

forte. There's a lot more on cryptography that has been done for

both pre quantum and post quantum due

to the fact that quantum can solve a lot of current cryptograph, current

classical cryptographic methods. I'm not super

familiar with it, so I don't want to speak to something that I'm not super

familiar with. Well, that's what's really got people freaked out. I think a

lot of people, A lot of people who with the money are freaked out about

that. Right. And for good reason. Right. I

was recently at a dinner with a big

tech luminary and he was kind of like, yeah, he was very down on quantum

computing, which I found kind of surprising. And I was like.

And he goes. And then somebody else at the table beat me to the,

to the punch of like, well, what about Shor's algorithm?

Because you know, that's a fluke.

And I'm thinking to myself, I think I might even said it aloud. Yeah, but

what a fluke though,

you know. So for the, you know, I think a good analogy would be like,

you know, somebody figured out that if you,

I mean it has, it has the potential to really upend kind of

how conventional cryptography is done and that that's a problem. And

yeah, I mean, you're right. Like, I think there's a lot of people that are

hyping up quantum to such a degree of ridiculousness,

but at the end of the day it's only really good at solving

at least right now. Right. I think. I think right now we

know it can solve a very small subset of problems. Right now those

are big problems, so yay us. But I also think, too, that.

Can you imagine, I think we're very much in the transistor

days of quantum computing. Right? So, like, I also

think we don't know what we don't know yet. Right. Like, I don't think people.

Bell Labs, I think, invented the transistor. Right. I could be wrong on that. But.

But I don't think they. They had envisioned TikTok, Right.

Or YouTube or podcasts. Right. So I think that. I think that there

are plenty of things now that we can't

imagine yet could come about because of quantum

computing. Right now we know it only solves a certain subset of things, but I

also think that we don't know what we don't know.

Yeah, and that's a very good point with it, because I also want to make

the point that we could be farther in quantum computing

if Covid had not happened. Really? So you think Covid really

delayed. It had a significant delay for a lot of

developments because due to.

So there's a concept in logistics known as Lean Six Sigma. Lean

Six Sigma works on basically having the most efficient way

of doing things in certain areas. What this also led to

was a lot of. One of the

principles in Lean Six Sigma that had an effect on the shipping industry

was that you're not supposed to have a significant amount of reserves in certain areas

because it's more cost effective to have more places moving

around than it is to have more reserves. So

during COVID that's why there was a lot of shipping shortages. Oh, there's a

time inventory and all that stuff. Exactly. That's exactly what I'm talking

about. Thank you. The. And because

they didn't have the backups, a lot of people didn't get food, a lot of

people didn't get necessities. But also a

lot of big quantum computational

projects, specifically building quantum computers, were delayed.

Oh, interesting. There was also other things

going on in the world that delayed the processing of certain materials that were going

into the quantum computers as well. I can't speak to those because it's

been a little while and I don't remember everything, but the.

This delay still had a significant impact on quantum computing. We

would be in, in

my opinion, at least five years ahead than we would be now

if those shipping delays had not happened. I. I cannot

say exactly how much it would be because we cannot. We also would need to

factor in how many people got sick during COVID how many people

unfortunately passed away, that would have contributed a significant amount to quantum

computing as well, and so on and so forth. But the point I'm

trying to make is quantum computing doesn't live in a bubble, right? There's a lot,

a lot of politics, there's a lot of logistics, there's a lot of everything,

ironically, that quantum computing can solve some of the logistics problems. But

the, there's a lot of things that quantum computing

is affected by and that we also need to take into account.

And also what quantum computing affects, including things like climate change.

Because quantum computing needs a lot of a significant amount of

energy, a significant amount of resources, to the point that

I'm sure you both know. But I'm just saying in general, the. We need

a significant amount of energy to cool quantum computers to the point that the

computers themselves are in subs, sub

zero temperatures, but to the point that they're subspace

cold level temperatures. Like if you go into the vacuum of space, it is

warmer than our quantum computer cooling systems.

There's a lot to unpack there. And yes, I've heard that like

there's still radiant energy from the big bang, that, you

know, it's more colder than would occur naturally, basically. But

that's an interesting point you bring up about COVID because when I

was, when I first really heard of

quantum computing, it was 2019 at a Microsoft research conference. And

historically it's only open to

Microsoft employees, unfortunately. So if you're a Microsoft employee and you're listening to this, you

definitely want to check out mlads, that's what it's called. Just search around internally.

They tend to be about 18 to 24 months ahead of the curve.

And one of the speakers was very adamant that this was

going to be a major player. This is November 2019, right?

So now I could never tell. Like,

was that just hype? Was she just hyping up the crowd or

was there actually some kind of disruption And Covid kind of.

You know, I'm not saying that that's the only

reason, but your math checks out pretty legitimately, so.

Right, because nobody in November 2019 saw Covid on the

horizon. So I mean, that would make sense. And you remember

Frank, my entrance into

the whole quantum world was with my father, who was an

IBMer, and he was writing algorithms out on

quadrille pads of paper in the 80s.

And no one understood anything that he was doing, but a

couple people at IBM understood exactly what he was doing and they Were like, you

just do. That because you're also very,

one, we're back to Westchester county and two

and all that too. I mean IBM is one of the

few companies in the world that really thinks

long term. Right. And they've even said that

there's a number of debate. Obviously Jensen kind of brought this up in

Jensen Huang early in the year kind of said what he said.

But okay, let's say 20 years from 20,

25. Let's just say we'll take what Jensen said, it's es

as ground truth. Not saying I, but he's

walking it back like, you know, I, I,

I told you I recently saw him on like Fareed Zakaria and he was

talking about how it's, it's really within a handful of years

that we're going to start seeing some things, but it's not, you know,

mass adoption of it,

so, But I'm sorry Frank, I cut you off. Well, that's okay. I think my

Internet cut me off. But I mean your dad was doing this in the

80s and 90s, right? So this is clearly not like this is something IBM has

been working with for a while. And correct me if I'm wrong, but I think

Shor's algorithm was written by, I forget his first name. Shor,

hence the name Peter Shore. And 94, I think

was about 93. 94.

So which you know, and I think you also,

you drop, you, you dropped a name that I don't think most people realize how

influential this guy's been. Claude Shannon basically

invented digital information theory. Right. So like the idea

he's probably the most influential person in history, that no one has any idea

who he, that the average person wouldn't know. Right.

Yeah. A good amount of my work has been investigating

Shannon Information theory as well as Shannon Entropy and using that as a metric

for other, other the problems and seeing how

that works. But I also wanted to have a quick note.

Funnily enough, IBM is also a huge part of my work as well

because I'm at the Watson School of Engineering. Oh, interesting. That's

awesome. That's awesome. And I work at Red Hat in my day job,

so clearly, clearly Big Blue is never that far away.

Right? There we go. Okay. Well actually

I think it was this week that IBM just made an

announcement about how they were working with

Moderna with the MRNA

vaccines and they were looking at, you know, how they could

really start doing some medical health care with,

with using quantum. And I,

I was just blown away. Like to me that

seemed like something that would be so practical and amazing for

people if they could do enough algorithms and to figure out

who is going to get like who has a proclivity to what. So they could

potentially, you know, avoid it and do better for themselves. I think the

medical advancements would just be out of this world.

Well, biology, medicine. Yeah, I mean medicine is basically applied

biology and biology is arguably applied chemistry. Right. Like so like it

wasn't that an XKCD cartoon where it

showed like, you know, which is the most pure. That XKCD is this

nerd web karma comic and

there's one of them where they show like you know, basically

they were these, they lined up based on like how

abstract their science was and like well you know, biology is applied chemistry, chemistry

is applied physics. And then there was some guy all the way like to the

side of the room that basically said, well I'm math, I'm a mathematician. Right. And

everything else is just applied math. That was, I thought that was funny. Little nerd,

little nerd joke there. Sorry about that. No, it's all good. We want that here.

So Michael, let me ask you, if you're looking at the quantum ecosystem

globally, who do you think is getting it right

and communicating to others well about

what they're doing so that people can learn? I'm

going to split that into two different questions because

the people who are. So let's go globally and we'll talk about

companies because every country tackles this a little bit differently.

US has the biggest base in quantum just because we have Google, we

have Microsoft, we have IBM. There's a good amount of other

companies that are up in Canada. I believe Xanadu is in Canada and they have

a really good base as well. D Wave I believe is over in the uk

but I don't quote me on that, I can't remember where they're, they're

based out of. But the UK is also having a significant quantum initiative.

Japan has a lot of work but not the. Not as much via company but

through their institute known as Riken R I K E N

and they have a lot of quantum that's coming out of there, not to

mention all the academic spaces in every country. France and Switzerland

are also having significant amount but again the more academic

and government oriented, the company oriented. So let's talk about the

companies and so on and so forth. The one that's been the best at communicating

has been IBM, has always been IBM. Their

software is open source. Everything is

very well communicated. If they have, they have very good

communication. Whenever they have issues with the software and they have very

good communication and new developments and so on and so forth.

The newsletters they do are incredible. Everything else is there is

wonderful. I also, I've failed to mention mit. MIT is doing a lot,

a lot, a lot. But

going back to the companies, I believe

the Google and Microsoft have

been doing a lot, but not have been talking about it,

which is both good and bad because in the current system that

we have where companies are competing, they need to not say anything. But when

somebody creates a new form of matter as a superconducting

fluid that allows Quantum to be working,

then there needs to be more communication about that and more disclosure

about that to make sure that we understand that this is really how it works

rather than it's just a fluke that they found in the lab.

Right. But the

actual research that they're doing is miles and miles ahead

because not only because of the funding that they have, but because of the resources

and the talents that they have. They have the best talent

for this. All the companies do because they not only do they invest in it,

they want a Quantum future. Nvidia is doing

incredible work. I don't always mention them because they're in

my head. They're more AI because of how much of the servers and AI work

that they do in general, but they, their basis in Quantum

is quite significant. On top of that, don't

mention them as much because both Google, Microsoft and

IBM have a lot more open access and a lot more access to their

systems than Nvidia does. Nvidia does work, but they work more with companies

than they do with individuals and they do have academic grants

and then do have a lot of work with academia for that kind

of stuff, but less with the public than the other than the other three.

I also wonder too like how much of the

defense industry, the military

industrial complex, how much of this are they working on and they're

not talking about? I think you bring up an interesting point. There's a lot of

innovation going on here, but maybe not everyone wants to share that information for

reasons real and imagined. Yeah, and

there's always the big question of, I mean

the America is home to the Manhattan Project and what we used

Quantum for and what. Right. Forgive me

for the, the

manner of speaking, but really blasted

Quantum into a public space, the

using. We also have a significant amount of

political tensions throughout the world. We, we don't know as much as what

China is doing, what Russia is doing, what compared to the.

While we're in the US around the time we don't know what Canada is doing

either. This isn't, this is not an affront to any

country. This is just saying. Goes

back to the, the concept of countries and kings, right? They, they

always share, they don't always share. Right. It's, it's, it's

basically poker, but the stakes are like a lot bigger, right. That

not everyone's gonna share their cards. Right. This is not new. Art of

War talks about espionage and keeping secrets. And it was

written what, 2000 years ago, maybe

2500 years ago. Like so this is not a new concept. So like, you know,

chances are any country that's alive, certainly anyone who's alive

today, was not around then. So this is, this is, this is more a function,

I think of the human condition than any particular political

ideology. Yeah, exactly. And the

one big issue is that if we're all

developing quantum at the same time and

we're not communicating about it, what have other, specifically quantum computing,

I should say, what have we already developed that everyone has and what have we

haven't developed that we all should be going for?

Right. And this goes across the board for countries, for

companies, for individuals. There may be someone in a different university

who's doing similar work than I am and is

a few steps ahead of me or a few steps behind me. Right, and you'd

be better together. It's very Canadian of me. But you know,

I mean, I know I talked about, I don't disagree here at all, but I

do, I think we would be better together and I think that

eventually there's going to be leaders

amongst all the different kinds of cubits

and they're not going to be the same leaders. And then,

you know, groups can then, you know,

silo if they want to, depending on, you know, what qubits they're using

for their solutions. But again, it

would be better as a community and sharing would is

the way to go in my opinion, as the

Canadian here

who's from New York. So you have to understand my inner conflict. Right.

I was gonna say like I'm always. Battling, like I'm a border and bred

New Yorker. It's the first thing I tell everybody. But I've been living in Canada

for 15 years. I became a dual citizen. But

I, I can see why there. I can see certain things that are just done

better. Not everything, but certain things are done better,

you know, so I think we should share. Let me ask you this,

Michael. At Impact Quantum, we're really all about

accessibility. What advice would you give

to young professionals or curious minds who want to contribute

to the quantum future. The

short of it is do it. There's a lot of

open there. It depends. But the long answer, it depends on which way you want

to contribute. So there's ways you can contribute

in software, there's ways you can contribute in the hardware. There is

reskilling programs that go for quantum

engineering, meaning quantum hardware engineering. Like you'd be working with actual

lasers and other systems to develop quantum

hardware, to see how you can develop qubits, how you can develop quantum

computers and quantum service and so on and so forth. There's other programs that

are just quantum algorithm stuff and all that is on

IBM for free. That's part of the reason that they're. I think of them as

the leader in IT because not only do are they able to

set up the entire IBM, IBM quizkit

language. I believe I'm pronouncing that correctly. I honestly have no idea.

That allows you to do quantum computing in Python, but they also have

very detailed and very informative

documentation for every algorithm that, that exists in

quantum computing. And you're able to go through it, able to understand

it. And that's actually a good case of when you can use ChatGPT

is explain this to me better. You find an algorithm, you,

you see what that does, but you're like, I don't know exactly where this should

be used. And then you have ChatGPT or another AI, say, well,

you can use it this way, you can use this, this type of data source,

you can use this type of thing and then build it. There's a lot of

competitions out there on different sites of how to use quantum for

different things. Of do we, can we use quantum for

biology? Can we use quantum for transportation problems? Can we use quantum for this, that

and the other thing? There's a lot of conferences too. If you have the ability

to go to conferences either as an academic or professional, there are quantum

conferences. I believe IEEE Quantum is still,

still has vacancies and that's going to, I forget where it is, but it's

going to be in a couple of months. And they're basically at the

forefront of quantum engineering, both on the algorithm side and the hardware

side. But the

better way to say it, get involved in whatever you can get your hands on

and then if you don't like that, move on to something else.

That's great advice, particularly in a day when an age when we're so

overwhelmed with information. There is a lot of information

out there. Pick one thing and keep going at it. If you don't

like it, move on to the next, move on to the next, move on to

the next if you have the ability to do so.

The best way to think about it, if it's not your job, have fun with

it. If it is your job, then figure out which is going to be the

best way to help your job. For example, there's something known as a

variational quantum eigensolver versus a variational quantum classifier.

VQC versus a VQE Eigensolver is

better for chemistry problems, classifier is better for AI

problems just because that's how they're built. So someone who working in

chemistry is better is going to be better suited for a vqe and then

someone working in AI is better suited with a vqc. And

this is also something that you can use an AI for to say which

algorithms, which systems are going to be best for me to use in my job

on a day to day basis. Right.

Interesting. What do you think are the current

bottlenecks in quantum hardware and software

that are the most urgent to solve? The

availability of qubits and servers and

so on and so forth. We're limited by the amount that we can

use, which is both good news and bad news. Bad

news is obviously we can't use as much. So it's either going to be a

high cost for somebody going to be using especially someone who isn't

at a either isn't at a university that has access or

someone who is or a company that has access to and they're just doing

on their own. It's going to be more difficult to use qubits. But

the good news is this is pushing for development. As

humans we like to adapt and this is another way of doing it. So there's

something known as NISK quantum devices and these

blend classical and quantum to make a near

term system. Some you can also call it a CQ system

which is a classical quantum algorithm and algorithm and

systems. The one of my projects in

itself is a full quantum quantum

system and another one, it another one is a

hybrid classical quantum system. And the classical

quantum system is the quantum

side of it doesn't exist. Meaning it's a novel way to

use a classical system and we quantumized it in a, in the manner of

speaking. But I can go on about that a little bit

later. But the main idea is when we

have the ability to only use a certain amount and we're limited in the resource,

we're still going to adapt. We're still going to try and figure out a way

to use it. And we're like, okay, we don't have enough quantum. Okay, we're going

to use a little bit more classical to meet the need that

we need, that we need to fill.

Interesting. What's your advice through kind of

existing IT professionals to

start looking into this? I'll go back

to what I was saying about find which one is going to be best for

you. Right. So some IT professionals are going to be more in

cybersecurity. So reading things on Shor's algorithm, how

quantum is going to affect RSA keys and how to combat that and so on

and so forth. That'll be very helpful. Right. And

people who are going to be more on the logistics side of

it, trying to see how their transportation problems and job

shop scheduling problems can be solved with quantum algorithms as well

as quantum systems. But everyone to take it all with a grain of salt

because using qubits

and trying to find how qubits are going to be used for certain

problems, mainly like let's say we're going cybersecurity. So we're

talking about malicious attacks. We don't have enough qubits to have

a significant DDoS attack on the system or something

that's going to take tackle a lot of RSA keys at

once. Again, I'm not a cybersecurity professional, so some of this may be a little

bit nonsense, but

the going back to the idea of

using quantum for anything, figure out what your thing

is and what quantum could solve for you, because there's a lot of things that

it's been adapted for. And using quantum in now

you can also, you don't need to use quantum specifically, you can use quantum inspired

algorithms that will allow for a little bit of a speed of a little bit

of help instead of using a full quantum system. And then you don't need to

worry about qubits at all. Right. Or

emulation, I think is another. Yeah. Thing people call it.

Yeah, but yeah, no, that's a good point. Well, there's going to be emulation and

then there's going to be quantum inspired algorithms. So when we're going to call

emulation, we're going to call more simulation. When you're simulating a

quantum algorithm on a classical system, while there's a quantum inspired

algorithm where it's going to be a. Let's say we take Shor's

algorithm and then use that for a specific cybersecurity

problem, but use the ideas behind Shor's algorithm

to rewrite the classical issue. Oh, I see

what you mean. So there's quantum math involved and there's quantum

mechanics, and using the quantum mechanics to basically apply

matrix calculations and other methods that Shor

does to that cybersecurity problem, and then it becomes quantum

inspired. I see. So no quantum

hardware, not even necessarily quantum algorithms

per se, but.

Interesting. Interesting, exactly. That's going to be something that's

going to be resurging a lot because of the lack of qubit access, because

of people still want to use it, people still want to

adapt. And the sad way of saying this

is people want Quantum to stay relevant. And without

access to qubits on full quantum software, the algorithms and other

quantum inspired algorithms are going. And this

methodologies are gonna are booming right now, and they're gonna keep booming until

we're able to catch up with the qubits. Right,

interesting. So if you could accurately

forecast one quantum wave or pivot

by 20, 30, so five years from now,

alignment with commercial applications, talent

scaling or policy frameworks, what do

you foresee? So

as the AI hype dies down,

investors and other groups are going to be looking for the next

big thing. They're going to assume that Quantum is going to be it for a

little bit. And that's what we're seeing the

beginnings of now. That's why we're seeing the big story about Quantum. And then it

dies down in a couple of weeks. Another big story about Quantum dies out in

a couple of weeks. That's similar to what happened to AI at the very beginning

of it as well. This is also the same thing that happened to

genetic engineering way back in the day, where there was a bunch of really big

stories about like the Human Genome Project and then a bunch of big stories about

how this is going to solve cancer and so on and so forth. And right

before CRISPR hit, there were a bunch of big stores, a bunch of

big quan, big, not quantum, excuse me,

big genetic changes and big. And a lot of

things that really helped get genetics onto its ground

that it's been for the past couple decades, and

then became a boom in using genetics for basically everything.

And that's what's happening to AI now. But the main thing

is that this is not small random

developments that burst forth. It's a staircase.

And each step is being built. Some of the steps just

look a little bit better than others. So as these steps are

being built, they're going to reach a certain point in which everyone

is going to know about it, everyone's going to have access, everyone wants to build

it. One of these points is going to be the

accessibility of it all, because AI and

technology only really boomed when everybody had access. If

OpenAI didn't give, didn't give as much access to people,

it would not have been as big. ChatGPT would not have been as big if

people didn't have access to as much access as they, as they did and as

they do. Quantum may have the same thing.

However, I'm saying this as somebody who is developing algorithms rather than

somebody who's developing algorithms within the university

rather than somebody who's developing the systems within

a government or military setting.

Quantum cryptography is going to be the thing that everyone wants to invest in

in the beginning because more the main thing that you can

count on with people is that they want to be safe.

And cryptography, cryptography and quantum cryptography poses a threat

to that. Regardless of what we say about AI, regardless of what we say about

climate change, cryptography is the quote, unquote,

present and clear threat for a lot of people.

That will be the first thing that will spark a lot of investment, that

will spark a lot of development, that will spark a lot of everything. So when

there is quote unquote breakthroughs and that next step to

really see how we can use cryptography and anti cryptography

methods and cybersecurity methods. I keep saying photography, but really we're talking

about cybersecurity here. Cybersecurity

methods in quantum and combating the quantum. Once those are hit,

then there's going to be a significant amount of boost, there is going to be

a significant amount of interest and then the rest will develop because of that.

Interesting. I like that.

That's cool. Where can folks find out more about you and what you're up

to? Sure. So I am going to be, I'm on

LinkedIn as you have the notifications from that. I will

be starting my work through GitHub. I'm going

to be publishing several things through there and I'm going to be posting my publications

as well as on my Google Scholar, my research gate

and my LinkedIn as well. So that's my research gate

and my LinkedIn will be the places to check. Okay,

cool. Excellent. Excellent. That's great. Honestly, this has been

fantastic. This really has. I mean this has been, this

has been a very enlightening interview. So thank you for that and thank you for

your time and we'll let RAI finish the show.

And that's a wrap on today's Quantum Ramble with Michael Magid.

Proof that system science isn't just a polite way to say I dabble in

everything from qubits to the quantum cold. We've

decoded just enough to sound clever at dinner parties,

but not quite enough to build a quantum computer.

Remember, if you think you fully understand quantum, you

probably don't. Until next time, stay curious,

stay entangled, and for heaven's sake, don't trust an

AI in a trench coat.