If we can advance quantum a little bit faster, while

quantum comes with a power requirement in the terms

of cooling, the actual cost to run the

QPU is almost zero, right? It

really doesn't cost a whole lot to run a QPU.

AI may be approaching its limits, but quantum

computing could be the next leap forward.

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

field of quantum computing. And you don't need to be a PhD, you just

need to be a little bit curious. And with me is the most quantum curious

person I know, Candace Cahouli. How's it going, Candace? It's great.

Today's a wonderful day. I'm really, really excited.

We are going to be speaking with Danny Wall, who

is the founder, CEO, and CTO

at OA Quantum Labs. Hi,

Danny. How are you today? I am fantastic.

How about yourself? Doing all right. It's always

good to hear from folks in a state warmer and

sunnier than where I am. We had our first winter storm warning

here for the season here in the Baltimore, D.C. area.

And kids, kids were— had a late start to school and that always

throws things off. But Candace is an old hat at

snow. It's Montreal. They probably already had like 20 feet already for the season.

No, seriously, like it's true because it always starts. It

usually always starts Halloween. Like you get a little bit in Halloween

just to have a taste. So if you're— if your costume does not fit

over your winter coat, It is not an acceptable costume

here in Montreal, Quebec. But yeah, it's snowing every day.

Like, it just snows every day. But that's just how it is. But you

learn how to deal with it. And so it's just fine. Just very pretty. We

actually get our first snow overnight tonight. Oh,

nice. Oh, you must be in the altitude then. About

5,000, a little over 5,000 feet. Yeah. Oh, okay. Okay.

You're coming to us from sunny New Mexico, or normally sunny New Mexico.

And so tell us, what are you doing? We, in the virtual

green room, we spoke briefly, working on building something really cool.

Yeah, so I'm building a quantum lab

out here. So OA

Quantum Labs is not just a quantum lab, so we don't do

just research. All of our research is

100% geared towards

creating true commercial application of quantum

technology. So a good example is we are also

the owners of multiple AI companies,

which we have now acquired. So as

part of that, we are applying

quantum computing in its current state of the

science to multiple different

components within the AI

ecosystem. Interesting. Okay,

okay. How so? Like what particularly, like, I'm curious

to see what the intersection of quantum and AI, sorry. Okay, so the very first

things that we did was reducing

training cost and time. That was the

easiest place where quantum could make the

biggest impact. And this was back when we

were still on, you know, sub-100

qubit systems, really in the 50s somewhere, logical qubits.

Now what we are doing is we're also improving

AI inference in a number of areas.

So if you, I'm going to

oversimplify this a little bit to the point of it almost

being wrong, but it provides a good analogy.

One of the things that quantum computing is really, really, really

good at is math, right? It does

math and complex math very, very quickly. So

if you think of a quantum computer almost

like a super ridiculous

calculator, you can use AI

for all of its inference, but when math needs to take place,

you throw the math to the quantum computer, get the math back, and

done, and then it comes back. Where this works the best is

in materials. When you're, when you're doing anything with

materials or molecules.

Interesting. I mean, that

makes sense, right? Because there's definitely a tight correlation between

quantum effects and chemistry. Yes. And it sounds a

bit like, like a GPU, right? In a sense, right? Like almost. You—

that's what I said, a bit like, right? Like you're sending off whether it's a

video game, whether it's AI or neural network training,

you're just saying, here's a bunch of stuff, GPU, go for it, right?

Yes. And then you come back with an answer. Yeah, yeah. Only you're saying

QPU, go for it. QPU, yeah, right. Yeah,

right. I'm hoping that term catches. I'm hoping that term catches on. Yeah, yeah,

exactly. We do. We hear it a lot. Yeah. So QPU

is actually already a term on that thing, but

Quantum computing, the architecture of quantum computers is different.

It's not really the same architecture where you have

a central processing unit and then memory

sits somewhere else, and then you have

buses between your CPU and your— it's not like

that in the quantum world. Interesting. The

memory is, let's call it, on-chip.

Right. Well, there's also kind of— there's also the thing, like,

once you read the memory, do you collapse the quantum state? I know that once

you get into kind of the brass tacks of, you know, beyond

like the theoretical, like you start to get some— it starts to get weird

real fast, right? Because like, you know, how do you— debugging a

quantum system, right? We've already talked with some other guests about that. Like, that's, you

know, how do you, you know, if you— how do you step through the code,

right? And like you peek at the variables. Well, as soon as you do that

in a quantum system, You're collapsing. You're collapsing and

you kind of lose the advantage of quantum. Yeah, yeah, yeah. So like, I, I

would imagine that there's a lot of these little gotchas that nobody's really fully kind

of worked through just yet. Um, so there are, and this

is why, um, as long

as you understand what the limitations are,

quantum has some really significant advantages

right now. This is the reason

why JP Morgan, as an example, is spending $1.5 billion

on quantum computing, because there are certain things, certain

mathematics, QAOA, right? Quantum Approximate

Optimization Algorithms, right? Where you're using

quantum to do

certain mathematical functions that just take too

long, and they're, you know, take a second or two on quantum,

they take minutes on classical. And, and when

you're in the world of finance, you know, a minute is too

long. Same goes with,

um, um, advanced correlation algorithms. You get into quantum advanced

correlation algorithms, and those run really

ridiculously a lot faster. Right.

But not for all problems, just certain. Yes, that's what I'm saying. Yeah.

When you, when you understand what problem domains quantum is

really good at, it becomes a lot easier,

faster to start applying commercial application

to it. Gotcha. So you

talked about the financial sector. What other, what

other industries are you think, do you think are the most primed to benefit

first? Okay, so where

it's already benefiting is anything where you need molecular

or quantum knowledge or effects or whatever, right?

So material sciences is a big one.

In partnership with Ursulaing Quantum

Innovations, we have created the single

most advanced materials, let's call

it, engineering platform in the

world, right? Our nearest

competitor is— oh my gosh, I was just going to

say them.

They just got this massive amount of money and I totally

spaced their name. Cusp AI, I think that's what it is.

They— so they're supposed to be a material science platform. They need 6

months and an entire team of material sciences

scientists to do almost anything. And we

were spinning out new materials at the pace of a new one every

2 weeks. Oh, wow. Okay. Yeah. Like,

we have— we got— we created a material that

is stronger and harder than

carbon fiber, but about half the price to manufacture.

We created brand new heat shielding that

survives multiple multiple reentries and is

far less expensive to produce than what SpaceX is using today.

We created a new material, a new

advanced material for

heat management. It basically pulls heat away to use as like heat sinks

and those kinds of things that is far better than

anything that exists. So we finally— we were creating so

many new materials so fast that we overran the sales team's ability

to keep up, so we spun that out into a brand new company,

and now that, that guy is off to the races.

Um, and, uh, so the other place where,

um, it helps a lot is again in modeling, uh, quantum

effects. I was able to create a whole brand new

GPU kernel that is far better than Flash

Attention V2 because I modeled how

electrons flow through a GPU and therefore was

able to optimize the code for how the attention

mechanisms work on inference.

Interesting.

Interesting.

What sorts of hardware does this run on? I'm sorry,

Candace. No, no, go ahead. What sorts of hardware? Is it hardware agnostic? Oh, no,

no, no. So I mean, I wrote it to be very specific

to the NVIDIA H100,

A100, and above better, right? That makes sense. Yeah, this

is, this, I, when I wrote it, it was when the,

uh, uh, X was coming out with all the news about their brand

new Colossus supercluster, blah, blah, blah. And I was like, I wonder

if I could, since I can model, um, molecules

and all that kind of, and quantum effects and all that other kinds of

good stuff, can I model how things flow

through a GPU and therefore improve on

improve on how the attention mechanism

works within a GPU, and it's better by a

lot. Interesting. Between 1.5 and

3x improved inference. Oh, wow.

Yeah. Depending on where you

are in the stack, do you need sparse attention or—

all of a sudden I drew a blank on the name— sparse attention or

Wow. I deal with this every day and all of a sudden I blanked. It

happens to the best of us. Of course, attention is the thing that they— that

you really kind of need the least of.

But yeah, so I got you. Okay. I mean, that makes sense.

Look, honestly, everything you're talking about is so incredibly exciting. So how

do newcomers interested in quantum and AI

researchers, entrepreneurs, investors,

What advice or first steps would you recommend today to get

them involved meaningfully? Okay, so

that's really gonna depend on which one of those you're talking

about. For investors,

the biggest thing that I would say is to,

is in two areas. Number one, look for people that

don't necessarily have the pedigree. It becomes really,

really easy in quantum to assume

that somebody must— that you got to have the PhD, and the more

PhDs on the team, the better. And you see a lot of that.

You look at D-Wave, Quantinuum, Qera, right? You

look at all of these guys, and what you see is this long list of

PhDs. And the truth is, is that companies like mine are

completely blowing their doors off. Like, I don't—

I'm rapidly getting to the point I don't even know how they're going to keep

up. We have created a quantum

error correction algorithm that

reduces physical to logical overhead by 100. Wow.

So yeah, it's better. It's better by so much.

It's almost

hard to believe. And we had to run it on

the IBM Lima and Bellum benchmarks. We had to run

that thing 3 times because we sort of assumed that

it couldn't have been right the first time. You know, like, how

is this good? So,

so number one, look for people that are actually doing it.

Number two, look for people that, that don't just need a check.

Right, right. So for— too,

too often investors are giving money to people either because

of pedigree or because they go, oh, this guy has got,

you know, two successful exits. So probably they can do a third one. But you

look back at history and that's not true at all, right? Look at,

look at Pets.com. Pets.com from, you know, way back in

the dot-bomb era, right? It was started by multiple

people that had done multiple different successful exits. And that

thing was a disaster, right? No, it's true.

And, you know, you mentioned that and 3DO, do you remember? Speaking

of the '90s. Yes. 3DO, 3DO was like, I remember the

Wired magazine article. Cover was like the digital start of the

rise of the digital supergroup. And aside from like a handful of people

who remember the '90s, no one knows what 3DL was, right?

No, it was just like— and you're right, like pedigree. I think, I

think there's a temptation. I think this brings up a deep point. Like, there's a

temptation to overbuy

on pedigree. Yes. Whether,

whether it's in quantum, the assumption that, well, how could you possibly

understand quantum if you don't have a PhD? And the answer is

look at the solutions that are created, right? And then, and

then the second thing, if somebody— just because somebody says I have

something, or maybe they actually do— I mean, and this is something

most venture capitalists or investors are already pretty good at,

is going, let me see your customers. Um, you

know, is there actually market traction for it?

At the end of the day, a company— uh, so OA Quantum

Labs isn't looking for an investor. But assuming that we were,

we have— we don't only have solutions, we have customers. So because we have

solutions and customers, like, I don't need your money. If I was going to—

if I was going to take money from an investor,

it would only be because that investor was bringing me

some kind of strategic alliance that

I like, that it's worth more than the equity that I

would give up. Does that make sense? No, I mean, that makes sense. Yeah, no,

I think, I think it's an interesting point you bring up. Like,

um, it's about selling solutions. Yeah, not the science,

right? It's almost like you— we got to give you a free copy of our

book on, uh, selling quantum solutions, right?

Um, because like, it, it's almost like you've read it. Like, because you're basically saying

effectively the same thing, just like it. Yeah, you know that you're right.

Like, if you can if you could prove the value— and I forget what it

was, it was like months versus weeks— like, you could prove that real value to

a business, doesn't really matter how many PhDs you have. I mean, obviously, right,

obviously somebody has to, you know, check the numbers and make sure the answers you

get are, you know, legit. Uh, but I mean, at some point

it's really where the rubber meets the road, right? Like, I would not have thought,

uh, if you look at pets.com compared to Amazon,

um, Who, I mean, in the '90s, people would have assumed Pets.com would have won.

Barnes Noble, like the same story. Fun fact, I worked at

BarnesandNoble.com. Oh, wow. I was the first

webmaster there.

Wow. Underestimating people who are relentless is a

mistake. Yes. Yeah. So when

it comes to, let's say it's a developer who's interested

in quantum, I would

say that the best way for a developer to

get involved in quantum is to get a Quantum Cloud account

and to start creating

stuff. Don't mess around with research.

Don't, like, I mean, yeah, take some time to learn, you

know, Qisk or whatever the

different, DLLs that get wrapped up into Python.

But yeah, take some time to learn what you're writing. But as soon as

you can learn something, start creating something from

it. Don't sit around and wait, create something from

it because there is

no substitute for experience. The

problem that most developers have is that they've spent their entire

lives either A, in school being taught, or

B, in their careers on classical binary

digital computers that are very time-dependent

and sequentially processed. Whereas quantum

is non-time-dependent and simultaneously processed.

And therefore the way you have to even think

about how you architect a

solution is different. How you think about how you're going to write the code is

different, and you don't know those things, or it's— I would

be better to say it's hard to understand those things until

you start writing the code and start seeing what

happens, right? Right. So that's a good way to

put it. Yeah. Sorry, Candice, I'll be quiet now. No, I'm just

thinking about, you know, you come from such a

unique background because most leaders are, you know, they're either in

the quantum world or they're in the AI world.

But because you're in both, it gives you such a

unique advantage to have this dual

fluency. So how do you find that that affects, you know, you

being founder and CTO and CEO of of your

company? It definitely in a lot of

ways makes the commercial potential

and applicability of what I'm

doing better or easier. It means

that when I am selling solutions, I

can articulate to people like, this

is, this is why what, what we're doing works

better., right? And I'm able to

speak to, you know, the CTOs of people. AI is

getting to be understood well

enough now in the enterprise and all those kinds

of things that when I start to explain, okay, this is where

the AI is and this is where the quantum is and this is why the

quantum matters. It's a pretty simple conversation

to have these days, especially now that they

know that

I'm bringing quantum enhancement. I'm not saying this

is a quantum solution, it's a

quantum enhancement. And that's— it's a very

subtle distinction, but the gap between them is, you know,

about the distance from one side of the Grand Canyon to

the other. Well, it also frames the conversation differently. Sorry, Ken. No, I

was thinking, so does that mean that the AI accelerates

the quantum? No, the other way around. So the quantum accelerates

the AI?

Yes. Yes. Yeah. And, and it's because it's quantum

accelerating the AI By having the discussion

in that way, it means that

the business people can understand it better. It means I

can now have a much

quicker conversation about this is what it means to your bottom line, because at the

end of the day, that's what really matters, right?

Right. If you're going to go to any enterprise, you had better be

able to answer be able to say that either A,

my solution is going to improve revenue, or B, it's going to

reduce your cost and therefore improve profit. If you can't say

it's going to do A, B, or both, don't

even bother having the discussion because it

doesn't matter, right? It's all about

solutioning. Yes. Oh yes, not tech for the sake of tech. I mean, right, tech

for the sake of tech is

an academic conversation, correct? And that's fine for

academia, but not outside of academia, correct? And

I'm— and to

me, one, quantum has gone far enough

now that it no longer even should be

in academia. And this is why you're seeing, even though everybody's— there's

been a lot of news stories lately about, you know, the bursting of the

quantum bubble. Or whatever. And D-Wave

and Quantinuum and Rigetti have all been, let's call it punished a

little bit. But the truth is, is that

as we start having more of a

business discussion, this is the business problems that we are solving

right now today, the more that

discussion goes away because now quantum starts moving into the data center

and it really needs to get there for there to

be additional significant investment investment to improve the technology.

That makes a lot of sense.

Yeah, yeah, we got to get it out of the research lab and into

the enterprise. Very important.

So when, if you're to look back earlier in,

in your work, when, when you had

that moment where you realized that this isn't just something theoretical,

but this is actually something that I

can commercialize What clicked

for you? Okay, so one of the companies that

I acquired is HughieBT. I was originally the CTO

of HughieBT.

Um, so

at HughieBT, we have the most

advanced digital identity solution by a very wide margin.

Nobody else is even close. And, and it's a— we use

behavioral biometrics as a way of, uh, it's—

we are 99 point and then add

7 nines percent of ability

to distinguish between one human and another human.

And because it's that accurate, it means we are that

accurate also distinguishing between a deepfake. I have had

people create deepfakes of themselves and not be able to defeat,

um, our solution. Okay.

Yeah, so the— when it clicked was

when, um, the training was taking too long, and I was like, okay, well, what

can I do? What can I do to this

stupid thing? Um, and this is one of

these weird

sort of, um, so AI is an odd thing in general. It's, it

can be odd. I, I am well known

for saying that AI is

really some shockingly simple

algorithms, and it is about as intelligent actually

as your calculator, right? Everybody wants to talk

about, you know, is AI sentient? Is AI conscious? Is AI, you

know, and how soon are we going to get to AGI? I don't think we're

going to get to AGI anytime soon. I really don't.

In fact, they've tried

to change where AGI is from it being

able to reason as good as a human to simply

being able— being as— what's the word

they use? Not learn. It's like adaptability or something. Like,

they changed the bar for what's going to be

considered AGI from reasoning capability to adaptability

or something like that. And I just rolled my eyes and went, well, this

is stupid. To me, it's not. Yeah, if you can't

reason as good as even, you know,

an average IQ person, then that's

not artificial general intelligence. It's just not.

So anyway, I Just on

a lark, I asked the

AI to consider itself as a

high IQ materials scientist and to give

me ways that I could

improve the speed of training

of the application. What it came up with

was basically use quantum computing and it also

output a whole bunch bunch of Cirq code. And I was

like, well, this is interesting. So just as

a hint to your audience, I know this is a

quantum thing, this, but, but just as a sort of trick with

AI, if you tell an AI

to act in a role that

is only sort of tertiary to

what its actual thing that you're asking it to do, let's say you wanted to

review code, Tell it that it's a chemist and to review

the code from the viewpoint of a chemist. It

will actually be more, for lack of a

better word, creative. I know AI isn't actually

creative, but sort of. It comes up with some

really interesting responses that I have

found dramatically improves it often,

its output. Because of its having to, like

I said, for lack of a better word, be creative. But anyway, so the

very first thing that I did was

implement quantum to improve the training

of HuGPT. Then that grew into improving the

inference of HuGPT. But in improving the

inference of HuGPT, I sort of,

by accident, for lack of a better way of putting it, created

this system for how molecules and all of those kinds

of things are modeled. I created a physics-informed

neural network. Let me rephrase that, a

quantum-enhanced physics-informed neural network. That then grew to where I

was using PINs, PINOs. So,

PIN, physics-informed neural network, physics-informed neural

operator, GAN, which is a graph

neural network, and a GNO, which is a graph

neural operator. I started putting all of these things together and stuck quantum in the

middle of it for doing the math, and then that

grew into materials and grew into molecular modeling

and all those

things. It came to me, for lack of a

better word, by

accident because of output from, from

an AI. And then just from deep

diving into quantum is that's how these

things happened.

Interesting. What misconceptions do you run into the most when people

hear AI plus quantum, and how do you try to

reframe the conversation so they understand what's actually

possible? The biggest one is they think I'm running the AI

on quantum. Right? That's the biggest one. They go, you're running an AI on quantum.

And then, you know, we go back into the whole, you know,

is it conscious or whatever thing?

And which I admittedly have a pretty

low tolerance for. It irritates me when I hear,

you know, people wanting to talk about you know, how intelligent

they are, that they might be conscious or might be sentient or

whatever. That stuff really is a pet peeve. I don't know why it drives me

so crazy, but

it does. But so anyway, that's the first misconception.

The second misconception, and it comes from people

within the AI industry, is the

belief that quantum doesn't really

have commercial application, that it doesn't really apply

to AI, and Oh, you're just playing a game.

You're not, you're not really doing what you're saying. You're not

really doing blah, blah, blah. I'm like, you know, it's kind of hard to

argue with the results, right? At the end of

the day, you ask, you give me a problem domain for a

material and I can spin out that material in 2 weeks. You tell me how

I'm doing that without quantum enhancing a lot of

different things. Right. And my nearest competitor needs 6

months. The nearest competitor from them needs

18 months. Right. Schrödinger needs

18 months. Right. There's a lot to that, right? Like,

you know, there's this idea of

speed that Grant Cardone, one of the, one of my favorite kind of sales authors.

Yeah. I love him. Yeah, everyone, you either love him or you hate him. There's

nobody in the middle. But, um, you know, he has a phrase that, that really

stuck with me. It's called speed is the new big.

Yes, yes, 100% believe that. The phrase I use all the

time is money loves

speed. Yep. Oh, I like that. That's true too. That's

quotable, right? I, I actually think I got that one from

Jay Abraham. I don't know if you remember him or not, but he's another big

sales guy from, from like the

'90s. Interesting. So you're building in a field where things

are evolving daily. Yeah. How do you

stay ahead? Okay,

so here's— things

evolve fast. But when you're in the field,

some of the times you almost wish they would

evolve faster, especially in

quantum. So quantum error

correction has two separate problems. One, you want

to maintain coherence for as long as possible, and number two, you

want to prevent decoherence.

Right? Two sides of the same fence, let's

call it. So because of

the nature of qubits and quantum and all of that kind of—

and all of that, it's a lot

harder. Those two pieces of the puzzle are a lot harder than it sounds. So

even though I've got this really great

quantum error correction algorithm where we can

maintain coherence for about 4x longer, so instead

instead of about

300 microseconds, we're getting

about 1.3 milliseconds we can maintain

coherence, right? The best that we have been able to do on

the decoherence side is predicting, well, these

qubits are likely to decohere, therefore we can ignore those on the other side

of the gate, right? 'Cause why

pay attention? Sort of cut down on the amount of

noise because we're ignoring the, we're

ignoring the qubits that decohered. So you're almost doing quality

assurance or QA on the qubits? Yeah, that's, that's actually a really good way

of putting it.

But we— there, there still needs to be a lot more work done

in the lab on

this aspect of preventing decoherence

and maintaining coherence, because there's only so much that can

be done on the software side, so let's call it, or

the kernel side, where

for that preventing decoherence

or maintaining coherence, right? I can help

the maintaining of coherence some, right? Like I said,

extend it about 4x, But that's the best

I can possibly get out of it. I'm not gonna get— 'cause now it's

a hardware issue, right? I can only do

so much. And this has been a problem for really a very,

very, very long— since quantum started. And it

hasn't, really hasn't improved a

whole lot. So that's a

big one. We still need a lot more work in the lab

because until we can solve the coherence

decoherence issue, scaling beyond about where we are

now is going to be near impossible because there's just too

much noise. What do you think it's going to take to solve

that problem?

Materials. Like new materials to be developed that the qubits, the quantum systems,

are made out of?

Absolutely. So I personally am

convinced that that really is the

major issue, is that part of the reason why we're having

these coherence problems is that the materials

aren't sufficient. And I can say I can spin

out new materials once every 2 weeks, but number one, I can

only sell so many. And getting these

new materials through into the companies that are doing

the research, IBM, Google,

Continuum, Rigetti, QuEra, those guys,

that can only happen so fast. Unless I'm physically part

of their engineering teams, which of course I'm not. I've got my

own company, right? So, um, you know, I would

like to have a much more in-depth discussion with these

guys about why, why their

materials are causing the decoherence, even though I suspect they kind of

know it, um, so that way those kinds of problems can be solved. But even

once the new materials have been engineered, then they've gotta get actually

into the QPU. Like, there's process

that happens with this. So while from the outside,

to go back to something, Candice, you had said before,

it seems like things are

moving fast, in a lot of ways, they still need to

move faster. Because quantum, we

need, AI right now is starting to bounce

up against

theoretical maximums. So because it's starting to bounce up against

theoretical maximums, it's— this is why once we

hit about GPT-3, you can almost

draw a line there and you can see that the pace

of AI improvement started slowing down and

we started we stopped going from, it almost seemed

like every few months there was

this massive new improvements that we were getting out

of AI. And lately all you're getting is

incremental, very slow incremental

improvements. Yeah, context windows are getting a little bit longer.

Yeah, now it can remember past conversations a little bit better,

or its ability to reason through code is slightly

improved. But that's all we're getting. And we're getting

that at the expense of massive

new power requirements. Yeah, that's the big

issue, isn't it? Yeah. Whereas if we

can advance quantum a little bit faster, while

quantum comes with a power requirement in the terms

of cooling, the actual cost to run the QPU

is almost zero. Right? It really doesn't cost

a whole lot to run a QPU, right?

Beyond the cooling, right? The cooling is, of course, is

expensive, right? I'm not saying that the dilution fridges, you

know, they're not cheap to buy them alone,

you know, $500,000 per.

And then the cost to run them is, you know, you got to keep it,

you know, we're in dot Kelvin

range, But assuming we can get beyond those

with materials, we can find that we're

able to push AI forward a

lot because we really need to start running some of these

neural networks, especially

convolutional, on

quantum. Right. Interesting. And for those that are not familiar with convolutional neural networks, they're

a type of neural network architecture that's really good for

image processing, typically. Images and video.

Yeah. Yeah. Somebody had an experimental use case for

them. I'm sorry, go ahead, Candace. No, I was wondering, has there

been a breakthrough or a micro-innovation

inside your lab that may not have made headlines

but signals like a major shift in

what's possible? Uh, well, there's been a few of them. Um, the fact that I

can engineer new materials in 2 weeks, um, is a

big one. Uh, that's, that's a really, really, really

big one. Um, and, and like I said, we completely overran, um,

the, you know, the ability of a sales team to even possibly

keep up. Um, we have some, some of the new materials

we have, um

going to North American Stainless or US Steel,

and then also to Ford. So some of the

heat management, like heat shielding and all that kind of good stuff, Ford will

be looking at shortly. But when you're talking about

materials, when you create, engineer a material, especially when

it's in silico, then they say, okay, well, now you got

to create the thing. And then once you create the thing, then you have to

test it, and then once you test it, then it has to get rolled into

production and blah, blah, blah, right? Right. Yeah,

but, but, um, so the materials is really, really a

big one. Um, we haven't, um,

uh, announced it broadly at all,

uh, partially because it would be, it would be too

easy to be too overwhelmed too fast.

Um, so that's a big one. Um, the, the quantum error

encryption would definitely be another one. We aren't talking—

the only people we've talked to about our QEC so far

is IBM. And that's because we benchmark it. We did our benchmarking

on their systems. So, you know, they're a pretty easy one to have

that discussion with. But, you know, IBM is a behemoth, right?

It takes them— even IBM Quantum, it

takes forever, you know, to get anywhere. But, you

know, they're an obvious first place. And our quantum encryption algorithm

is even agnostic. The, the— it doesn't matter whose hardware

it is, it's going— the, the error correction is going to work no

matter what, um, you know, whether it's D-Wave or Continuum

or whoever. Um, so

those would— yeah, the error correction would be a big one, and

the, our— the materials, the— I, I say it's

a quantum-enhanced pin, but that's only to simplify the discussion.

There 4 different neural networks, and then what I call a

controlling neural network that sits above it, and sort of in the middle, to think

about it architecture-wise, in the middle is

where the quantum enhancement sits, and the various neural

networks sort of all talk to each other and talk

to the quantum as a way of making this thing run

so

fast. Interesting. What— I know Candace usually asks

this question. What's the biggest misconception out

there about your business and kind of what you're up to? So

there'll be— there, I guess I have to say there's two of them, and it

depends on who you're talking to. Number one is in

the business, too much of business outside of finance. This isn't so

much true of— in the finance sector, they're starting to

understand quantum. Because of QAOA, they understand, they're starting to understand. But outside

of the financial vertical, there's still

too much belief that quantum is a

laboratory and research effort and there's no real

true commercial applicability to it. And that's just

completely false. That's probably the biggest

one. And if we're talking

about developers, I like to give this sort of as

a hint. Most developers are

using quantum computing almost like a

trinary system, and I've had quantum developers disagree with me

on this. And then I say, okay, let's look at your code, and I prove

it to them. Most people

are using quantum computers almost like a

trinary computational device. You're gonna have to explain that. I roughly know what that

is, but, uh, okay, so I like to be enlightened on terms of the

difference between that because I've had this discussion and I didn't have a good

answer to counter the statement. Yeah, okay, so, so

right now, digital computers, classical computers, are binary,

all right? It's a 1 or a 0 and that's it,

okay? Most people are using, uh,

quantum computers the same way. So think of 1 or 0 as spin up,

spin down, right? And then you

have superposition. Okay, so there— so trinary is spin up,

spin down, superposition. Those are the

3. Oh, okay. And they don't go any deeper with

superposition. They stop there.

Superposition actually means more than

just both. Which is what superposition sort of means, but

it doesn't just mean only

that. Um,

so it's— how do I put this in a way that I don't give away

too much of my own secret sauce? Um,

so you could treat— you could treat it that way. You could treat like a

trinary system and you wouldn't be wrong, but you're not taking advantage

of the superposition. Advantage, yes. Right. So for most normal

people, I think a good way to look at this as,

um, like a checkbox on a, on an online form, right? It's

either checked, unchecked,

or some designers will have a third space means you never touched

it, right? So it's either kind of like yes, no, or unknown would be another

one, right? Right. So you could almost use it like a maybe. So where the—

what most developers are effectively doing it is using it like

a maybe. Yes, no, maybe, on, off,

don't know, right? But strictly speaking, superposition

is yes and no at the same time. Correct.

And, and it's not that it's yes, so yes, it's yes and no at the

same time. But so let's, let's—

I don't know, this might be getting a little bit deep into the woods, but

let's look at Schrödinger's cat for a minute,

right? Okay. The— in the thought experiment, it's the

cat can be thought of as alive and dead at the same

time. But here's the truth: the cat could also be

thought of as in the process of

dying. Okay, so it's not just alive and dead at the

same time, it's alive, dead, and in the process of dying. And if it's in

the process of dying, how far along the process

of dying

is it? Oh, okay,

okay. So, so there's— there is a saying from,

um, a personal development guy that I really like a lot.

Most people in the personal development space, the minute they say

quantum physics says, the next words that come out of their mouth

are nonsense. Okay, nonsense. Uh,

Dr. Joe Dispenza does a really good job when he says quantum

physics says, the next words that come out of his mouth mouth are probably going

to be right. When they are wrong,

it's usually he's in the early part of

his discussion, he's trying to get you to understand something, and if you listen to

him a little bit longer, he makes it correct.

So superposition is not just both. A better way

of describing superposition would be it is a

definition of all possible

possibilities. Gotcha. Okay, that's what superposition actually is. So if

it's all possible possibilities, that opens up more

than just trinary computation. And that is about

as far deep into the woods on that subject as I will

get, because that's fair. We're at the top, we're towards the top of the hour,

so like, it's, it's probably— we'd love to have you

back because, uh, um, yeah, no, I, I, I feel you, like There's

a lot to unpack there. I can kind of sense like,

oh, this— it's kind of like you pull a thread on a sweater, like, oh,

it's not just this little bit. It's actually way more

than I anticipated. Yeah. And then you start getting into the

woods of what is superposition exactly and whether it's,

you know, we're talking about when you're measuring spin up,

spin down, it's because you have collapsed the particle

into a into a particle, and superposition is actually just

the waveform, right? And that's right. And then you start getting the double slit and

all those kinds of fun things. Yeah,

causality, you start— yeah, yeah, yeah. So anyway, I can,

I can geek out on this for kind of— we'd love to have another— you

back on the show. Yeah, absolutely. This has been a fantastic

conversation. We really, really appreciate your time. I thank you. I've had a lot

of fun. They're skanking, skanking in time.

Black holes are wailing in a horn line so fine. From plank scales to

planets, they're connecting the dots. Candace and Frank, they're

the

cosmic hotshot. Quantum Podcast, turn it up fast. Candace

and Frank blowing my mind at last. Quantum Podcast,

they're breaking the mold. Science has got beats.

It's bold and it's gold.