Hi, everyone.
Today we're going to talk about quantum computing
and try to demystify a topic that
might be intimidating to a lot of people.
But it's really exciting
and represents the future of what computing might be able to do.
Elena, talk to our listeners
a little about what quantum computing is
and why they should care about it.
So quantum's been around for decades.
But in the last few years, there's been significant advancements
to the hardware.
And it's becoming much more of a reality than a pipe dream.
According to Gartner, quantum will become more of a reality
by 2023, which is much sooner than the 20 years
from now, which is in most people's thinking.
What's also interesting from them
is a statistic that 20% of all companies
are going to have quantum computing related projects in 2023
versus 1% today.
And the possibilities that quantum computing opens in terms
of solving really complex problems that are impossible to solve
in today's traditional computing is what makes it so appealing.
And what are some of the advantages of quantum computing
over classical computing?
So the first primary one is speed.
So quantum computing is incredibly fast and faster
than traditional computing.
And the second is cost and efficiency.
So think about us being able to simulate
on a traditional computer what quantum is able to do.
But you need hundreds of thousands
of servers, millions of servers in order to do that.
Where you just need one quantum computer for the right use case.
OK, all right, great.
So how does it work?
If you think of traditional computing, as processing with digital
bits—ones and zeros—quantum computing leverages qubits.
And there's two very important properties that you can't
go to a quantum lecture and not talk about.
One is superposition.
And one is entanglement.
So I think the easiest way to explain it is through an example.
So if you picture a coin, and we're flipping a coin,
as we're flipping that coin—while it's in the air, it's a head
and a tail at the same time.
That concept is called superposition.
Now, if you introduce a second coin,
and we're now flipping two coins next to each other,
they're related to each other.
They have, let's say, wind exchange or whatever other
environmental forces that make…
Vibration or something like that.
Exactly—that make them not independent.
And the non-independents, it's called entanglement,
where their results are dependent on each other.
So quantum computing has superposition and entanglement
among a few other important theorems.
But those are the most important two that help make it
what it is.
So if I have one coin, I can have a one or a zero, a head
or a tail—like a visual thing.
If I have two coins instead of just a one and a zero, I have four
different possibilities, right—a head and a tail, a tail
and a head, two heads or two tails?
And if I have three,
I suppose I'd get even more, and four I'd get even more.
So it enables you to do things—that's how you get to do things
much faster.
Exactly right.
And not to get deep into the physics,
but I think it's important, if you have n qubits
with your quantum machine, you can simulate two to the n states.
So in your example, two to the two is four and two to the three
was eight.
In terms of power, it’s interesting to note that a 200 qubit
machine—in order to simulate in traditional computing—you need
more bits than you have atoms in the entire universe, not just
earth.
Yeah.
So we're not going to do that, right?
So let's talk a little bit about some of the use cases.
So as you said, this has been around for decades.
I first heard about quantum computing in the mid '90s.
It was this pipe dream that I'm like, OK.
One day I might have to worry about that.
What are some of the actual real world problems
that people are looking at quantum to resolve?
So with traditional computing
it's very good at sequential processing.
And quantum computing is very good at parallel processing.
So in terms of use cases,
they're really optimized for the parallel use cases.
So something that comes to mind is large-scale optimization.
So in financial services,
let's say you're trying to optimize a particular portfolio
and you have 12,000 stocks
and you need to maximize the returns based on different variables
and constraints within your portfolio,
quantum computing would be very helpful in that use case.
Machine learning is another one that comes to mind.
Sure.
So large amounts of data, trying to find trends
within that data, quantum computing
can help you leveraging superposition.
And entanglement can look at the data all at the same time.
And as long as you're applying the right algorithm to that data,
can help you glean insights that would take
many, many, many, many thousands of traditional computers today.
Specific examples that come to mind, let's say in the agriculture
space—so we need to plant crops.
And we need to maximize our revenue.
And there's different light factors and how much rainfall there
is and the particular makeup of the soil let's say.
So quantum can help compute all of those variables
to figure out what is the maximum plant that you can get
in a particular place subject to criteria.
So if you start to see agricultural companies all
of the sudden buying quantum computers, you'll know why.
Right, exactly.
So just carrying that example a little bit
further, because you could do a lot of those agricultural sort
of things with traditional models and all that.
But one of the challenges I think you have
is you're going to take the soil samples
or whatever, you can't measure everywhere.
So you're just going to look at one area over here, one over
here and guess that things in the middle act the same way.
Whereas with quantum, instead of doing 1,000 samples,
you might be able to do a million or a billion samples like that
and get much more particular about where you do things.
Is that fair?
Exactly.
Sampling is a great use case for quantum computing.
So why aren't we doing more of this?
Why don't we hear more about it?
Why is it still so many years away?
So it's a good question.
And I think it's really important to note that the end state here
is not to get to a point
where quantum computing is the end-all be-all in the universe.
A much more realistic state is that quantum computing
and traditional computing live side by side.
We have traditional computing that
is very good at sequential processing.
And we have quantum computing that's good at parallel processing.
In an end state, we leverage both.
So let's say a person in the enterprise
comes in, they're doing their traditional applications.
And then when they need to leverage a quantum computer
for a particular use case or a particular example,
they can rent that quantum computer in a mainframe environment
or a cloud environment for their particular application needs.
Kind of like how supercomputers were.
Used to be only by a handful of companies had them.
And you rent out time.
And now they're much more prevalent, right?
Exactly.
OK.
Finally, this topic is probably a little bit beyond what
a lot of our listeners
would have thought about as they think about their environments
today.
What are they really need to know?
If you're a business leader
and you want to learn more about how this applies your business,
what should they be focused on?
So the first thing is to educate yourself and not
get overwhelmed by the word quantum computing
and potentially not having a physics background.
The goal of introducing quantum computing in the enterprise
is not about having everyone have a PhD
and become a quantum physicist and understand the nuts and bolts
and build the hardware.
If you're good at that, there's definitely places to go.
But from an enterprise perspective,
the goal is leveraging this fantastic hardware and capabilities
to the benefit of the business and solving real business use
cases.
So I would urge business leaders to educate themselves, find
use cases that are relevant for the business,
and explore how they can improve their business through quantum
computing and speed up, and efficiency and cost.
Great.
Elena, thanks for joining us today.
Thanks.
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