Welcome back to Impact Quantum, the podcast where qubits get

curious. And entanglement isn't just a relationship

status, it's a career path. Today's episode is a real

quantum leap, as Frank and Candace sit down with the

ever engaging Alex Kahn author, educator, and

quantum computing pioneer who may or may not be on a

first name basis with every photon in College Park, Maryland,

Known for his book Quantum Computing, Experimentation with

Amazon Bracket, Alex joins us to unpack the not

so light speed evolution of the quantum ecosystem from Amazon's

quantum ambitions to ion traps, optimization,

and whether Excel can really prepare you for the multiverse.

We talk hype versus hope, entanglement without the emotional

baggage, and why quantum computing might just be the new

GPU. So brew your favorite beverage, align your

qubits, and prepare your mind for a journey into the

wonderful world of quantum weirdness. Let's get entangled,

shall we?

Hello, and welcome back to Impact Quant. Sort that over. Hello,

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

the emerging field and ecosystem

of quantum computing and how it's really gonna take a

village, a quantum village of curious quantum curious

people. And, with me is,

the most quantum curious person I know, Candice Kahuli. How's it going, Candice?

It's going great. I'm really excited about today's conversation.

We've been having such great conversations. So I'm just loving what we're

doing, and I'm and the curiosity is just

exploding all over the place. It's really good. Absolutely. Absolutely.

So, I'm really excited about having our current guest,

because when we did the pre call with him to talk to him, I was

like, that guy's name sounds familiar. And then he mentioned that he wrote a book.

And here is the book. I told him that I well, okay. Can't get it

into focus. But for those of you who didn't

see that, it's quantum computing experimentation with Amazon Braket.

And our guest today is Alex Khan. Alex Khan also lives in

the old line state or the old bay state. I forget what the official nickname

of Maryland is. And, we were

talking recently about these various quantum hotspots around the world.

And one of them is College Park, Maryland Mhmm. And, which,

where he used to work. So welcome to the show, Alex. Yeah.

Nice to have nice to be here. Awesome. Awesome.

And I always when I think College Park, most people will think the

University of Maryland, I think of IKEA, because the large

IKEA in the area. And my wife does a lot of IKEA

furniture building and things like that. So,

welcome to the show. Thank you. Yeah. Glad to be

here. Cool. I I I have to confess

I haven't finished the book because, but I did get through quite a bit

of it. It's very well written. It it discusses kind of the Amazon Bracket

service, which, I haven't followed where Amazon is with

that, because I

tend to be very Microsoft focused, unfortunately.

Okay. And now I'm at Red Hat. Now I'm very IBM focused too. So,

tell us, tell us what made you wanna write the book.

Well, actually, it was, PAC Publishing that reached out to me, and,

they had obviously heard about my,

different papers or involvement in quantum computing.

When Amazon bracket came out, I had, well, before

that, even with D Wave, I had made some videos about how to get

into D Wave, you know, how to, do optimization problems

with D Wave. A lot of the concepts were

just very new for me as well, annealing

and, cue boards and optimization. And so

I made some videos, same thing with, Amazon Bracket at the

moment. IMQ came into,

was added to Amazon Braket, I wanted to get my hands on

it. And, and then I, made a

video of that, you know, letting people know

how how to use an ion trap, and,

I use a simple example in there. So I think back publishing

heard about it, and, they wanted me to

leverage my experience with using

different, optimization problems, with Amazon

Bracket. So, I think I was a

natural fit to write it at that time. I mean, right now, I think there's

a lot of people that have been,

that have, used Amazon Bracket. Amazon Bracket has a very

solid team. They have a lot of blogs on there. But

in those early days, I think, maybe I was the only well,

the few people out in the ecosystem that could write, that book.

I still think you're a great author. Like so, you know,

don't discount yourself. I would love to see another edition of the book and things

like that. Because I know, I know this field

changes pretty rapidly. And Yes. I think

2025 has been a crazy year in quantum, and we're

only, like, we're recording this on April 10. Right. Right? It's

already been a wild year. And I would

say, for me, the kind of I

I'd been sparked my interest in quantum in 2019, and then it kinda spark

kinda died out. But, like, this time for me was when Google

announced the Willow project and their results from there. And then suddenly,

you know, the CES kind of debacle and then just the recent

rapid fire announcements from Amazon, from,

Microsoft, from all these players, international players.

So so what what's your take on twenty twenty twenty five so far

this this year? Yeah. It's, overwhelming to some

extent. I mean, I, you know, I try to keep up with,

you know, what's happening in the ecosystem and what algorithms are coming out,

what new systems or devices are being added to Amazon

bracket. So, every year,

it's just a little harder to keep up with everything.

So, you know, even last year at, the University of

Maryland, National Quantum Lab,

I I just saw a lot of work happening inside the

university. I mean, they're working on algorithms. They're working on sensors.

They're, they've got, various super conducting

quantum computers over there that they're researching, various use

cases. But then they're also doing, you know, using

the chips for quantum gravity and for quantum sensing,

and, they're building the quantum Internet. So, I mean, there's

just so many areas in just one

place. And so when you start multiplying that now

where every country,

every university wants to get into this, there's

just, you know, new papers, new ideas,

unique creative concepts, new ways of

teaching coming out from every area, and, you know,

there's more books. So it it's very exciting. It's a

definitely a growing field. The quantum

hardware is also growing. There's a lot of new companies that are building quantum

hardware. I mean, Amazon also built, you know, have have

announced their quantum computer. So in

that sense, I think it's it's an amazing

field to be in. You know, every day is there's some

excitement in some area, new benchmarks. So

so, yeah, it's all I can say is just, hard to keep up

with it, and I've tried to,

follow more of the optimization route. That's kind of my area, and

it's kind of become my area of expertise. Though, you know,

as you'll hear, I'm working on all kinds of other things as well.

Interesting. Yeah. And that's what I find because there's so much

information coming out about every aspect of

quantum. You know, it it it begins

you you understand that just when, you know, for someone like me who's

curious, but who's been in the tech sphere for over, you know, ten

or a few more years than that, you know, you

get involved with it and it's exciting, but then you have to kind of

decipher what's real, what type,

and what means what to whom. Right?

So you're excited when you get to hear about the oscillate. You're

excited when you hear about Majorana. But

then when you when we speak to people who are more on the

academic side, they're explaining to us,

well, you know, it's a little bit more hype than you might

think because there's error correction

issues and scalability issues, and

there's a lot of things behind the scenes that, you know, aren't

quite there yet. You know? So how do you

feel about kind of that divide that

there is between, you know, the physicists, the academics,

the engineers, and then those who are trying to kinda put this into

commercial use, who are trying to find, you know, their

quantum algorithm that they're gonna use for the next thing that they're gonna work

on. What is your what is your thought on that?

So, I mean, I think there is definitely a big gap between,

you know, where we are and the and the hype. Sometimes the hype does

get way ahead of itself.

But the reality is that this is a very interesting and very

innovative technology. Like

INQ's founders, have been working on this for thirty

years, you know, when they were working with atomic clocks,

and, you know, found a way to actually calculate,

and do a calculation using a qubit.

So it's taken a long time, and there is

obviously a lot of development happening. So when I started in 2019, there

was only a five qubit quantum computer, you know, that I could use with

IBM. Now we have hundred qubit quantum computers.

We, it was, I think, two years ago when DARPA did a,

RFP for, building one logical

qubit. I mean, here, just one logical qubit. Now

we have multiple logical qubits, and, the error

correction codes are getting better. Before, we thought it would take a thousand,

actual physical qubits to make one logical qubit. I think

now it's less than a hundred. So, you know, there's

there's a lot of development, lot of new ideas.

So and I think it's also progressing, you know,

very rapidly because a lot of companies and a lot of researchers are working

together in this. So there's definitely

there's, definitely hype, but I think that's because

sometimes the communication, the way it reaches the market, and

then when people try to simplify it and write it down and

of of course, you know, they want to get more eyeballs

on the paper. They'll make an announcement, you

know, x y z company had this new

revolutionary advancement.

It just gets blown out of proportion. But the people that are reading the papers

and that are in the field,

they they see steady incremental, progress.

So for us, you know, I I see all of those, and I try

to help out and explain as much as I can to the people that are

following me. But, I mean, we we we're

we're seeing, you know, very solid progress

on a constant and, you know, rapid pace.

So so I I think it's all good. I think, you know, at one

point, I was also worried about the hype, and then I realized you need a

little bit of hype to get people excited and for the

general public to pay attention. If there was no hype,

nobody would be, you know, even interested. You wouldn't get

get this information out to the high schools and to the students and for

them to, want to even pay attention.

So I I think it I think it as long as somebody is not

inflating something too much and blatantly saying something that's

not true, I think it's it's good to have this

hyphen out there. There is a fine line between hype and fraud,

isn't there? Well, I mean, if you're a public company and

you're saying something in Right. Direct, then, obviously, that is

that's, you know, different. I also think too

that VCs are not gonna it's a lot easier to raise money in which you

build the hype around you. Right? Like, you know, you have to put a little

bit of a ketchup on the on the burger right now

to to to make it more palatable. Because it is a long

play. Like, I think it's still a long play. Like, now what is is it

is it a three year long play, five year long play, or as

Jensen Wong kind of said and walked back, a twenty year long play.

I don't think it's that. I think it's a it's it's not if you wanna

make a quick buck, I don't think Quantum is really the place for you if

you're an investor. I think it's one of those things where the

there's gonna be a massive long haul investment. I could

be wrong. Could be wrong. But, I always press the key.

I can't really comment on that. But Right. Right. Right. If you think about, you

know, like, our goal to want to go to Mars, I mean, that's the

lofty goal. And you you have to start somewhere and you

have to start putting the pieces together, and it's a complicated

project. Now I think the the difference between going to Mars and making

a quantum computer is that Mars is still in the same orbit.

But, you know, with, with our computing, classical

computing is always getting better. So it's like Mars is getting further and

further away as time goes by. So,

it is more challenging when you compare quantum

computing to classical computing. And it would be it

would be like when the GPUs came out and, you know, we

were playing video games.

If you were trying to compare a GPU to,

an actual computer, classical computer, you

would say, well, what's the value of a GPU? But the GPU did

one thing really well, and it got a lot of people excited

about video games and rendering, you know, light tracing and all of

that. So in that one niche

market, it started to make an impression. And,

you know, it grew it grew with that market. I mean, you know, people

didn't care if the games were kind of rudimentary

and, it wasn't perfect. You know, we kept it's

like we funded and we kept paying for better and better GPUs

and funded that whole industry of making better games and better

software, and the hardware got better. And and I

think the quantum computers will move like that. I think it

becomes challenging when you're trying to compare a quantum computer right

now to a classical computer, and I don't think that's really a fair

comparison. I know

that VCs and, you know, different industry

leaders will obviously want to have some advantage over

classical computers, but I think the the important thing is for now, at least

the way I see it and for most, quantum enthusiasts

to just use a quantum computers and learn how to use them,

and and then I think innovate and come up

with new use cases where maybe a quantum computer has a niche

market and, and and the

classical computing is not really in that market as much.

So given the experience that you have,

with aligned IT and and some of the other ventures,

What do you see as the most promising real world

application of quantum computing?

So Okay. I'll I'll I'll try to answer that in two ways. I mean,

there's obviously a lot of different areas and applications,

and, it would be like asking when the first transistor came out, what would be

the, you know, best application for a transistor.

Right? I mean, we at that point, you wouldn't be able to imagine what

the world would look like with a transistor. Right? So I

think that's what we're trying to do. We're we're trying to imagine what this world

would look like with quantum computing. And quantum computing,

you know, as you see in the book, is dramatically

different. It's solving potentially the same

problem, but in a very different way. You have to think differently.

Even if you don't think about how the calculations are actually

happening and the fact that you're using qubits or, you know,

a superconducting qubit or an ion trap, The fact

still is if you cannot solve the problems the same way

as you would writing a normal, you know, normal

code. So now where where would there be the

most impact? So what I found is

that the in my area, since quantum

computers have this property of superposition and entanglement,

they can basically connect two variables together.

So if one variable changes in a certain way, the other one will

change with it. So it take,

naturally, the quantum computer can has the property of

correlating variables together. So you can think

of all the applications where you have correlated variables.

And and portfolio optimization is a very simple example where

one asset is correlated with another asset, either, you

know, negatively or positively. If one asset goes up, the other

one goes up. Or when if one asset goes up, the other one goes

down. So you could code that in a classical

computer. But with a, quantum

computer, you just have to correlate the two variables, whether

it's on d waves, annealer or whether it's in,

a gate quantum computer. You just have to put in the right rotation. You

know? So once you do that, those two variables are

now correlated. And then you

can solve you can imagine all kinds of problems that you can

solve where you have binding two

variables connected, and that's where the whole

combinatorial optimization with quadratic terms

becomes a natural fit for a quantum

computer. So, I mean, there's there's, you know, millions

of applications like that. And I think, you know,

like, there's a lot of development happening in QAOA.

So that definitely is an area that will continue to grow. There's not

a quantum advantage of the QAOA algorithm,

but, it's gonna continue to evolve, but that's the

natural thing that a quantum computer can do. There's,

quantum chemistry. Now that is

also kind of an optimization problem. You know, you're

looking for the minimum energy of a molecule or of,

you know, some some property. So

in that sense, there's a, you know,

there's a lot of applications there. And so there's another algorithm,

VQE, where people are using VQE,

which is a quantum algorithm to solve energy problems. So

they have to build a Hamiltonian, and then they embed the

Hamiltonian into the, you know, into the

qubits, so into the quantum circuit. And the

system will naturally, you know, go to the, go to the

lowest energy value and give you that. So, I mean, that's

that's that is very possible. But, what

I'm also finding is that with

the noise, since the qubits are still noisy and, you know, we still

have only a few qubits, even hundred are not

fully connected, you

cannot think in terms of one variable to one qubit. I

think, you know, as I have developed my own understanding working

with quantum computers, it was easy to take a variable and

say, you know, this one bit of information is

equivalent to one qubit, and that's a very expensive way to do quantum

computing. So, you know, you're using one bit for one qubit.

But now, we are looking at

solving very large data problems,

like I'm working with the team on a genomics problem.

If I was to take one base of of a

genome sequence and embed it on one qubit,

I would need 10,000, a million qubits to

embed, you know, a 10,000

base, sequence. That that's not really going to be

very useful. So what we have to do

is we have to think about how really leveraging quantum computers

where you use the power of two to the power

n cubits. And so every time

you have more cubits, you get a two to the

power n, increase in

variables. So these are called amplitudes.

So, for example, with, with two qubits, you have four

variables or four weights or four

amplitude or four probabilities, however you wanna call it.

But these consider them as four knobs or four variables that you can work

with. Well, by the time you get to two, to

13 qubits, it's big number.

Yeah. It's, you know, it's like, 8,000 something.

Right. So with just 13 qubits, now you have

8,000 knobs or variables that you can work with.

So now I can embed an 8,000

long chain of genetic

information potentially into that.

So this is, but, you know, there's we haven't done a lot

of that yet. So, you know, we're working on algorithms. We're trying to figure

out how do you embed that information into the qubits. How

are you gonna calculate once the information has been embedded?

How do you store that information? So,

there is there is a lot of potential,

but I think, we have to come up with the algorithms. And, I

mean, the hardware will progress and get there,

but we don't even know how to use, that technology. So I think that is

what we have to prepare ourselves for. Well, in terms

of preparing ourselves, I know you also have

experience in academia. And so

it kind of makes me wonder if what we are currently

teaching in universities for

quantum computing, if what we're teaching is correct

or if we should be teaching something else now that

you're kind of practically in all of it. Is there any

kind of perspective that you have on on things that could

be added to the curriculum or should be more focused upon

as we're bringing up, you know, the next generation, you know, the they're the

alphas, and even Gen Zs, you know, we have

an opportunity to teach them, you know, the the right

things, you know, while they're excited about it. Do you have any thoughts on

that? Yeah. Definitely. So I I taught,

quantum computing at, Harrisburg University. And then when I came to

the QLab, UMD QLab, I

was given a few students, or, you know, some of the

students were selected that would be doing the extra work

of doing a project with me. So, I got an

opportunity to teach them. I will say

that learning quantum computing is a is a long journey. You have to

be really passionate about it. And, you know,

it's like any discipline, whether it's, computer science or

biology or chemistry, it's, it takes

many years. It's a long journey. I think,

I, you know, I I don't think it's useful if you just wanna get a

quick return on your investment, you know, take a few

videos on YouTube and things that you can, you know, get into quantum

computing. There are just a lot of lot of things to

consider. You know? Like, we've already already talked, you know, you have to know your

what type of difference you're dealing with, what kind of algorithms are out

there. You have to, you know, decide whether you're gonna be

doing the coding and writing software algorithms, or you're gonna be

writing a software stack, or are you gonna be

working on building the quantum computer? So, you know, there you've got

various other disciplines from physics and

heat transfer and, you know, chemistry probably,

material science, optics. So

I I think the the field is really

growing and trying to understand itself. I mean, you know, a

few years ago, there wasn't even really degrees you could get in

quantum information science.

But math math is

the definite basics that you the further you can go in math, the

the better you're gonna be in the quantum computing. I mean, that's

pretty much a given. Every day, I'm, like, struggling with how

much I can do because of my own math

background. So That makes me feel better because,

like, I read these quantum books and, like, you know, it used to be

fifteen minutes in and I get a headache and I'd have to stop. Now I

can get to about forty five minutes. But, yes, that's that's good to know. I'm

not alone on that. Yeah. I mean, I struggle with that as

well. And then, you know, I, I was working on,

density function and and then in chemistry, there's the density

function theory and I mean, I don't know this stuff.

So I'm not a chemist.

But, you know, it is it is an it's a field that really just

pushes you and pushes you if you're excited about it. It's like, you know, you

you wanna climb a mountain and you wanna get to the top. There's

just all kinds of challenges in your way, and you

have to just keep pushing yourself and overcoming one

challenge at a time. So so, I mean, I'll say for

the education, the education is definitely,

improving. There's a lot of people that want to figure out how to

teach the next generation quantum computing. I mean,

I've tried to do kind of my best in

in explaining to a you know, my book was,

written more for, professionals,

architects that are already in the industry. They already know

computing, and let's say their boss tells them that, you

know, go I've heard about this quantum computer. Is this something that's useful for

us? So, I mean, I wrote it for that

audience for them to be able to quickly browse through

the book and really see what is quantum computing, what does it look like,

what can it do, what do these devices you know, what are they capable

of? And then they can decide on their

journey. But when I was teaching high school

students, you know, we had to start at the very

basics and, you know, just matrix multiplication

and making sure that they even understood that part.

I've also taught, I had classes where I was

teaching, just general business

majors, and they were not interested in building a

quantum algorithm or, you know, they would never

build a quantum computer, but they just wanted to know generally what is

quantum computing so that if, they're working for a

company, let's say they're in the procurement department and, you

know, their manager says, we're buying a quantum computer.

So how would you begin to evaluate what a quantum computer is? And,

you know, one company is saying we've got 30 cubits. Another one is saying we've

got 50 cubits. And one is saying, I've got this fidelity. And another

one is saying, you know, we have an error corrected quantum computer.

How would you even know what questions to ask, right,

to to determine whether you're going to buy the right quantum computer?

So so then, you know, that was a very

different market that, just wanted to know the terminology

and the basics. They were very excited to take the course,

but, I mean, they were not very interested in getting down to the math

level. So I think it depends on the audience. There's a

lot of room for everyone to join into the

quantum ecosystem, whether you're doing marketing, whether

you're in procurement, whether you're, you know, in one

conferences building, you know, setting up conferences,

doing podcasts. Right? There's a lot of opportunity,

to bring existing skills or whatever your

passion in. You're in computer science or chemistry or

gaming. I mean, I built a a VR

application. We could talk about that later. But Oh, very cool. So,

so I I think there's a lot of opportunities, a lot of different

ways to think about quantum computing, and it's really depends on the

person. Can where do they want to go in quantum computing and,

what mechanism they can use to go from point a to point b? And,

really, I think everyone's journey is going to be a little different. I mean,

I've not seen two people that have the same journey in quantum computing.

I think it I think you what you touch on is really good. And I'm

glad you're here because you're one of the few people probably the first guest we

really had that has an equal footing in academia as well as

industry. People with fifty

fifty, ratios there are pretty rare anyway. But, you know, when

you think back to the early days of classical computing, right, it

was largely the electrical engineer types and people

soldering wires together. But if you look at as it developed over

time, we have graphic designers, and we have, like, the whole

everything from soup to nuts in terms of what,

what the skill sets are needed. So, very

glad to hear you validate kind of our thesis for the show is, like, you

need Yes. Quantum curious people. I'm also even the first

time. I really appreciate him talking about the marketing, the sales,

you know, the business minded. Like everyone forget about it.

Yeah. Beautiful. Because it really shows what an

all encompassing, field that it can be for people

with a variety of disciplines. And

they are needed. So that was great. Thank you. I love that, Alex. That was

great. I also feel a lot better about my my oldest's choice to

take AP Physics next year over AP Computer

Science. So Well, I mean, you're going to have to program

both I mean, no matter what field we're in now, you have to know a

little bit of programming or at least know how to use chat GPT to create

a program. Exactly. Yeah. Yeah. Vibe coding. Yes. Right? Exactly.

Exactly. Right? I predict a lot of money will be made by

consultants fixing Vibe Coding and updating and patching Vibe Coding

applications. But Yep. That's just the cynical side of me.

So what do you what do you think is really kind

of where do we go from here? Like, in in terms of, like, if

quantum is definitely I think it's out of the lab, but I think it's also

in that weird adolescent phase of it's still heavy on

the research. I think data science followed a very similar aspect to this.

Right? Most of what we call AI is really data science.

Most. And most of what we call data

science was really statistical and mathematics and kind of PhD

level statisticians and

mathematics. And I think there was a lot of gatekeeping in the field early

on, but it kind of exploded. And I think that where do you

think we go from in quantum? Do you think that where do we go from

here in terms of building out an ecosystem? Like, what

what do you think needs to happen next versus what you think will actually happen

next? Well, I mean, to build the

ecosystem, I think, you know, we just need more marketing

and and depending on when you want to pick up

somebody. Right? If you wanna pick them up in, sixth grade or

ninth grade, I think there are different,

ways of introducing quantum. Generally,

it's quantum mechanics. Right? Quantum mechanics was a class that, you know, you didn't

normally take till you were in, upper level

classes in, in undergraduate. So I I took

actually, I did take quantum mechanics classes. So, and

it was probably the most complicated,

confusing class that I took. It was, you know, not

my so I'm a mechanical engineering major, so it wasn't something I could put my

hands around. So

trying to get a new generation of people to really

understand quantum means you have to start introducing

these concepts, quantum mechanics or superposition

or entanglement or quadratic or

combinatorial optimization in the math early.

So we need, you know, we need students who are really

see that. You know, they see an opportunity, and they're told these

are the classes you can take, and it will get you there. They'll get you

on the journey. So so that's one way.

I have also seen a lot of books where different authors

are presenting quantum in different ways. You know,

there's Bob Cook's book, Quantum in Pictures.

There's Constantin's book on, programming quantum computers,

and it just works on probability. So it just basically says a quantum computer

is like a probability controller, I'm gonna

simplify it. You know, you just maintain the probabilities

of those variables. Right? I said two to the power n

variables. So how do you change those

probabilities? There's, you know, certain options.

And, actually, that's, for me even, that was, that book is

great because you really begin to see if you're gonna build an algorithm.

You have to think about this is what you have. You have this

device that changes probabilities. Now how do you get to where you want to get

to by doing that? Right? If if you're

building a sand castle and you were given sand,

that's what you have. Right? So now Right. Right. You've got water, a

cup, and you're trying to build a sand castle. Right? So that's what

you're working with. So I think that is you only have to play with that.

You have to kind of get intuitive with this

tool. So, so to build you

know, so you're saying, where are we gonna go from here? I think,

we need better, you know, teaching tools. We need,

people motivated to get into this field early.

Every layer of the stack, I think, have its own challenges. So

whether it's on the hardware level and, you know,

there's multiple kinds of qubits,

photonics or ion traps or superconducting

or quantum dots or, you know, cat

qubits, neutral atom. Each one is

different. Each one, you have to program differently. The

algorithms are different. What you can do with it is different.

So I don't know if in the future we're gonna have these specialists that are,

like, neutral atom specialists and Right. So time

specialists. Right. Right. So so

so the the software layer, the the the coding layer doesn't abstract

away a lot of that or or not enough? Well,

if you think about it, each of these quantum computers

uses a certain physical property. So neutral atoms are using,

a property, where the red where the red bug atom,

grows the outer layer shell grows,

and it uses, the the quantum property where

two qubits can't have the same a different state.

No. Actually, two qubits can't have the same state. So if one

qubit is one, the other one becomes a zero. So

it it forces one of them to change its state if

you want to have a state on one of them. I mean, that's that

is the physical property that they're using on the Redbook

Adam or neutral Adam systems, so like Cuera, Adam Computing,

Inflection, Pascal. Right? Those

companies are using this one specific property.

Now with that property, you can have thousands

of qubits in a lattice, and you can create a

two dem two d structure. You can position the

cubits wherever you want to position them. And then once

you build this, grow this grid radius,

you start impacting cubits with

each other. And so that system naturally solves

the maximum independent set problem. It prevents

Okay. Depending on how far you grow that,

Redbird radius, you, kind of bring

different qubits into that one state where you can't

have two qubits with the same value. So with

that, that's an I mean, the system naturally does

maximum independent set. And with that, they are looking at

what can we do with it. So, you know, they're trying to solve all kinds

of different chemistry problems or optimization problems,

but the system fundamentally

is built like that. And and so I think there's a

lot of nuances and challenges and

opportunities on how that system will be developed,

how those systems will evolve, and what you can do with

them. Now what I just mentioned is the adiabatic

or the, you know, it's a different regime

where the red book radiuses grow when you

shine the the microwave or the laser on

them. But, you can also

then build finer lasers that touch or, you

know, affect each atom independently.

And now you can start teaching each atom as

a qubit and start doing some digital,

gate operations on them. So now you've got kind of

this adiabatic or annealing type of system

along with the red book system or the maximum independent set

system, plus you can do some gate operations.

So I don't know what you can do with that. Right? I mean, this

this is just, like, new technology that's coming out, and there's a lot

of researchers writing papers where they're learning

from these systems. They're trying to use them for different applications.

So it is, is really a

very nuanced field. You cannot you cannot just put it

all in one brush and say all quantum computers are equal. Right. Like,

the photonic systems, they have

very different way of, functioning. You know, you have to send thousands

of qubits through, photons. You have to entangle

them first and then send them into a circuit.

And the algorithm there is called a measurement based

algorithm, kind of like quantum teleportation. So

Oh, okay. That makes a lot of sense now. So, I mean, that's a different

way of even writing or thinking about an algorithm. It's it's almost

like you're you've already got the entanglement

there, and now you're writing an algorithm where you are measuring

one qubit and expecting the other qubit to do what you

want with this one qubit. You know, your

since they're entangled, if you manipulate one qubit, the other one is gonna

change as well. Right. And you're constantly

manipulating one and expecting the other to do something

different. So, it's

again, that's a very different way of even thinking. So the

question is, alright. Well, what can we do with that? How we how is that

gonna be useful in the future? And that's what I'm that's what I'm

talking about. That each of these systems have a lot of nuances, and

you can spend, I think, your whole career working in

one modality, and really

understand it. And it just

depends on where, you know, like, where do you want to actually be in

that software stack? You wanna be at the pulse level where

you're controlling the qubits and sending the microwave or the laser

pulses, the Ravi rotations.

Are you at the control system level? Are you at the

algorithm level? Are you at the use case level?

So and none of this has been triggered out. So

Well, I mean, I think it it's very analogous to

kind of and software engineering, right, where, you know, people get it.

They build up their career and say the financial services industry. Right? And

they're, you know, when labor markets get really tight, they're like, well, no. We

want someone with private equity experience or we want someone with Yeah. From Oregon.

Like, they were but but I think that, like, I think it's probably gonna it's

probably gonna shake out something like that. That'd be my guess. Yeah. No

doubt. No doubt. I mean, you know, it's it's it's like, if

you, like, you know, get an MBA and you can go a million

places with an MBA and go into consulting or you can

go into finance or management or

anything. So and, you know, I mean, I one example I was thinking

of when you were asking me these questions was, you know, like, when

Excel you know, when you, went Excel or what was

it? Note one two three or something like that. Yeah. Yeah. Yeah. Yeah. You

noticed I mean, there were some people

who just got it. Right? They got the the cell

structure and how you can calculate from one cell into another

cell, and you can put a function. And there were other people who just never

got it. You know? No. That makes a lot of sense. That

makes a lot of sense. And, I know Candace is itching to ask a

question, but one I will I just wanna add one last thought. When somebody had

told me that, by and large, the software will abstract a lot

of the underlying hardware thing, it sounded a little too good to be

true. So it sounds like it might be a little too good to be true.

That's basically what you're saying. You you can. I think, you

know, for example, if somebody was trying to build a traveling

salesman problem and,

all you wanted was the the the use the person

the client to put in their,

cities or their locations and the distances and all of that,

then yes, you could potentially have

many layers going into converting

that problem into something that eventually is

solved on that quantum computer. But I think the point I'm

trying to make is that maybe that's not the right

problem for a Rydberg atom system. Or maybe it is that is the right

problem for Rydberg atom system, but it's not the right problem for

a photonic system. Or you know, so we don't know

which problem these different quantum computers

will solve more efficiently. And I I think it would be

like, you know, we have GPUs. So GPUs,

do matrix multiplication, and they became a

natural fit for, a lot of

the matrix multiplication you need to do when you are

creating a three d environment and you have to you do

a rotation or you look from point, you know, from one angle to another

angle. Just that shift in perspective

requires every every element to

be recalculated. Right? And it's the same calculation over and over

again. Right. So the GPU was a natural fit for

that particular matrix multiplication type of a

problem. Now if you were to say, well, can we use it

for all kinds of other things? Well, you probably could,

but is it gonna be the most efficient tool to solve

those problems? So so I think it

is I think it is I mean, obviously, every company would say that, you

know, my my quantum computer can solve every problem, but I don't

think they're gonna say that. And I think what, eventually, we're all gonna

realize is that annealing quantum computers can solve

optimization problems better. Gate

quantum computers are gonna solve certain kinds of problems. If you have,

like, an ion trap where every qubit is connected to every other qubit,

you're gonna be able to solve more

matrix problems where you have, more entanglement

between the variables. But if you have, a superconducting

qubit where one one qubit is connected to two, three,

or four other qubits, that's not gonna scale

very well. So you're gonna have to solve nearest neighbor type of problems

more often on those systems.

And, I you know, there was a company,

who was actually trying to build quantum computers

that were customized to the problem you're solving.

Uh-huh. So, I mean, you know, I think once we figure out

what these devices are, what they can do, what is well, how can we

control them, We might be creating new

kinds of quantum computers to solve specific kinds of real world

problems. So can I you know, so

it's it's still, I think, up in the air?

Interesting. We know you there's a lot of things that you've talked about,

all of which are incredibly fascinating to this curious self. So I'm

gonna ask you a question just kind of to understand something. We

talked about the different kinds of qubits. We've talked about, you

know, how those different times those different types of qubits will be

good for different purposes of real world problems.

I'm curious to know, number one,

is entanglement the same

by definition for each type of qubit that you're

dealing with? And as a follow-up to

that, I would love for you to give us a sixty second

definition of entanglement.

So, I mean, entanglement is a quantum mechanics property

where, one, when you

entangle two separate things, whether it's

photons or electrons, you bring them into one

state. So they'd be from a quantum mechanics perspective, they're not two

things anymore. They're basically one thing with one

state. And so when you change that

state or when you affect that, on one

side, the other side changes naturally.

So, you know, the simple example is that you've got two

photons. You know, one is up and the other one,

let's say you tangle those two full photons with where if one is up, the

other one is up as well. So that is,

let's say, that's correlating those two photons, you know, positively.

You can also correlate them in the opposite direction where one if you detect that

one is up, the other one will always be down. But it

is those two photons have become one state.

And so the property of one and the other is not

different. They're not separate things. They're one thing.

And so in nature,

you can take those two photons apart, you

know, light years apart, but that

state remains entangled so that if you affect one,

you're still affecting the other even though,

there's a distance where light cannot travel from

point a to point b and give it that information that you have,

you know, affected one photon. And this is the idea behind

quantum teleportation or,

and quantum communication. But photon is, you know, is a

light, it can move at the speed of light, and you can have

distances. That same property we're doing on a

chip. So when we are entangling two qubits together on a

chip, you're still entang you're still making them

into one state. And so with that,

you're able to, like I said, correlate

variables, correlate two two things together. And it's

just a a property of nature. And so you're

asking, is that one property for all qubits?

Yes. It is. It's you know, whether, you know, it's and once you

the the actual quantum mechanics property of entanglement is the same.

However, not all quantum computers are

using just entanglement. Like, the red book radius is slightly

different quantum mechanics property when when you

are dealing with, the red book radius encompassing

two atoms. So for

the, you know, the electron shell of one is going over the

electron shell of the other, and they become kind of a entangled

state. So the different,

quantum properties that are being utilized in

these different systems. Interesting.

This has been a fascinating conversation. I really enjoyed it,

and, I don't wanna be respectful of everyone's

time. But we'd love to have you on the show again and and and kinda

deep dive. And, if I do bump into you next

week, I'll bring my book along so you could sign it if you don't mind.

Alright. And No. Definitely. Awesome. And where could

folks find out more about you? Well, LinkedIn is the

best place. So if they do a search for me, Alex Khan, you

know, on LinkedIn, it's Alex Khan

MBA. Okay. And, my company, Aligned

IT, so they can go to

wwwalignedit.com. I've got a lot

of information there. I've got some videos and different,

papers that I've written are all kind of listed over there.

Excellent. So, yeah, those are two places.

Excellent. Excellent. And And I'll let RAI finish the

show. And that, dear quantum curious listeners,

brings us to the end of another episode of Impact Quantum where we

explore the world of quantum computing one superposed

step at a time. Massive thanks to Alex Khan for

joining us and giving us a front row seat to the quantum

evolution. From optimization to entanglement,

from academic ivory towers to Amazon bracket, he's

given us a lot to think about and probably a few

sleepless nights wondering if our spreadsheets are secretly quantum

algorithms in disguise. If you enjoyed this

episode, be sure to subscribe, leave a review, or

better yet recommend us to someone who still thinks quantum is just a

fancy way of saying really small. And remember,

in the quantum world, uncertainty is just another

way of saying infinite possibilities. Until next

time, keep your states coherent and your curiosity entangled.

Cheerio.