We are about to geek out and I for one

am delighted. I'm Molly Wood. I'm really

happy to be here with both of you. So

you've probably heard the saying there

are so many things in technology that

have been 5 years away for a long time,

right? It was full self-driving cars. In

my world of climate tech it's fusion

energy.

It's AGI maybe depending on who you talk

to and it is quantum computing which has

been 5 years away for Jerry if you don't

correct me if I'm wrong 30 years.

And yet

quietly while everyone else has been

talking about AI quantum computing has

been doing stuff

commercializing helping people. There

are actual results. It's now being used

alongside classical computers to tackle

problems that traditional computing

still can't solve

including complex molecular and chemical

challenges, real implications for drug

discovery, material science, the clean

energy transition and a bunch of things

we haven't

thought of yet. So Jerry Tower is an IBM

fellow and CTO of quantum centric

supercomputing at IBM.

>> [applause]

>> Jerry is a is a turbo smarty definitely

clap around. K Wayme Timmerman is CEO of

the Chicago Quantum Exchange a

consortium of leading universities and

national laboratories advancing quantum

research workforce development and the

quantum economy economy economy.

Let's just call it that.

Right?

>> [applause]

>> The economy. Okay, so let's start there.

What is happening in the economy

and with quantum computing specifically?

I'm going to start with you Jerry and

tell us a little bit about your

day-to-day if you wouldn't mind.

>> Yeah, sure. So

my background is actually in physics and

actually building a lot of the

underlying devices that has turned into

this this this quantum computing craze.

But, today my role as CTO of Quantum

Circuit Supercomputing is actually

planning for how quantum computing can

become a part of our lives as part of

the supercomputers of the future. You

know, everybody's talking about data

centers and thinking about AI in terms

of GPUs and CPUs. So, my job is to work

out for IBM and also for a strategic

landscape, how QPUs or quantum

processing units becomes a part of that

equation.

Um but, I'd say that, you know, why

you 5 years away we would keep talking

about that, but actually there's a lot

that's been happening, right? And and

uh some of the dis- discourse has really

shifted, I'd say, in the last

two, three years from

uh just some of the underlying physics,

right? And understanding of the science

of the devices

to actually using these capabilities,

real quantum computers exist that can

push the limits of what we can compute.

If it's in chemistry or if it's in some

of the really hardest problems that

people are trying to tackle with

advanced supercomputers, now we're

starting to see workflows that are using

quantum as part of it. And that's what's

really exciting, that we're kind of

starting to have the commercialization

discussion as well of how this going to

impact our world.

>> Okay, more on that in a minute. Kate,

tell us about your day-to-day and

building this kind of the ecosystem that

really is the key to deployment and

adoption of any new technology, right?

>> Yeah, happy to. Um so, first of all, my

background is in neuroscience, so I'm

generally an example of how anyone could

get into quantum.

Um and I would say my typical day really

is spent uh I will easily have a

conversation with a researcher within a

university who's working on kind of

fundamental discoveries that they're

going to use for quantum or to develop

develop new quantum tools. And then I'll

next talk to some startup company that

may not have even incorporated yet or

maybe has two people on the ground and

maybe they need help fundraising or

developing strategic partnerships and

then I'll work with a large Fortune 500

company like IBM and have conversations.

So, really being able to

kind of span that entire economy

as you talked about.

>> It's a thing now.

>> Yeah, I love it.

>> Say more both of you if you wouldn't

mind about what has happened in the last

two or three years. The the from it

sounds like the ecosystem conversation

and give us just like a teeny bit of the

physics.

>> Yeah, so um

>> We can keep up. You can keep up.

>> So, 10 years ago actually we made a big

big

shift when we put the first quantum

computer on the cloud. At the time it

was only five cubits, okay? So, you can

think about scaling in terms of bits,

right? In terms of how how how powerful

is your quantum computer, what can what

it can do.

10 years ago five cubits really was

really a toy, something that you could

learn about

how you might actually program a quantum

computer just to start.

But then we shifted in the last few

years to 100 cubit plus quantum

processors.

And that shift really brought out the

capabilities of pushing the envelope of

what you could actually explore on these

things.

You actually can run programs on these

quantum computers which you cannot

simulate with any classical computer.

So, that's a big difference. It's a It

says that it's something that

it's not clear what is useful for, but

we know that it does something beyond

what you can do with just your

traditional computers. So, a lot of the

the the phase we're in is this

exploration. How do we use that

fact that it can do something you can't

do classically

as part of a way to discover new

algorithms and new ideas that might

really you know push the envelope for

some kind of problem of economic

interest.

>> And then Kate, how do you talk to people

about translating that into value and

what are the types of value you're

seeing?

>> Yeah, and I think as Jerry said, you

know, we have definitely seen this

transition over the past few years. I

think particularly one that's for now

really focused on kind of scientific

utility. Who is it that's really good at

working on previously intractable

problems? It's scientists. So, taking

quantum systems and particularly quantum

computing

and working with physicists, with

chemists on things like materials, new

materials development, new drug

discovery, things like that. So, I think

having those kind of deep meaty

conversations is where we see a lot of

activity right now. And then also I

think to that point about conversations

about you know, the broader economy, the

fact that quantum systems, yes, include

computing but also includes things like

quantum sensors that have been deployed

on the International Space Station,

flown around on aircraft measuring

things like gravity and magnetic field

and already deployed today. And so, I

think we have a path towards quantum

system deployment. So, we're not kind of

doing this all by ourselves. We we can

see at least some steps moving forward.

>> Right. And then what does the actual

division of labor look like now? Like

we're saying, you know, in our intro,

quantum computing is happening alongside

classical systems. What does that look

like in practice?

>> Yeah, so you know, certainly from the

point of view of the infrastructure of

this, there's still a lot of scientific

engineering and physicists that are

building the actual machines and working

on improving the machines. But I say

that now what we're seeing a lot of the

new workforce is coming through is

actually in the programming of these,

developing the algorithms, developing

the developing the use cases. A lot of

the scientific compute that Kate just

mentioned,

in tapping into for example the the

national laboratories, right? And all

the scientists there. Those are some of

the heaviest users of advanced compute.

If you just look back a few years ago, I

mean a few decades ago, maybe to when

the GPU craze started, right? At first,

the GPUs start started with

doing graphics for grand games.

>> Mhm.

>> But then they were able to to to get a

hold of all these scientific use cases

in places like the national labs, where

they're just looking to get a new tool,

right? That they can push the envelopes

of mathematics for what they can

actually use these for. And we're trying

to tap into that same energy, right?

Like we have this new tool, it's

scaling, it's pushing the limits of what

we're going to do. Um and how are they

going to actually leverage it? And it's

really those scientists that we're

looking to.

>> You know, Jerry just said in some ways

that we're still figuring out what we

can do. Like now that we can do it, now

we figure out what we can do, which has

been the history of technology as long

as it's existed, right? We didn't know

what we could do with 4G broadband in

every single phone plus always-on GPS

until Uber was like, "We got a plan." Um

we're kind of in that stage, right? Is

it fair to say that that's a little bit

where we are with quantum computing

exists and it works. Now what?

>> I think it's that there's actually some

very known use cases, right? There are

ones that we know where

>> always like cool science ones that no

one knows of.

>> Yeah, and like you know, I think you

people have heard about Richard

Feynman's dream, right? Of actually

simulating molecular structure using

these quantum computers.

>> Mhm.

>> And uh

there's a path to get there. And there

there's known algorithms that we know

will get advantage for, but it takes a

road map of of improving these systems

and the quantum computers to the point

that we get there. But it's the point is

in the meantime, we don't need to just

sit and wait. And this is where we are

building these ecosystems early and to

really engage and use the machines that

as they're being built to already push

the envelope of what we can discover on

them.

>> Yeah. And how are you thinking about

community? And Kate, in particular, talk

about the

you know, the Chicago Quantum Exchange

has deep ties to this work. IBM's going

to have a quantum system installed in

Chicago this fall. Um

what does that start to look like and

how does it change the game around

workforce development and really

hands-on

work?

>> Yeah. So, that's definitely a few

different things. I think one of the

things is that So, the Chicago Quantum

Exchange, it's a consortium of many

different types of organizations. And

part of what we are doing is really

integrating them into a

discovery-to-deployment ecosystem, which

is incredibly needed for a technology

that's at this stage of development. So,

thinking of it integrating I folks from

universities, from government labs, from

teeny tiny startups, and industry

leaders like IBM

to particularly kind of advance those

research topics, but then really move

them to scale and do it quickly. And by

having kind of partners from the early

stages all the way to ones with

expertise in scaling, that's really kind

of the the secret sauce of being able to

move this forward. Um, and then with the

fact that the IBM is going to have a

computer in Chicago, will honestly have

a single place where you've got the

ideas, you've got the people, and now

the hardware that can really be working

together. Um, I think a perfect example

of this is that by bringing all these

things together, I think of it you can

have an example where uh and this this

happens actually semi-regularly where

you have a researcher within a

university who's doing some research

partnerships with IBM. And then that

faculty member or another one will,

let's say, start a startup company. That

startup company grows and becomes either

a strategic partner and or an investment

partner with IBM. And again, then they

can go and one, scale, but then also

push the limits of the hardware and

software that IBM has really to its

full, and then honestly helping them

helping IBM move it forward even

further.

>> Right. It's In some ways I feel like you

also just described the Stanford model.

Um,

just keep spinning out entrepreneurs

left and right. So,

we would be remiss if we did not note

that part of the reason that

the economy has been a little

underground lately is because

everybody's talking about AI. So, we're

also at a time when we're reinventing,

you know, what we think the workforce of

the future is going to look like.

People are wanting to maybe go into that

space. Money is going into that space.

Like, what does that mean? I'm going to

start with you, Kate, on this workforce

question.

What does that mean? You know, like, is

it cool to be in quantum anymore?

>> Yes.

Um,

>> [laughter]

>> I actually think the AI moment is a

perfect one on so many different levels.

Uh, first of all, as a human being, I

love a good template, right? I have an

older sister. I looked at her growing

up. Everything she did that turned out

well, I just kind of followed in her

footsteps. And if something didn't work

out well, I was like, I'm going to make

a different choice. Um, and and I think

AI is a little bit like the, you know,

older sister of quantum.

And and particularly when it comes to

the adoption piece, right? We were all

just, uh,

you know, surprised. Many of us were

surprised, uh, in in, uh, 2023. And this

is an opportunity where we can say,

"Look, quantum technologies are

developing right now. There are

opportunities for people and

organizations to, honestly, start on

that adoption curve earlier, so you're

not surprised." And I think on the

workforce side of things, that's a a

perfect example. There's actually a, you

know, we've come up with a clear

strategy for kind of building a

workforce. And really in, uh, a way that

kind of brings together whole

ecosystems. One is awareness, really

even as early as K-12, I want to say.

I have a 13-year-old son. I started this

job 8 years ago. Day one, I was reading

these quantum board books, and he got

it, right? If

>> Wait, wait. There are quantum board

books?

>> these quantum, uh, you know, quantum

materials

>> for babies. Cuz I've always heard that

that's what we're going to need because

this is slightly

>> Yeah, quantum mechanics for baby for

babies. I'm now doing an advertisement

on the road to there. There's a whole

series of them. Um but yeah, awareness

and then preparation. Hey, you've got

someone who's really interested giving

them opportunity for hands-on

experiences, mobility, helping them move

up the educational and career ladder.

And then this is where you know, IBM

really needs a piece is employer

leadership, right? Like IBM is going to

know what jobs they need today, what

skills they're not seeing, and they're

going to be the early indicators knowing

what jobs they're going to need in 6

months, 6 years, and the like. And then

also coordination. So, really working

together across regions, but also really

across the nation and the globe to make

sure we are building a workforce that

really is meeting the needs of the

technology and of the growth. Um and

particularly doing it with the knowledge

that even today less than half of

quantum jobs require a PhD. So, they're

open to people with 2-year and 4-year

degrees, often time also with 5 years of

experience, right? So, but but as

>> Which you get through board books.

>> Yeah. Well, well so this is this is we

we did this kind of big analysis uh of 3

years of job posting data and found that

uh

less than half of quantum jobs in

industry require PhD, but they often

require these 5 years of experience. And

where is a perfect place to get 5 years

of experience in quantum? It's while

getting a PhD. But

that was super useful because now we're

really leaning forward and building up a

lot of experiential programs, right?

Summer research experiences, hands-on

programs, so people can get that

experience without getting the PhD.

>> Dario, what can AI do that quantum can't

do? And what can quantum do that AI

can't do?

And how do they, you know, how do they

diverge and how do they come together?

>> think you know, fundamentally, right,

quantum is an underlying different kind

of math, and that's really what

differentiates it in from from any kind

of traditional computers, even AI

included. Uh in some ways, the way to

think about it is less on AI versus

quantum and more about the the the the

hardware itself, right? You have CPUs,

which are good at doing basic math, like

adding. You have GPUs, which are good at

doing tensor math, right? Which is

responsible for everything with LLMs.

And then QPUs, the quantum processing

units, are good at these quantum

circuits, and it's a different language

that it's good at. And so really it's

about how you bring them together and to

solve problems that break down into

these different parts into these

different pieces of math to best

leverage it. Um and really the whole

concept that I see with, you know, AI

and quantum is a lot of convergence,

right? There's a lot of people now

starting to use AI to help discover

algorithms and find the right kinds of

problems to actually run on the quantum

computers. Uh and there's similarly

there's there are workflows that are

using the infrastructure together. We

recently

uh worked with partners from the

Cleveland Clinic and uh the RIKEN

Institute in Japan to run a 12,000 atom

molecular simulation of a protein that

actually used two quantum computers, one

GPU-based supercomputer, and one

CPU-based supercomputer.

>> Mhm.

>> Right? So all these pieces coming

together to to to to really solve this

type of problem that pushes the limits

of what you can actually do with

generally general computing. And so

really that's the that's really the

excitement that I see that, you know, in

many ways the infrastructure is coming

together, and then the tools are going

to come together so that we can really

push the boundaries of both AI for

quantum and also quantum for AI.

>> Can you break it down into an example?

Like what can the class In that

scenario, all these computers walk into

a bar.

What can the classical computer do?

What do the the LLMs do and what is the

quantum computer

>> Yeah, so like you know, in this example

of the

this large 12,000

atom protein that we actually simulated,

we had to use the GPU supercomputer to

actually break it down and we used

techniques there to break it down into

smaller parts which we were able to

actually go and simulate on the quantum

computer.

Then we took the results from the

quantum computer and a lot of the post

processing there happens actually on the

regular CPU base supercomputer. So, the

different pieces of math are coming

together in the end what we're trying to

get was an energy configuration of this

complex molecule. Um, and what we're

starting to see now, you know, if so

talking about AI and data centers and

pieces like that is how can we compare

the usage of these different

infrastructures to solve a problem? And

this is really where things are getting

exciting right now where we're starting

to see this plane of comparison. Be it

cheaper, be it faster, be it or be it

more accurate to use quantum as part of

this computational workflow and

otherwise would have been just

supercomputing workflows in the past.

>> Mhm.

>> Where does quantum sit in the

in the infrastructure conversation

comparatively?

>> It's surprisingly a lot

>> like super mad at quantum computing

right now.

>> Surprisingly a lot more energy

efficient, right? So, we're not talking

anywhere near gigawatts of power, but

systems like where we're putting in

Chicago with with with Kate there

around you know, tens of kilowatts today

and even as we scale towards the latter

part of our road map with the end of

this decade, they'll be in the megawatts

or so. And so you know, a far cry from

what's needed for some of the GPU types

of of data centers today, but you know,

also in the near term I think they're

going to be very additive.

>> Yeah.

Kate, what do you what do we need to see

real quickly as we're sitting here now?

What does a really meaningful quantum

milestone look like for you. Like

genuine proof of commercial like true

commercial viability.

>> Well, I'm a scientist. So, for me a real

milestone doesn't have to be commercial.

So,

I do I do think

>> such a capitalist. How embarrassing.

>> I do think you're in San Francisco. It's

perfect. Um

I do think that the first milestone is

going to be that scientific partner that

is able to leverage a quantum computer

to do something that just cannot be done

with HPC and classical computing today,

right? Um

but I do agree like, you know, soon

after and I'm I'm not going to say an

actual date, I think that enterprise

partner example is exactly what is

needed for kind of widespread adoption.

>> Yeah.

Okay, do you have a milestone in 18

seconds that you're

going to say?

>> to those, right? In terms of actually

getting those demonstrations of science

as well as an enterprise use case. Yeah.

But then also from our perspective, we

keep executing on our road map and we're

excited about bringing a a real error

corrected fault tolerant quantum

computer by the end of this decade.

>> Amazing. Were you in radio, Jerry and

Kate? Thanks so much. What a great

conversation. Appreciate it.

>> [applause]