Steve McLaughlin: 
According to IBM,
quantum computing
could offer ways
to create medications
that save lives
and machine learning methods
to diagnose illnesses sooner.
It could even create
financial strategies
to live well in retirement.
But what exactly
is quantum computing,
and what does it take to achieve
these quantum breakthroughs?
[steam whistle]
[applause]
[marching band music]
>> I'm Steve McLaughlin,
dean of the Georgia Tech
College of Engineering,
and this is
The Uncommon Engineer.
[marching band music]
Man: [archival recording]
We’re just absolutely pleased
as punch to have you with us.
Please say a few words.
[applause]
Steve McLaughlin: Welcome to
another episode
of The Uncommon
Engineer podcast.
I'm Steve McLaughlin,
dean of the Georgia Tech
College of Engineering.
The Uncommon Engineer discusses
how Georgia Tech engineers
make a difference in our world,
in our daily lives,
and in ways
you might not expect.
Our guest today is
Professor Moinuddin Qureshi
from the Georgia Tech School
of Electrical
and Computer Engineering
and the School
of Computer Science.
His research has focused
around quantum computing,
and he's here today to explain
to us just what that is.
Welcome to the program,
Moinuddin.
Moinuddin Qureshi: Glad
to be here, Steve.
Steve: So anyone who's kind of
watching anything
in the science landscape—
new technologies, new
and crazy ideas out there—
have probably heard
about quantum science,
quantum computing, some of
the things that IBM is doing.
And before we start
getting into some details,
try to explain a little bit
about what quantum computing is.
And I think, more importantly,
you know, how it maybe changes
how it is people
think about science
and how really different and
unusual quantum computing is.
Moinuddin: So quantum computing
is a really exciting field.
I got into this field only about
three or four years ago,
so I'm relatively new as well.
What got me excited
about this field
is that it's a new paradigm
that allows you to solve
fundamentally different types
of applications.
So some applications
like factoring or understanding
how materials behave,
how cells and organisms behave,
these are extremely hard
for conventional computers.
And some of these problems
become solvable
if you have a quantum
computing paradigm.
Let me give you an example.
If you want to factor out
a 2,000-bit number,
even if you take the world's
fastest supercomputer,
make it the size
of the United States,
give it 100 years,
all of the energy of the earth,
you'll still not be able
to factor it.
Whereas with quantum computers,
you might be able to solve it
within a few hours.
So problems that are just
beyond the capability
of conventional computers
become solvable.
So it's not necessarily
for taking applications
that we are able to do right now
and make it a little bit faster,
but rather let us
explore something
that is just beyond
what we were able to do.
Steve: I see.
I think that's a really
interesting place to start
because I think people are
familiar with their computers
and programing and apps.
And, you know,
the world does think about
just how did it faster,
how do you do it cheaper,
how to do it smarter.
But you're saying that
there's just problems out there
that even those machines
can't tackle.
Those aren't necessarily
the first problems
that people are working on,
but that's kind of
where it's headed—
is being able to explore
solutions to problems
that folks had
never even conceived of.
Moinuddin: That's correct.
That's the real promise,
real potential
of quantum computers.
To put it in layman's terms,
maybe we are trying to increase
the speed of our cars, right,
maybe go from 50 miles per hour
to 100 someday, 200.
Even if you have
the fastest car,
you'll not be able to drive
from Atlanta to Sydney.
Fundamentally it will require
you to either have a ship
or an airplane.
You need a completely
different mindset
to be able to go
to a different set of problems,
not just do what we are doing
somewhat faster
or somewhat better.
Steve: And so, you know, I tend
to think of computers today
as, you know, operating on bits,
you know, 1's and 0's,
the language of computers,
1's and 0's—
the way that we program,
the way we do arithmetic,
the way that we store
information
kind of in 1's and 0's—
and I know that quantum is a
little bit different from that.
And so maybe you could
kind of share,
first of all,
maybe correct anything
that I've said so far,
[chuckles]
and then head towards
what quantum does differently.
Moinuddin: You're
absolutely right.
The conventional paradigm
of traditional computers
is 1 and 0's.
It's a switch;
it's either on or off.
In quantum computers,
you have qubits that are not
just in this on/off state.
They are in a superposition
of 1 and 0.
So there are basically
two important properties
in quantum computers
that you might keep hearing
if you read the literature—
superposition and entanglement.
Superposition is this ability
to have a collective state,
and entanglement
is the ability
to have correlated state
across multiple qubits.
And this is a very
powerful paradigm. Why?
Because if you have n qubits,
you potentially have access to
2 to the power of n state space
that you can manipulate
within one cycle, right?
But 2 to the power of n
is large, very large,
and we don't have anything that
can have that much of access.
For example, the number
of atoms in this universe
is less than
2 to the power of 200.
Even if we take all of the atoms
in the universe
and operate it from
the start of the universe,
so now we will not
be able to do
2 to the power of 400
calculations, right.
Whereas in this paradigm,
you can manipulate that space
within one cycle.
So it's a completely
different paradigm
that gives you access
to a gigantic state space.
Steve: So you talked about—
you talked about superposition.
The way I think
of superposition is,
you know, normally we think
of a bit as 1 or 0.
Now our qubit can be anything
between 1 and 0.
Oh, OK.
That's cool.
I'm with you.
So now can you say a little bit
more about entanglement?
Moinuddin: So let me start
with superposition
and then go to entanglement.
A good visualization
of superposition
is thinking of a sphere.
A normal bit can either be
north pole or south pole,
whereas a qubit can be
any point on the sphere.
And the operations
on a quantum computer take you
from one point of the sphere
to another point on the sphere.
So basically the operations
are rotation operations.
What entanglement allows you
to do is to create a state
where if you measure one qubit
or you affect one qubit,
you essentially know the state
of the other qubit, right,
so you don't have
independence.
The aspect of entanglement
is that you can create a state
where the qubits behave
in a correlated fashion.
You do something to one
or you measure one,
you automatically affect
the state of the other.
You're able to manipulate
the other
or you know something else
about the other.
Steve: My own experience
with the kinds of things
we've been talking about
is at a certain point,
you just kind of
have to accept them
even if you don't understand
them completely
because once you—
whether you understand
them intensely or not—
it's good to move on
and then to figure out
how you would use them to build
systems or build computers.
Moinuddin: There are lots of
counterintuitive
or sometimes hard-to-grasp
things that can happen,
and even people like Einstein
were not necessarily comfortable
with some of these ideas.
Let's just say these ideas
have taken several decades
to experimentally validate,
understand.
And even people who work
in this field for decades,
they sometimes very clearly say
that,
you know,
there is a little bit of fear
that some point that goes in
in these things.
And so that's true, right.
So as a computer scientist
or a computer system designer,
my approach is similar
to what we do
with traditional computing—
At some point you do learn
about transistors
and how p-n junctions are made,
but if you want
to build a system,
you want to solve a problem,
at some point
to abstract out, right.
So a similar thing with quantum—
At some point,
if you want to make progress,
it's good to know the theory,
but as you go higher and higher,
it's important to build
the right abstraction
so that you're not bogged down
by the details;
you're able to be productive,
and you can contribute
at the right level.
So that is the path forward;
that's about to make progress.
Steve: You're making me feel
pretty good
that my surface-level
understanding,
is good enough
to kind of move on.
And so maybe that's what
we’ll do is we’ll move on.
And I'm really interested
both in the history,
but then also I know there's
a lot happening right now.
It's extremely exciting time
with companies—
Google, Microsoft,
Amazon and IBM
are all very,
very heavily invested.
So can you spend a little time
talking about 20 or 30 years ago
the kinds of things
that people were saying
that thought you could do,
and then now why?
Why today?
Why is all this excitement?
Moinuddin: I would say that one
of the big step function
that happened
in quantum computing
that
made it accessible
to the wider population
happened in 2016
when IBM made
a five-qubit machine
and put it on the cloud.
It gave cloud access
to the general public,
so that people can log in
and run a real program,
a quantum program,
on a real machine.
IBM has made a lot of
contribution in this area.
They've made 16-qubit,
20-qubit machines.
Some of these machines
are publicly accessible.
There are billions of quantum
experiments that have been run.
And that's one of the reasons
I see
why there is so much excitement
in the last five years
because till 2015 or so,
most people would think about
this
as experimental scale machines
that general public
may not have access to, right.
Now we have access.
We can run programs,
and that's why there is
there's so much excitement
because this is real.
We're in a unique time
in history
where quantum computers
are available.
They're very small-scale.
I'm talking about 20-qubit
or 50-qubit—
these are tiny,
but that's great—
we at least have real machines,
and we can run programs,
see what happens,
so that's why I think this field
has become a lot more exciting
to at least
the computer systems,
computer application community.
Till now, most of the work
was either from the physics side
or the theoretical
computer science side.
Steve: So one of the things
you mentioned was applications
that folks can't really think
about solving now
using classical computers
that quantum computers
might be better for.
So, you know,
as we're recording this,
the coronavirus is really
just starting to massively scale
in the U.S.
I heard kind of in the press
they were very quickly
able to sequence
the DNA of the of the virus;
that, then, leads
to potential therapies.
And so what's
your take on all that
given if we had
a quantum computer today,
how could it help?
Moinuddin: So all of these—
so first of all,
this is a really
serious time, right.
And we are looking
for drugs for COVID-19.
Any drug discovery is very
computationally-intensive.
It's a search process which
takes a lot of time, right.
And then the potential—
so there's potential
that if we can understand
the material behavior
and the cell behavior,
then we might be able
to accelerate
the production of drugs.
And that is considered
one of the promises
of potential
of quantum computers
that fundamentally these are
sort of multibody interactions
which are very hard
to assimilate
with conventional computers
which we might be able to do
with quantum computers.
But these are good
promise problems.
The scale of a quantum computer
that is required to do
something like that would again
be in the order
of potentially millions, right.
So it's not something
that we might be able to do
in the next few years,
but the hope is that if we have
sufficient level
of understanding
and build the right size
of machines,
we might be able to solve
some of these hard problems
and make a meaningful impact
on the way we design drugs,
understand materials,
understand photosynthesis,
understand even some of
the phenomena of nature, right.
So those are considered
potential applications
of quantum.
But again,
these are not something
that we'll be able to do
in the next couple of years;
these are promise problems
that are quite far out.
Steve: So you've talked about
how they're still very much
in the infancy.
When you think that the real
impact is going to be felt
by a large number of people
that, you know, people day in,
day out will feel the impact
of a quantum computer?
Moinuddin: So I would say we are
a little bit far out from there
because we're just reaching
a point
where we might not be able
to simulate
these quantum machines
on a classical machine.
But until that point, whatever
the quantum computers can do,
your classical machines
can do, right.
So we're just at the cusp
of breaking that barrier.
The hope is that
within the next decade or so,
we'll have few hundred-qubit
or a thousand-qubit machine,
and that might be sufficient
for us
to solve some of the problems
that does not require
very high-quality qubits
or fault-tolerance.
The larger-scale applications,
such as factoring or understand
larger molecules,
might require a machine
that is in millions of qubits
and also does something called
as fault-tolerance—
ability to do error correction—
because these are really
long-running programs
with billions of instructions.
Even if one of
the instruction fails,
you might get a wrong answer,
so there's a continuum.
For some applications, we might
be able to see the impact
within the next decade.
The larger-scale applications
might be a couple
of decades, right.
But again, this paradigm,
it's not that we are competing
against classical machines,
right.
If we don't have
a quantum computer,
we might just not be able
to solve these problems.
So it's not that—
well it might take 20 years.
There is no alternative.
This is practically
the only known way
we know how to solve
some of these problems.
So it might take some time,
but this might give us
capability to do computation
to solve problems that are
just beyond our reach,
and it might take a decade
or two decades.
But in the grand
scale of things,
it's not that much time.
Steve: So one of the things
we always like to do
is talk about
your own research.
I know we talked
a little bit ahead of time
about the area
you're working in
is in this new paradigm
of a computer,
how do you think
about software?
How do you think
about getting the data
to the quantum computer?
I think that's your area,
and I'd love to hear
more about it.
Moinuddin: Yep. So basically,
there are two set of problems
that my group
has been looking at.
First is how do you have
intelligent software
that improves of the reliability
of quantum computers.
The second is, a quantum
computer still requires
a lot of peripheral circuits,
conventional control processor,
some sort of memory
that needs to be designed.
And my group is looking at
the cryogenic control circuit
and memory that's required
to operate a quantum computer.
So speaking of the first part,
the software part,
the quantum computers
that we have nowadays
they have qubits
that are not perfect.
So these qubits are actually
quite error-prone.
For example,
they retain their state
for about 50
to 100 microseconds,
after which they lose
their state.
Good luck getting your compute
done within 100 microseconds.
If you do an operation
on the qubit,
there's a non-negligible
probability like 1 percent
or 2 percent that operation
did not get
completed successfully;
you've got an operational error.
And this is actually quite bad
because if you have
1 percent error rate,
if your program
has 100 instructions,
chances are very high that,
well, you encountered
an error, right?
So reliability
is one of the biggest
challenge with computers.
Because we're dealing
quantum computers,
because we're dealing
with devices that are very—
that operate at a very small
energy level, they're fickle.
They tend to get disturbed
by little bit of energy.
They're very hard to control.
So what my group is doing
is that even
if you have
a quantum computer
that has qubits
that are error-prone,
it's not that the error profile
is constant;
some qubits are more prone
than others.
And so what my group is doing
is mapping algorithms
that takes the hardware
characteristics into account—
“That’s odd. This qubit is more
error-prone; let me avoid this.
That is a good qubit;
let me go there.”
So you can morph
the computation
according to the hardware
characteristics.
And that's an area of research
that we're pushing into.
As we learn more about
the hardware,
we can do better
in the software.
So making these qubits perfect
or less error-prone
is very hard.
It's something that people
are working on,
and we expect the error rate
to reduce a little bit.
But even if we have a machine
with a particular error rate,
the way we write
our computation
can significantly make it appear
to the program
that you're your quality
is quite good,
your qubit quality
is quite good, right,
so you mask out the impact
of hardware errors
with intelligent software.
And then that's
the main area of focus.
The second area of focus,
having hardware
for quantum computers
is some of the things
that we have looked at as well.
Your quantum computer of qubits
that does all of
the quantum work,
but they need to be told,
“Oh, you do this; you do that.
"You do this kind of rotation;
you do that kind of rotation.
OK, these two qubits,
now you entangle.”
Those instructions come from
a conventional computer.
Now qubits operate at ultralow
temperature— 20 milliKelvin.
We don't have a lot of power
at that level,
so we need technology that can
work at that temperature regime.
Typically, there is
a control processor
that's kept at about 4 Kelvin
that can interface
with these qubits.
Again, 4 Kelvin
is very low temperature,
and the technology that works
at that temperature
[indistinct]
essentially
we have dense memory.
So it becomes a very
computer system problem.
OK, you have a processor.
That processor needs memory.
But you can't have
that at 4 K.
You don't want to
have it a 300 K
because the wires that will
come in, that will leak heat.
So what we looked at is
how do we have cryogenic datum
to combine with
the cryogenic control processor.
So that is the second area
of research that we are doing.
Steve: I see,
and it's actually something
that we didn't even talk about
from the very beginning.
You know,
that the quantum computers
that we're all talk about
run at extraordinarily,
like you said,
extraordinarily low temperature
in order for them
to interface to the real world.
The real world is, you know,
65 degrees Fahrenheit.
And so those
practical realities,
which engineers tend
to be really good at,
those practical realities
let the physicists
do their thing,
like you said,
some of which we understand,
some of which
you take on faith,
but as you begin
to build a system
that require software,
all the hardware,
those are the things
that you're working on, I think.
Do I have that right?
Moinuddin: You have the right.
Steve: That’s cool.
You know, some of our listeners
are high school students
thinking about whether they want
to study engineering,
kind of explore
what an engineer does.
I'm really curious
about how you—
what path led you
to becoming an engineer?
Moinuddin: I enjoy engineering,
and I did not know
that I enjoyed engineering
until very late in my career.
The thing that I enjoy
the most is solving problems,
and engineering is all
about solving problems.
I like to solve puzzles.
Practically almost
all of my papers,
you could cast it as a puzzle
and you solve the puzzle.
So it has that solving a murder
mystery kind of a feeling.
You're trying to solve
something you don't know.
You don't know
how to solve it.
You try something;
it doesn't work.
You try something else,
and then suddenly one day
you have this brilliant insight
and you come up with a solution,
and then you sort of tie back.
A paper is essentially
a story that tells you,
“OK. This was a problem.
This is how we solved,” right.
So I'm very intrigued
about this ability of engineers
to solve problems
and, more importantly,
solve problems at a low cost.
So if you solve a problem,
but the cost is very high,
in our field,
practically the solution
is as good as a non-solution.
People don't adopt it.
So one of the challenges
in engineering
that I really enjoy is,
well, you have
to solve the problem,
but the cost
has to be very low.
Otherwise, it's as good as
you're not solving the problem.
So this interplay of there
is a problem
and you want to solve it
with minimal cost,
because if you can do,
then there's almost
a direct impact.
People are very interested
in the solution
if the costs are low, right?
So I enjoy engineering.
And even in high school,
I did not know
that I wanted to be an engineer.
But all the characteristics that
make somebody a good engineer—
enjoying solving puzzles,
thinking about cost,
trying to sort of develop a
solution with very little cost—
was already there.
And the fun part
about engineering
is you get to do this.
You get to impact the world.
And this can be a career, right.
So somebody pays you
for solving puzzles.
Steve: Count me in.
I think that's why we have
the greatest job in the world
because you're right—
We get paid to a problem
find and problem solve,
and it's really, really fun.
One of the other things
that we always ask on
The Uncommon Engineer is,
Moinuddin,
what makes you
an uncommon engineer?
Moinuddin: Again, I don't know
if it's common or uncommon,
but again, I don't really care
that much about the exact
problem domain as long as
I can find good,
impactful problems
that are easy to understand
and easy to explain.
So I did not used
to work in quantum
computing until
about four years ago.
This is an area
that has lots of good problem,
ability to impact,
and it can cast the problem
so that you can explain it
to somebody
what is the problem;
what's your insight;
what's the solution.
Quantum computing is one of the
things that we do in my group.
We work on hardware security.
Again, that's a problem
where you can define
it like a puzzle, right—
Somebody's trying to break in;
you're trying to defend.
Another area of research
that we are doing is robust AI—
How do you defend a machine
learning model
against malicious inputs,
against model-stealing attacks.
You'll notice
that there's almost nothing
in common in all of this.
What's common
is these are good problems
that can be cast as a puzzle.
If you solve it,
there's ability to make impact.
And I would say that that is
one of the things that I enjoy
and I let—
I communicate with my group
that we are not
necessarily people
who work on memory
or on computers.
We're engineers.
We're here to solve problems.
And if there are problems
that are outside
what we do conventionally,
it's fine as long as we have
a stepping stone,
we can understand the problem,
we can communicate the problem
and the insight
and the solution.
And that's something
that I genuinely enjoy
is the ability to cast something
as a puzzle, solve it,
and then the exact domain
doesn't really matter as much.
Steve: So the kind of your
uncommon superpower
is the ability to find a good—
spend a lot of time
finding a problem,
finding the right kind
of a problem
for which you're good at,
and then once you do that,
it works.
So we're really glad
that you came today.
And I hope our listeners
got a little
more insight on quantum.
Quantum is really tough,
really tricky.
And I really,
really appreciate the things
that you shared about quantum,
where it's headed.
And we're really fortunate
to have folks
like you here at Georgia Tech.
So thanks for coming in today.
Moinuddin: Thank you.
Glad to be here.
Thank you again for inviting me.
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