We have a very exciting

last talk coming up. Dario Gil will take us

into a quantum world. Dario is the Vice President

of Science and Solutions at IBM research, where he

leads over 1,500 engineers that are researching in technologies

and physics, math, health care, life sciences and others. And while some of

you will think, a quantum world,

that’s too far out, I’m very sure Dario

will tell us otherwise. So come up here

on stage, please. Thank you. Thank you. I was joking with

Mark that we couldn’t pick an easier topic to end

the day, on quantum computing. But I’ll try to make it

entertaining, and hopefully easy to understand. I’m going to start

with a reference to this term of beautiful ideas. And it came from hosting

a filmmaker about a year and a half ago, in the

laboratory I just showed you. At the TGA Watson Research

Center in Yorktown Heights. And he was a filmmaker

that directed this documentary called

Particle Fever, that I don’t know if you’ve had

a chance to watch, but I highly recommend it. It’s about the team

that was pursuing the discovery of

the Higgs boson, in the largest physics

experiment ever conducted. And a major

character in the film is a professor from Stanford. And at the beginning

of the film, he said something that

really captivated me. He said, “The thing that

differentiates scientists is a purely artistic ability to

discern what is a good idea, what is a beautiful idea,

what is worth spending time on, and most importantly,

what is a problem that is sufficiently interesting,

yet sufficiently difficult, that it hasn’t yet been solved,

but the time for solving it has come now.” ” So I want to tell you about

this beautiful idea, whose time for solving it has come now. And that is the possibility

to create quantum computers. If you look at how

we have created the basis of the

information revolution, and you trace it back to

other beautiful ideas, like what Shannon

taught us, to think about the world of

information abstractly. If you look at an old

punch card and DNA, we’ve come to appreciate that

both carry something in common. They carry information. And Shannon told us

that this world of bits could be decoupled from its

physical implementation. That was really interesting. But in fundamental

ways, it went too far. Leaving too much physics out. So here is two scientists that

work at IBM Research, Charlie Bennett on the right, continues

to work in our laboratory, And is an IBM fellow. And they asked the

question, at the time, of is there a fundamental

limit to how efficient number crunching can

be, computing can be? And when they asked that

question as physicists, they ended up with a

very surprising answer. And they found the

answer to be no. It turns out, that

number crunching can be thermodynamically reversible. These led to an

exploration of, what is the relationship between

physics and information? And there was a

now-famous conference that was jointly organized

between IBM research and MIT at Endicott house,

where this topic was explored in more detail. And the plenary speaker was

none other than Richard Feynman. And Feynman proposed

in that conference, that if you wanted

to simulate nature, we should build a

quantum computer. And I’m gonna explain

you what that means, and how it’s created, and the

problems that it will solve. But first I’ve got to tell you,

what is a fundamental idea? The fundamental

idea, just like we have bits in the

classical world, that can be a zero or a one. In a quantum computer,

you have qubits, which stands for quantum bits. Now, the difference

is that there can be a zero, a one, or

both at the same time. That exploits a principle

of quantum physics called superposition. And it sounds weird and

crazy, but it’s true. Now to give you this unease that

you should feel when you talk about quantum information,

and quantum computing, I’m gonna give you a

very simple example. A thought experiment that

also happens to be true. So let’s imagine that we’re

going to solve this problem. The problem involves,

you have four cards, three are identical, one is

different, one is a queen. We shuffle the cards, and

we put them face down. And the problem we’re

going to solve together, is find the queen. We’re going to be

assisted by two computers. One is a classical computer,

one is a quantum computer. So what we do, is

we turn them down, and we load them into memory. So we use four memory slots. The cards are

identical, we put zeros. The one that has a

queen, we put a one. So in our four slots, we

will have three zeros, and one is a one. We load them on

the two computers. Now we has to write a program

to find the queen, find the one. How would it be

done classically? You would go and

pick a random number, you don’t know where it is. You go look under that memory

slot, see if it’s a one, if not, you go to the next

slot, and so on, and so on. On average, it would take you

the equivalent of 2 and 1/2 turns to find it. It turns out, that with

a two-qubit quantum computer for this

problem, you can always solve it in one shot. So that uneasy feeling

that you have now, should be an explanation that

quantum computer is not just about building a

faster computer. It is building something

that is fundamentally different than a

classical computer. Now, a way to think about

it, an abstraction of it, is that a quantum

computer is always going to have a classical

computer next to it. They have to go together. So you have a classical

set of bits, right? The problem that you’re

trying to explore. And what that quantum computer’s

gonna allow you to do, is to explore these

exponential number of states. These 2 to the n, where n is a

number of qubits that you have. So now, we have relatively

small quantum computers, with few qubits. But just think of the

number, that by the time you have 50 qubits, you

have 2 to the 50 states. That’s a phenomenally

large number. But in the end, after you

explore these number of states, you go back to a

classical output. A string of zeros and

ones, that you interpret with a normal computer. So why is this interesting? And I think in this

audience, I don’t need to explain in

great detail, you know, what exponentials mean,

and why 2 to the 50 is a very large number. But it’s still, I think

it’s an interesting way to communicate

the power of this, and I like to map

it to some problems. But I like to go after

this apocryphal story that actually, IBM

used in the 1960s to explain to people the

power of exponentials. And it had to do

with the person who invented chess, that goes

to the emperor, and says, well here’s his wonderful game. And asks, what do

you want in return? And the person who

invented it says, give me a grain of

rice on the first day, for the first square,

and the second day you give me twice as much. And on the third square, third

day, you give me twice as much as the day before. And the emperor agrees

promptly that that seems quite reasonable. And after a week you

only have 127 grains. After a month,

you have more rice then you’ll eat in your

lifetime, for sure. But just by the time you get

to the end of the chessboard, you have more rice

than Mount Everest. So there are a large

number of problems in the world that have this

characteristic, that they blow up exponentially. And a dirty secret in

the world of computing is that we obviously talk

a lot about all the things that computers can solve, and

can solve a lot of things. But then, there’s

a lot of things that computers can not solve. And very interestingly, they

cannot solve it now, nor ever. And the reason is because they

have this exponential built into them. So take as an example, this

fairly simple equation. Factoring. So if I have a number,

M, that is made out of the multiplication of

two large prime numbers. And I only give you M, and

I ask you find me p and q. It turns out, that that

is phenomenally difficult to solve. There’s no other way but to

divide it sort of sequentially, by prime numbers. So in fact, it’s

so difficult, we use it as the basis

of all encryption. But, if you had a very large

universal fault-tolerant quantum computer, which

is many, many years away, you could solve that

problem in seconds, what would take billions of

years in a classical computer. That tells you something

about the power of what is going to be possible. Take chemistry, as a problem. Because it also has

this characteristic, that it blows up exponentially,

if you try to calculate it. This equation that you see

here is very interesting, because it’s predicted

to occur at the ocean floor near volcanic

sites, and famously has been hypothesized to be the

basis of the formation of life on Earth. But if you take a molecule

like iron sulfide, and you try to do relatively

simple calculations with a normal

machine, it turns out, that we’re not very accurate. And the reason is

that molecules form when electron orbitals

overlap, and the calculation of each orbital requires a

quantum mechanical calculation. So for that simple

molecule, you have on the order of 76 orbitals,

and two to the power of 76, is intractable with a classical

computer, so we can not solve it. Again, on this theme of our

assumptions that computers solve everything,

but they don’t. If you look at calculating

for example, the bond length of a simple molecule

like calcium monoflouride, we still get it off

by a factor of two, even using the largest

supercomputers in the world. To me, this has been

very interesting, this recognition of all these

problems we cannot solve. It’s also true in

optimization problems, that are the basis of

logistics and routing, and you know,

portfolio optimisation. There’s tons and tons of

problems in which at best we do approximations, but

we’re far from optimal, because a number of

possibilities is enormous. So if there’s one message I

want to be able to come across, it’s that we have these

easy problems, which is the world where

classical computers fit, and the problem it’s solved. But then there these other

hard problems, that go outside. And if you don’t

believe that p equals np, which I would say the

majority of mathematicians don’t believe that that is the

case, that those problems are hard for a reason, the only

avenue to go and tackle that, aside from approximations,

will be to the creation of quantum computers. So where are we? We believe that small

practical quantum computers are going

to be possible, and we’re building them now. It requires reinventing

the whole stack. The device is different. It’s not the

traditional transistors. As an example,

this is the device we use for that

quantum computers that we create at IBM, based

on superconducting Josephson junctions. And you’re seeing an example

of one of these device, is superconducting device. And because it’s

superconducting, you have to cool it. So this is what a small

quantum computer looks like. What you’re seeing

here is something called a dilution refrigerator. And this quantum processor

sits at the bottom of this refrigerator,

at the nice temperature of 15 millikelvin. So that is colder

than outer space, where we have to put this

quantum processor in. This is what, for example,

a 16-qubit quantum processor looks like. And you know, inside,

you see the square where the qubits are, and you

see these squiggly lines, which is these coupling

resonators that allow you to send information

uncoupled to the qubits, To send the information. This is what the

wiring looks like, into the refrigerator going

into a quantum processor. There’s these coaxial

cables, because the way you send information

to a quantum processor, is through a series of

microwave pulses, that go in, and then you’re

able to take it out. Now, if you look at pictures

of what computers were like, right, in the ’40s

and the ’50s, it’s kind of like where

we are today, right? That’s what, you know,

quantum computer, that’s the signal processing

required to actually send all those signals down

the coaxial cables, it looks like that. But we’ve also seen

this movie before, in the sense that we know

how much progress we have made from those early system. And while we don’t anticipate

that quantum computers will be on your phone, because they

require cryogenic cooling, we definitely

believe that access to quantum computers

in the cloud will be something that people

will be able to leverage, behind the scenes,

even not knowing. Because we believe that,

we created a small quantum computer last year, and we

made it available to the world. In something called the

IBM Quantum Experience. And all of you can go and log

in and have access to this. It’s available for free. It’s a 5-qubit machine. And since we launched it,

we have over 36,000 users from over 100 countries

that have been doing it. And 15 scientific

publications have gone on it, and people are learning how

to program, and to learn about this new world, and

what is being created. And you can actually

run things on this. So I was telling you about

these chemistry problems. So this is an example of

the expected theoretical calculation, and the actual

calculation, on a small quantum machine, of hydrogen.

So we’re starting to solve small problems. And what is coming in the years

ahead, in the next few years, will be machines that

no classical computer will be able to emulate. Because by the time you

have order of 50 qubits, think about that, that’s

2 to the 50 states. And no classical machine

will be able to emulate what that can do. And that is new territory. And that’s the territory

we’re all going to enter. And now is the most

interesting part, because it’ll be the path of

discovery of what we can do, and what value we can

create, on problems we couldn’t solve before. So I’ll close with

Feynman, who proposed this original idea of creating

these quantum machines. In his inimitable

style, he said, “Nature isn’t classical,

dammit, and if you want to make a

simulation of nature, you better make it

quantum mechanical, and by golly, it is a

wonderful problem, because it doesn’t look so easy.” Thank you.

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