[MUSIC PLAYING]
 Hey, I'm Diana, and you're
watching "Physics Girl."
There's this thing
that you see happen.
You see it happen
every single day.
Phase transitions.
Liquid to solid.
Solid to liquid.
Liquid to gas.
And scientists understand
these phase changes.
We understand them.
But there are some phase
transitions in nature
that scientists don't fully
understand because they
involve quantum mechanics.
The most mysterious
phase changes
are called quantum
phase transitions,
and they happen at the extremes.
For example, if you get
a magnet hot enough,
you can get it to suddenly
lose its magnetism.
Or in the coldest temperatures
of, say, outer space,
you could get insulating
materials to suddenly turn
into superconductors.
And so, yeah, we
understand what's
going on with the
individual particles
inside of, for example,
water when water boils.
But we don't understand what
the individual particles are
doing inside a superconductor.
It's an exciting
frontier of physics
because it's a problem
not yet solved by humans.
But what if something else
could solve this problem?
There is something that's better
at solving certain problems
than humans are.
Something that could give us
better product recommendations
than we could give each other.
Something that could diagnose
medical conditions better
than doctors could.
That thing is a machine
Yeah, it's a weird thought.
But what if I told you there's
a technique that scientists
and engineers are
researching that can
help us solve this quantum
phase transition problem?
That technique is
quantum machine learning.
This is my editor Jabril.
 Hi.
 But he also has an
awesome YouTube channel
about computer science.
And since I know nothing
about machine learning,
he's gonna help us--
me-- figure out what
machine learning is.
Yeah?
 Oh yeah.
 Yeah.
Oh yeah.
 So you want to know what
machine learning is, right?
 Yes.
 So, simply put, it's
teaching an algorithm
to learn almost anything
that you want it to learn.
 An algorithm.
 An algorithm.
So, for example--
OK, for example, let's create
this fantasy scenario in which,
like, you're a gardener
who loves their plants, OK?
 I have a garden!
 Yes, that's right.
All those garden
photos that you post.
OK, so let's use
you as an example.
 OK.
 So you're a gardener.
You love your plants.
And let's say that you
run into this problem
where you notice every once in
a while your plants die, OK?
 It happens.
 After a while, you deduce that
the problem is either from one
of two things, all right?
A, it's either from insects.
You know, ruining your plants.
Or B, dehydration.
Maybe you're not watering
them good enough or something
like that, right?
You love your garden, and your
plants are mysteriously dying.
OK.
If you could find out
their cause of death
while they're dying,
then you can treat them.
However, there's a problem.
The only way that you can
find out the cause of death
is if you pull them from the
roots and snap them in half.
 You're not a very
good gardener, but--
 I'm sorry, I'm sorry.
 We'll roll with this scenario.
JABRIL: And so what you do then
is you start taking thousands
of photos of your dead plants.
So now you have this data
set of a bunch of images
and their cause of death, OK?
So you will then
train an algorithm
by inputting all
of the photos you
took matched with their labels.
You know, the insects.
Insects.
Dehydration.
So on and so forth.
And the algorithm
learns the differences
in features of images labeled
as dehydration and the images
label as insects.
Yeah, so the process in which
the algorithm learns all this
is actually really fascinating.
And I actually made a
video over on my channel
if you want to come
and check it out.
 Jabril, focus!
 Sorry, I'm sorry.
 But actually it
is really good.
I'll put a link in
the description to it.
 Thank you.
You can now-- this
is the exciting part.
You can now take a new photo
that the algorithm's never
even seen before.
Feed it to the
algorithm, and it'll
give you a likely probability
on the cause of death.
 OK.
 So now you don't have to
kill your plants anymore.
Problem solved.
 Yes!
 Thanks to the
machine learning.
 So machine learning
is this process
of training an
algorithm on images
that you input with
labels to learn enough
about the features of the
images so that if you input
a new image, it can look
at that image's features
and then figure out which of
the categories it fits into.
Correct?
 Kind of.
That's only one type
of machine learning
that we went over today.
 There are more?
 There are lots more, yes.
 OK, cool.
That's awesome.
 But now to bring it back
full circle to quantum phase
transitions, was it?
 Yes!
 Right.
 We're gonna
unpack more of that.
Thank you for your help, Jabril!
 Yeah, any time.
 Get outta here.
 [LAUGHING]
 OK, so, how can we
apply machine learning
to quantum phase transitions?
I talked to a researcher at
San Jose State University who
works on this exact problem.
 Yeah, so I study
properties of quantum systems
of few particles by
doing simulations,
numerical simulations
of these systems.
When you mention quantum
phase transition,
I just want to make a
distinction that those are
strictly at zero temperature.
DIANA: So technically to be
quantum phase transitions,
they have to happen
at absolute zero,
the coldest theoretical
temperature in the universe.
But we can't actually
get anything down
to that temperature
besides in simulations.
And Ehsan's group does use
simulations in their work,
but even their simulations
incorporate some thermal
or heat energy.
 You generate a large number
of system configurations.
And by analyzing them, you can
infer where a phase transition
might be taking place.
DIANA: The phase transitions
that Ehsan's group studies
are magnetic phase transitions.
They simulate a
number of electrons
and then see what happens
with different concentrations
of electrons and what
the magnetic fields
of the electrons do.
These systems act
differently just
below a specific critical
temperature, the phase
transition temperature.
Above that temperature, they
see no order in the system.
And below it, they get kind of
a messy checkerboard pattern.
But it's hard to
see that transition.
 Something goes on in
these configurations
that are not possible
to be seen by naked eye.
But what we realized
was that we could
design an artificial
neural network that
can distinguish these
configurations at very
high temperatures.
There is no order in my system.
From of course the other
type of configurations
at temperatures below
the critical temperature
where I have an order.
So this artificial neural
network can look at these,
and you can train
it by showing it
lots and lots of these images.
 So, in conclusion,
if we can figure out
what's going on with
the individual particles
and transitions to
superconductors,
we could potentially make a
room temperature superconductor.
That's a huge field
of research right now.
Because if we made a room
temperature superconductor,
well, any electronic
components like your laptop use
or your phone, neither
of them would get hot
because there would
be no resistance.
It's a superconductor.
So as the electricity
goes through,
none of the electronic
components would heat up.
That would be amazing!
Probably far off in
the distant future.
But it is incredibly
exciting physics.
All right.
I hope you learned
something fun in this video.
I know that I did.
I have a few announcements.
Please stay for them.
We made another fun video
over on Jabril's channel
where I competed with
this AI that he wrote.
Go check that out.
I'll put the link
in the description.
And huge announcement!
After four years of
being on YouTube,
I finally graduated to
YouTuber with merch.
I have a t-shirt!
I designed it over many, many
months of procrastination
with my animator Kyle Norby.
And I love it so much.
But I don't own it yet because
it's still in preorders.
But I'm super excited for
it, so I'll put the link
to buy that in the description.
And keep an eye out for it
when I wear it in my videos.
Let me know if you like it.
But not if you don't like it.
And one last announcement.
As you know, my channel is
part of the PBS Digital Studios
network.
And they have a bunch of
new shows, one of which
is the "Origin Of Everything."
They've got videos on the
origin of blue for boys
and pink for girls and on, where
did grades in school come from?
They've got great videos.
Check them out.
I'll put a link to their
channel in the description.
Or Google them.
I don't know.
You guys are good at that.
All right, that's it.
Thank you so much for
watching, and happy physicsing.
[MUSIC PLAYING]
